ENCODING THE CONFIGURATION OF A CONSPECIFIC PHEROMONE IN THE ANTENNAL LOBE OF A MOTH, MANDUCA SEXTA by Joshua Pierce Martin _____________________ Copyright © Joshua P. Martin 2011 A Dissertation Submitted to the Faculty of the GRADUATE INTERDISCIPLINARY PROGRAM IN NEUROSCIENCE In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY In the Graduate College THE UNIVERSITY OF ARIZONA 2011 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Joshua Pierce Martin entitled: Encoding the configuration of a conspecific pheromone in the antennal lobe of a moth, Manduca sexta and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy ___________________________________________________Date: 14 December 2011 John Hildebrand ___________________________________________________Date: 14 December 2011 Wulfila Gronenberg ___________________________________________________Date: 14 December 2011 Thomas Christensen ___________________________________________________Date: 14 December 2011 Jean-Marc Fellous Final approval and acceptance of this dissertation is contingent upon the candidate's submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. ___________________________________________________Date: 14 December 2011 Dissertation Director: John Hildebrand 3 STATEMENT BY AUTHOR This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the copyright holder. SIGNED: Joshua P. Martin 4 Acknowledgements This work was made possible by the support and assistance of several exceptional people. First and foremost, none of these people would have been here if not for the work and vision of my thesis advisor, John Hildebrand. John created a unique institution in the Division of Neurobiology, and recruited talented scientists to make it the best possible place for me to learn my trade. His support and guidance made my work possible, and motivated me to live up to the reputation of this institution. My fellow students were the best friends and colleagues I could ask for. I especially thank Andrew Dacks and Aaron Beyerlein for being my friends, fellowtravelers, and office-mates these (many) years. The senior members of the lab, Hong Lei, Jeff Riffell, Carolina Reisenman, and Pablo Guerenstein, took me in and taught me what I needed to know, and they will become my models for the next step in my career. I thank my committee for shepherding me through this process and giving freely of their time. Tom Christensen built the mountain of work to which I add my few stones; Jean-Marc Fellous helped apply sophisticated analysis to my data; and Wulfila Gronenberg acted as counselor and advocate whenever I needed him. Finally, I thank my family for standing by me always. My parents buoyed our little family through growing pains and hectic times, and supported me in the odd little path I’d chosen. My wife April and I leaned on each other these years, and though she struggled with her own studies and career, she found deep wells of strength to help me when I needed her. We both find inspiration in raising Bea, our own little taxonomist (“Butterfly! Giraffe! Monkey!”) and animal communication enthusiast (“Moo! Rawr! Caw caw!”). 5 TABLE OF CONTENTS LIST OF TABLES …………………………………………………………………….. 7 LIST OF FIGURES …………………………………………………………………… 8 ABSTRACT ……………………………………………………………………………. 9 1. INTRODUCTION ………………………………………………………………… 10 2. PRESENT STUDY ………………………………………………………………... 14 2.1. The neurobiology of insect olfaction: Sensory processing in a comparative context. ………………………………………………………………………… 15 2.1.1 Introduction ….……………………………………………………………. 15 2.1.2 Summary ………..…………………………………………………………. 15 2.1.3 Specific contribution of the author ………………………………………. 17 2.2 Innate recognition of pheromone and food odors in moths: a common mechanism in the antennal lobe? …………………………………………….. 18 2.2.1 Introduction ……………………………………………………………….. 18 2.2.2 Summary …………………………………………………………………... 18 2.2.3 Specific contribution of the author ………………………………………. 20 2.3 Synchrony Between Glomeruli Encodes the Behaviorally Effective Ratio of Components in a Pheromone. ……………………………………………….. 21 2.3.1 Introduction ……………………………………………………………….. 21 2.3.2 Materials and Methods …………………………………………………… 27 2.3.3 Results ……………………………………………………………………... 34 2.3.4 Discussion …………………………………………………………………. 43 6 TABLE OF CONTENTS - Continued 2.3.5 Tables ……………………………………………………………………… 61 2.3.6 Figures ……………………………………………………………………... 63 3. CONCLUSIONS …………………………………………………………………... 77 3.1 Summary of results and future directions …………………………………… 77 3.1.1 Olfactory mechanisms of species-specific behavior …………………….. 77 3.1.2 Synchrony in olfactory coding …………………………………………… 79 3.1.3 Ratio coding in the main AL ……………………………………………... 81 3.1.4 Final thoughts ……………………………………………………………... 83 REFERENCES ……………………………………………………………………….. 85 APPENDIX A: THE NEUROBIOLOGY OF INSECT OLFACTION: SENSORY PROCESSING IN A COMPARATIVE CONTEXT. …………... 105 APPENDIX B: INNATE RECOGNITION OF PHEROMONE AND FOOD ODORS IN MOTHS: A COMMON MECHANISM IN THE ANTENNAL LOBE? ………………………………………………. 129 APPENDIX C: DEVELOPMENT AND APPLICATION OF A METHOD TO IDENTIFY PROJECTION NEURONS AND LOCAL INTERNEURONS FROM EXTRACELLULAR SPIKE TRAINS …………………………………………………………… 138 7 LIST OF TABLES Table 2.3.1 Stimuli used in behavioral experiments ………………………………….. 61 Table 2.3.2 Stimuli used in physiological experiments ………………………………. 62 8 LIST OF FIGURES Figure 2.3.1 Anatomy of the antennal lobe, and characterization of PN responses to stimuli. ………………………………………………………………………………… 63 Figure 2.3.2 Behavioral responses of male moths to mixtures with various ratios of pheromone components. ………………………………………………………………. 65 Figure 2.3.3 Firing rate responses of individual PNs form a poor representation of the ratio between components in a pheromone mixture. ………………………………….. 67 Figure 2.3.4. Synchrony between pairs of PNs is modulated by the ratio between pheromone components in a mixture. ………………………………………………… 69 Figure 2.3.5 Synchrony across the recorded population of PNs is maximal in the behaviorally effective range of pheromone component ratios. ………………………... 71 Figure 2.3.6 MGC PNs oscillate at a higher frequency than the LFP. ………………... 73 Figure 2.3.7 A possible network underlying ratio-selective synchrony in the MGC. ... 75 9 ABSTRACT Odors that are essential to the survival and reproduction of a species take the form of complex mixtures of volatiles. Often, an odor source such as food or a potential mate releases a mixture with characteristic ratios between the components. Here, the encoding of the characteristic ratio between components of the pheromone released by a female moth is investigated in the antennal lobe (AL) of a male moth (Manduca sexta). The mechanisms by which olfactory systems of diverse insect species process odors are adapted to the particular environment and olfactory behavior of the animal. In the moth, innately attractive odors produce patterns of synchrony in the output of the AL, the projection neurons (PNs). Male moths exhibited attraction to synthetic mixtures of pheromone components that was selective for ratios at or near the natural ratio released by females. Selectivity increased as the moth neared the odor source and initiated mating behaviors. PNs in the macroglomerular complex (MGC) did not exhibit an effect of component ratio on their firing rate responses. However, pairs of PNs exhibited increased synchrony in response to the behaviorally effective ratios of pheromone components. Individual pairs exhibited selectivity for ratios within 1 order of magnitude from the natural ratio. Synchrony in PN spiking was not phase-locked to the network oscillations in the AL. A model for ratio-selective enhancement of synchronous PN output in the AL is proposed. 10 1. INTRODUCTION Investigation of the staggering complexity and seemingly infinite adaptability of the brain takes place in the context of a basic fact: A brain, like any organ, evolved in ways that improved an animal’s chances of survival and reproduction. That the structure and function of animal brains, even human brains, has been shaped by their environment through the same forces of natural selection that shape wings, horns, or mandibles has been argued since very shortly after the origin of the theory of evolution (Huxley 1863). The natural world is the context in which brains have evolved, and it is the proper context in which explorations of the brain take place. In this thesis, I investigate how the olfactory system of an insect, the hawk moth Manduca sexta, processes information about a particular sensory stimulus essential to successful reproduction: the pheromone released by a conspecific mate. In doing so, I take advantage of the growing understanding that information processing in the brain is a product of a complex interplay between the general capacity of neural networks to extract and encode information, and the specific requirements survival and reproduction place on the form and function of an animal’s nervous system. I progress from a general discussion of olfactory neurobiology in diverse insect species to an argument that the synchronous activity of neurons can encode stimuli of innate significance in the moth’s AL, and perhaps in other systems as well. In behavioral and physiological studies in the moth, I investigate how the configuration of the components of an odor, specifically the ratio of one volatile compound to another, determines the attractiveness of the odor to the 11 animal and is encoded in the output of the AL as synchronous spikes between output neurons. From the general principles of olfactory neurobiology to the encoding of a species-specific olfactory signal, understanding is facilitated by the firm grounding of neurobiological research in evolution and natural behavior. The first chapter is a review of the literature of the neurobiology of insect olfaction (Martin et al. 2011). In this review, we argue that seemingly disparate results obtained from investigation of the olfactory system of different insects can be explained in the context of the environment, behavioral niche, and the nature of olfactory signals necessary for survival that have shaped the form and function of the insect’s olfactory system in the course of evolution. We propose that a comparative framework can best utilize the rich diversity of insect systems by considering that olfactory systems act as a matched filter for the physical, temporal, chemical, and configurational characteristics of olfactory stimuli important to that insect. Where these characteristics align, e.g. the temporal structure of encounters with an odor plume by flying insects, we expect to see common neural mechanisms. Where insects differ, e.g. specialists focusing on the odors of a small range of sources versus generalists capable of exploiting information about many more odors, the requirements placed on each species’ olfactory system will emphasize different mechanisms. In this way, the natural experiments that evolution has provided can test hypotheses about the role of shared and unique mechanisms in this sensory system. Next, I turn to the particular mechanisms by which moths recognize innately attractive odors, whether the source is food, facilitating survival, or conspecific mates, 12 facilitating reproduction. I review evidence from the chemical and configurational structure of innately attractive stimuli, and the behavioral effects of changes to the peripheral and central olfactory nervous system that suggest that the AL of the moth comprises a pattern-recognition network for odors with innate behavioral significance. Results from investigations in both the pheromone and floral odor-processing subsystems of the AL indicate that this network encodes the behavioral significance of an odor as synchrony between projection neurons (PNs) that terminate in higher-order processing centers. In behavioral and physiological experiments, I test the hypothesis that synchrony between PNs encodes the behaviorally effective configuration of the components of the female pheromone. Closely-related species of moth release pheromones composed of the same chemical components, differing only in the ratios of these volatiles in the blend (see Baker, 2008 for review). In this work, I build on previous work that demonstrated that the natural, 2:1 ratio between two key components in the pheromone blend produced by female Manduca sexta improves the ability of PNs to follow pulses of pheromone (Heinbockel et al. 2004) and that synchrony between PNs is enhanced in response to the blend over the individual components alone (Lei et al. 2002). I first demonstrate that these moths exhibit behavioral selectivity, preferentially initiating mating behavior in response to the natural ratio of components. Next, in paired recordings from pheromoneresponsive PNs in the AL, I demonstrate that while the firing-rate output of PNs is insensitive to the component ratio of the pheromone, pairs of PNs exhibit synchrony selectively for ratios at or near the natural, 2:1 ratio. Finally, I suggest how observations 13 in this work can clarify the role and mechanism of synchrony in the AL and how this work may be extended to the mechanism underlying this animal’s innate attraction to certain complex floral odors, and perhaps to changes associated with learning the salience of a previously unattractive odor. In this work, the goal of understanding how the configuration of components of a complex stimulus is encoded by a network is facilitated by investigating a natural behavior with clear ecological and evolutionary significance. 14 2. PRESENT STUDY The first portion of this thesis consists of two published reviews on the connection between an animal’s environment, olfaction-based behaviors, and the form and function of a species’ olfactory system. These works of scholarship set the stage for the exploration of an olfactory mechanism underlying a highly species-specific function: processing the configuration of components of a conspecific pheromone blend. The following is a summary of the main points of these reviews. 15 2.1 The neurobiology of insect olfaction: Sensory processing in a comparative context. 2.1.1 Introduction Insect olfactory systems have long been utilized in studies of olfactory neurobiology because of the striking commonalities between distant phyla (Hildebrand and Shepherd, 1997; Christensen and White, 2000; Ache and Young 2005; Kaupp 2010). The extraordinary radiation of insect species over 400 million years of evolutionary history (Grimaldi and Engel, 2005) has, however, necessarily lead to diverse adaptations of the olfactory systems of insect species occupying diverse environments, and utilizing olfactory information for diverse behaviors. This has lead to some disagreement in the literature over the description of olfactory neurobiology in “the insect.” In this review, we argue that the diversity of insect species studied in olfactory neurobiology presents an opportunity to test hypotheses using the “natural experiments” that adaptation has provided. We review the historical and recent research in insect olfactory neurobiology, highlight the differences between insect species in form and function of the olfactory system, and suggest, where possible, how the details of an animal’s environment and olfaction-based behaviors can inform investigations. 2.1.2 Summary We begin by establishing the chemical and physical nature of olfactory stimuli important for survival for various species of insects. Insects rely on the chemical structure 16 of volatiles released by different sources (e.g. host plants, prey, and mates) and the structure of the odor plume in time and space to identify and localize odor sources. We next review both innate and learned behaviors that rely on olfaction, before discussing how evolutionary changes in olfactory receptors and the central nervous system underlie speciation based on changes in these olfactory-based behaviors, e.g. exploiting a new host plant, or sexual isolation of closely-related species. We progress through the olfactory system, noting differences between species including (1) the spectrum of complexity in the organization in the first olfactory neuropil, the AL, (2) the variation on a basic Bauplan of tracts connecting the AL to higher-order centers, and (3) the degree of segregation or integration of AL output channels in their targets, the mushroom body (MB) or lateral horn (LH). Finally, we review the often conflicting findings in research on coding and processing of olfactory information in insect brains (e.g. gain control, sharpening and broadening of representations, and encoding of temporal information). Of particular relevance to Chapter 2.3 in this thesis, we discuss the role of synchrony in several insect species. We suggest that the strictly phase-locked oscillatory synchrony observed in the locust is a product of that species’ generalist feeding habits. Long exposure to odors while feeding, and the requirement of associating a large variety of available food sources with their nutritive and toxic characteristics, makes the large coding space afforded by the slowly changing patterns of AL output beneficial to the locust. Moths, however, must identify and locate odors based on brief, stochastic encounters with an odor plume, and therefore rely on information contained in brief neural responses that do not evolve into 17 complex temporal patterns. In Chapter 2.3.4 of this thesis, I further argue that a common oscillatory mechanism, altered to suit the requirements of coding in each species, underlies olfactory coding in both species. This review attempts to lay a foundation for future work in insect olfactory biology that takes advantages of a comparative approach. 2.1.3 Specific contribution of the author The specific contribution of the author to this work is provided here in accordance with the thesis guidelines of the Graduate College of the University of Arizona. The laboratory group was invited to submit a review of insect olfactory neurobiology by the editor of the journal. The author of this thesis participated in early discussions on the scope and major arguments of the review, and was selected as the first author at this point. The author solicited input as to the section topics, assigned one section to each collaborator, and wrote several sections. Collaborators submitted outlines and first drafts of each section, which the author reworked and rewrote to produce a consistent tone and continuity of themes throughout the manuscript. Approximately 50% of the manuscript was written with input from the collaborators in this way, and the other 50% was written solely by the author. The published manuscript is attached as Appendix A. 18 2.2 Innate recognition of pheromone and food odors in moths: a common mechanism in the antennal lobe? 2.2.1 Introduction In this review, we turn our attention from the neurobiology of innate and learned olfactory behaviors across many insect species, to compare the mechanisms underlying two innate olfactory behaviors in a moth: attraction to conspecific mates and the floral odor of a host plant. Although the plasticity of behavior and the neural mechanisms of plasticity are rightly a focus of neurobiological research, many behaviors are “hardwired” and rely on sensory stimuli with innate salience to an animal (Tinbergen, 1951). Study of these behaviors in neurobiology capitalizes on robust, observable behavior and the existence of a neural code for certain stimuli in naïve animals. The moth Manduca sexta exhibits upwind flight to two categories of odors: the pheromone released by conspecific females (Tumlinson et al. 1989) and the floral odor of certain night-flowering plants (Plepys et al., 2002; Raguso and Willis, 2002; Riffell et al., 2008). In this review, we consider the evidence that the AL of moths constitutes a pattern-recognition network for the configuration of these odors, and that the output of the AL that signifies an innately attractive odor is synchronous activity between projection neurons. 2.2.2 Summary We first note that the energetically costly flight and short life-span of a moth predisposes the animal to rely on innate attraction to stimuli that are most likely to be rewarding, rather than the flexibility of learning the reward associated with new stimuli. 19 We then examine olfactory behavior and neurobiology in the context of an odor for which moths are obligated to specialize: the pheromone of a conspecific mate. Moth species often have ranges that overlap with closely-related species. Although olfactory receptors in the antenna are extremely selective for particular pheromone components, closely related species may release the same components, so that the signal differs only in the ratio between the components. We review the evidence from natural and laboratoryderived populations in which changes in the expression, response, or AL target of olfactory receptor cells accompanies changes in the attractive ratio of components, and conclude that the AL network recognizes the pattern of activation between glomeruli that corresponds to the conspecific pheromone. The observation that synchronous activity between PNs is enhanced in response to the female pheromone blend in Manduca sexta suggests that this synchrony encodes the salience of the odor and motivates the investigation of the encoding of the behaviorally effective ratio in Chapter 2.3 of this thesis. Similarly, the innately attractive odor of a flower (Datura wrightii) commonly visited by hawk moths such as Manduca sexta elicits synchronous firing between PNs in the AL that respond to floral volatiles. Stimulation with other mixtures of floral volatiles that are not attractive to naïve moths elicits less synchronous activity. Finally, we review the evidence that synchrony in this system is dependent on the network of inhibitory local neurons in the AL, and propose a model for the encoding of innately attractive odors in the moth AL. This work sets the stage for the investigations in Chapter 2.3 of this thesis, which demonstrate that synchrony encodes the range of attractive ratios between 20 components and is correlated with the degree of attractiveness of an innately significant odor. 2.2.3 Specific contribution of the author The author of this thesis was contacted by the editor of the journal in which this work was published, and asked to write a review for a special issue released in conjunction with the annual meeting of the International Society for Neuroethology. The author conceived of the topic, researched the literature, and wrote the manuscript with the input of the co-author. The published manuscript is attached as Appendix B. 21 2.3 Synchrony Between Glomeruli Encodes the Behaviorally Effective Ratio of Components in a Pheromone. 2.3.1 Introduction Identifying the odors released by potential mates, sources of food, safe places to raise offspring, and other salient sources is central to the survival of most animals. These odors often take the form of complex blends of many volatiles, which have a behavioral significance to an animal that is more than the sum of their parts (c.f. Riffell et al. 2009a, 2009b, Jinks and Laing, 2001, Martin and Hildebrand 2010). Olfaction therefore relies on a form of object recognition, wherein the component parts of a whole are perceived as one unitary percept, distinct from other objects and apparent against a background of other olfactory stimuli (Stevenson and Wilson, 2007; Christensen and White, 2000). Perception of an object depends on information about its component parts and the relationship among them. Various chemical communication types rely on the production of odor blends with characteristic proportions of components by a sender and corresponding selectivity for the natural proportions by a receiver (e.g. Takken et al., 1997; Silva et al., 2005; Cardé and Minks, 1995; Cha et al. 2011; Bruce et al., 2005; Visser and Avè, 1978; Tasin et al., 2006; Najar-Rodriguez et al. 2010). The fixed proportions of components of the pheromones released by female moths have particular importance: fidelity between sender and receiver prevents interbreeding between closelyrelated species (Roelofs and Cardé1977; Anton et al., 1997; Vickers et al. 1991; Linn et al., 1988; Linn et al., 1991; Minks and Cardé, 1988). This system represents a form of 22 innate object recognition in olfaction (Martin and Hildebrand 2010) that constitutes an accessible system for the investigation of how complex odors are represented in the olfactory system. It shares features of other innate, olfactory-based behaviors that have been utilized in other studies (e.g. Semmelhack and Wang, 2009, Suh et al. 2004; Kurtovic et al. 2007; Fishilevich et al. 2005; Troemel et al. 1997; Kobayakawa et al. 2007; Riffell et al. 2008): robust, well-characterized behavior, known characteristics of the sender and receiver, and an olfactory stimulus with known ecological and evolutionary significance. In this study, we capitalize on the benefits of neurobiological investigation in the pheromone processing system of a moth (Manduca sexta) to explore how the ratio of components in an odor is encoded by the central nervous system. Blend processing in the olfactory system Much of the existing model of blend processing in olfaction is based on experiments using combinations of a few (typically two), often randomly-selected volatiles. At the periphery, olfactory receptor cells (ORCs) express olfactory receptor proteins with varying degrees of specificity, most often broadly responsive to a range of volatiles (Hallem and Carlson 2006; Malnic et al. 1999) (Figure 2.3.1A). Stimulation with mixtures of volatiles typically produces ORC responses that are lower or weaker than the responses to single volatiles (suppression, Moskowitz and Barbe, 1977) or increased less than would be expected from a linear summation of the individual responses (hypoadditivity), with rare examples of synergy or enhancement (Shields and Hildebrand 2001; Michel et al. 1991; Lucero et al. 1992; Ache 1994; Kurahashi et al. 1994; Olson and Derby 1995; Daniel et al. 1996; Sanhueza et al. 2000; Carlsson and 23 Hansson 2002; O'Connell et al. 1986; Akers and O'Connell 1988; Tabor et al. 2004; Deisig et al. 2006; Carlsson et al. 2007; Silbering et al. 2007; Akers and Getz 1993; Cromarty and Derby 1998; Duchamp-Viret et al. 2003). The pheromone-receptive ORCs in the antennae of moths are typically “specialists” (Kaissling 1974) that respond selectively to one component of the pheromone. However, stimulation with the full pheromone blend can either enhance (Ochieng’ et al. 2002; O'Connell et al. 1986) or suppress a response to the preferred component (Hillier and Vickers 2011), or have no effect (Akers and O'Connell 1988), depending on the species studied. In response to changing ratios of pheromone components necessary to elicit behavior, ORC responses either decrease with the concentration of the non-preferred component (Hillier and Vickers 2011), or do not change (Akers and O'Connell 1988). In neither case were responses specific to or selective for the behaviorally effective ratio observed. Mixture effects in the first olfactory neuropil of insects (the antennal lobe, AL) and mammals (the olfactory bulb, OB) are similar to those in the receptor layer (reviewed in Christensen and White, 2000). As ORCs that express a particular olfactory receptor synapse in a single glomerulus in the AL or OB (Fishilevich and Vosshall 2005; Couto et al. 2005; Gao et al. 2000; Mombaerts et al. 1996; Ressler et al. 1994), the output of a glomerulus in response to an odor mixture is partly a function of the mixture effects at the periphery. Interconnections between glomeruli, chiefly mediated by GABAergic interneurons (Nowycky et al. 1981; Jahr and Nicoll 1982; Hoskins et al. 1986; Python and Stocker 2002; Wilson and Laurent 2005; Christensen et al. 1998) produce additional suppression or hypoadditivity of glomerular responses, with respect to the responses to 24 individual components (Carlsson et al. 2007; Silbering and Galizia, 2007; Olsen et al., 2010, Deisig et al. 2010; Tabor et al., 2004, Johnson et al. 2010, Trona et al. 2010, Kuebler et al. 2011, Silbering et al. 2008). Blend-related synergism is also more commonly observed at the output of the AL than at the input. As a result, the spatial output patterns of glomeruli reflect both elemental and configurational information about odor mixtures, as processing at this level both preserves representation of the components and produces a unique representation of the mixture (c.f. Kuebler et al. 2011, Deisig et al. 2010) . The Euclidean distance between the glomerular representation of blends and their components can also be increased by associative learning (Fernandez et al. 2009). Similar distance measures of the representations of synthetic mixtures change smoothly with the ratio of their components in the OB (Khan et al., 2008) and AL (Carlsson et al., 2007, Fernandez et al. 2009), and no selectivity for a particular ratio was observed in these previously reported experiments. Responses to natural odors, blends of multiple volatiles, often exhibit little difference from the responses to their components at this level of analysis (Lin et al. 2006; Carlsson et al. 2007). However, Najar-Rodriguez et al. (2010) have documented an important exception: responses in a particular glomerulus of a moth AL are selective for a particular ratio of one component of a host-plant odor to the whole. The selectivity of this response also parallels the selectivity of the animal to the blend composition. Despite the ecological and behavioral importance of odors with characteristic ratios, neural selectivity to a particular ratio of components in an odor blend has rarely been demonstrated. Blend processing in the antennal lobe of Manduca sexta 25 The pheromone system in the moth Manduca sexta (‘Manduca’) has long been a model for understanding how the interactions of glomeruli shape olfactory information processing. ORCs in the antenna are highly selective and sensitive for particular components of the pheromone blend released by calling females (Kaissling et al. 1989). Two of these ORCS each respond selectively to one of the two key components of the eight-component pheromone necessary to evoke olfaction-mediated flight in males: (i) E,Z,-10,12-hexadecadienal (bombykal, BAL) and (ii) E,E,Z-10,12,14-hexadecatrienal (EEZ) (Tumlinson et al. 1989). Each ORC population terminates in one of the enlarged glomeruli of the macroglomerular complex (MGC), a region devoted to processing pheromone signals that is found in many moth species (Boeckh and Boeckh, 1979; Matsumoto and Hildebrand, 1981; Kanzaki and Shibuya, 1986; Christensen and Hildebrand, 1987). ORCs responding to BAL terminate in the toroid, and those responding to EEZ terminate in the cumulus (Figure 2.3.1A). A population of PNs arborizing in each glomerulus responds with excitation to stimulation with the component activating input ORCs to that glomerulus, and inhibition to the component activating input to the neighboring glomerulus (Figure 2.3.1B) (Christensen and Hildebrand 1987, Hansson et al. 1991, Heinbockel et al. 1999; Lei et al. 2002; Heinbockel et al. 2004). Several lines of evidence suggest that mixture processing in this system is sensitive to the ratio between components, and is dependent on inhibitory interglomerular communication. Inhibition from glomeruli simultaneously activated by the pheromone blend enhances the ability of MGC PNs to follow pulses of stimuli, a feature critical for airborne tracking of odor plumes and locating the odor source (Christensen and 26 Hildebrand 1997; Christensen et al. 1998; Heinbockel et al. 1999). This effect is maximal at the natural ratio produced by females, and the enhancement is only seen in a subpopulation of PNs that are inhibited by stimulation with the component that activates the neighboring MGC glomerulus (Heinbockel et al. 2004). This study (Heinbockel et al. 2004) provided the first evidence of a ratio-coding mechanism in the AL. The enhancement of the temporal coding for odor encounters observed here constitutes a representation of the ratio between components that may underlie recognition of a conspecific mate. However, Heinbockel et al. also found that the firing rate output of individual cells was not selective for the conspecific ratio. Thus the activity of individual cells does not selectively encode the ratio between components separately from the encoding of encounters with the plume. Analysis of the behavioral selectivity of male moths for the conspecific pheromone ratio has found that the ratio of components affects an “arrestment threshold” after the odor source is located, but before mating is initiated (Roelofs 1978). Another investigation in the AL showed that the presence of both components in the blend enhances synchronous firing between PNs in the same glomerulus (Lei et al. 2002). These data suggest that the conspecific ratio between components may also be encoded in the synchronous output of PNs. Here, we report how the spiking output of the AL selectively encodes the behaviorally effective ratio of components in a natural odor across the independent channels represented by glomeruli. Using stimuli matched between free-flight behavior in a wind tunnel and electrophysiological recordings of PNs in the AL of male Manduca, we find that: (1) moths are selective for a range of ratios surrounding the natural, 2:1 ratio 27 (BAL:EEZ) produced by females, and that (2) pairs of PNs in the AL respond selectively to these ratios with enhanced synchrony. Pheromone blends with altered ratios, created by increasing or decreasing the concentration of either component, produce both reduced attraction to the odor source and reduced synchrony between spikes in the output of the MGC. We propose that the ratio coding discovered in this specialized, tractable system in an insect AL is also relevant to both, more generalist olfactory coding (Christensen and Hildebrand 2002; Christensen 2005) and olfactory coding in other taxa (Hildebrand and Shepherd, 1997; Ache and Young 2005; Kaupp 2010; Christensen and White, 2000). 2.3.2 Materials and Methods Preparation Male Manduca sexta (Lepidoptera: Sphingidae) moths were raised in the laboratory on artificial diet (modified from that of Bell and Joachim 1976) under a longday photoperiod (LD 17:7), as previously described (Sanes and Hildebrand 1976; Tolbert et al. 1983). For physiological experiments, animals were prepared for experiments as previously described (Christensen and Hildebrand 1987). Moths were restrained with wax in a closely-fitting plastic tube. The labial palps and proboscis were removed, and a window was cut in the dorsal portion of the head capsule. The cibarial pump and other muscles were excised to allow access to the brain. One AL was desheathed to facilitate penetration by the electrodes. To eliminate movement, the head was removed and pinned to a wax-coated petri dish, oriented with the ALs pointed upward. The preparation was superfused with a saline solution (150 mM NaC1, 3 mM CaC12, 3 mM KC1, 10 mM TES buffer, and 25 mM sucrose, pH 6.9; Christensen and Hildebrand 1987). 28 Behavioral experiments Attraction to a mixture of pheromone components was investigated by use of a custom-made Plexiglass wind tunnel (L × W × H = 4 × 1.5 × 1.5 m). Fans force air through a series of filters and baffles into the tunnel, producing a uniform, unidirectional flow (wind speed 20 cm/s) that facilitates the formation of an odor plume along the length of the tunnel. At the upwind end of the tunnel, 4 µl of one of the pheromone mixtures (see Table 1) or 4 µl of cyclohexane control was pipetted onto a 1 cm2 piece of filter paper. The odor source was centered vertically and laterally in the tunnel, 30 cm from the upwind vent. The concentration of pheromone components used in these experiments was chosen to approximate the rate of pheromone emission by a calling female moth (Lei et al. 2009). A naïve male moth (2-3 days post-emergence) was placed on a stand 3.5m downwind, and allowed to behave for 5 minutes or until contact was made with the odor source. Moths that did not take flight within 5 min were not scored, and not considered in the analysis. Low intensity light (0.5 lux), filtered with red photographic gel, was used to facilitate behavioral scoring and video recording of the flight paths. Video records were made on analog tape using a CCD video camera (Cohu model 6415-2000) with a macro lens and were used to confirm real-time behavioral scoring. The presence or absence of the following behaviors was recorded: upwind flight (within 1.5 m of the odor source), approach (flight directed toward the odor source, within 0.75 m), close hover (cessation of forward movement and hovering within 10-30 cm of the odor source) contact with the odor source, and abdomen curl (a typical mating posture). Moths were tested between 29 one and two hours after the beginning of scotophase. Individual moths were tested with one of 5 odor mixtures, one of three dilutions of the 2:1 mixture, or cyclohexane control (Table 1). Odors were chosen semi-randomly for each trial. Overall stimulus effects and pair-wise comparisons between stimuli were tested using the chi-square and Fisher exact tests, respectively, with an alpha level of 0.05 (Sigma Plot 11, Systat Software, CA, USA). EAG calibration of stimulus concentration An electroantennogram (EAG) device (PRG-2, Syntech) mounted on a stand was placed in the wind tunnel, centered vertically and laterally, 0.3m from the odor source. Antennae were removed from male Manduca (n=4) at 2-3 days post-emergence, and attached to the EAG probe using electrode gel (Spectra 360, Parker Laboratories Inc., NJ, USA). The antennae were rotated such that the ventral side faced upwind, toward the odor source. Signals were acquired via a PC interface board (IDAC-02, Syntech, Germany) and Autospike32 (Syntech, Germany) software in a laptop computer. EAG deflections were identified as a significant (>2 standard deviations) deflection from baseline using custom software in MATLAB (The MathWorks, Inc., MA, USA). The peak amplitudes of all detected deflections were calculated from the baseline, and the mean, standard deviation, and range of all recorded deflections was calculated. The EAG device was inserted into the physiology rig used for the paired single-cell recordings (described below), and antennae from additional Manduca males (n = 4) were stimulated with 15 pulses of each of 5 concentrations of the 2:1 pheromone mixture: 3000, 300, 30, 30 3, and 0.3 ng/µl total concentration. The amplitudes of the evoked EAG deflections were calculated as above. Juxtacellular Recording In order to record the responses of pairs of neurons to a large (10-15) battery of odors, the longer duration of an extracellular recording technique was required. We therefore used a juxtacellular recording method adapted from Pinault (1996). This technique allows for high signal-to-noise ratio for the detection of spikes from a single cell, and recording sessions lasting several hours. Thin-wall borosilicate glass capillaries were pulled using a laser electrode puller (Sutter P-2000, Sutter Instruments, CA, USA) to a tip diameter of ~1 µm. Electrodes were filled with a 2M KCl solution, yielding resistance <20 MΩ. Signals were amplified to 1,000X by an Axoprobe-1A amplifier and an inline 10X DC amplifier. Two electrodes were lowered into the AL by Leica micromanipulators into the MGC region and advanced slowly until spikes were distinguishable from baseline noise. Only recordings with single spike amplitude (corresponding to spikes from a single cell), were retained for analysis. Sensory stimulation Olfactory stimuli were delivered by injecting pulses of odor-laden air (250 ms duration, 0.1 l/min) into a constant clean air flow (1 l/min) directed at the middle of the antenna ipsilateral to the AL from which recordings were made. A solenoid-activated valve controlled by an electronic stimulator (WPI, FL, USA) produced trains of 15 pulses at a 250 ms interval. Air from the valve was directed through a 0.5 ml glass syringe containing filter paper loaded with 1 µl of pheromone components diluted in 31 cyclohexane. The odor stimuli consisted of two key pheromone components either presented alone or in a mixture: the minor component of the female pheromone (EEZ), and the major component (BAL) (Tumlinson et al. 1989, 1994). The natural ratio of BAL to EEZ found in solvent washes of the female pheromone gland is approximately 2:1 (Tumlinson et al. 1989). Additional mixtures used in the physiological experiments are presented in Table 2. Characterization of neurons The juxtacellular recording technique can allow for intracellular staining of neurons from which recordings have been made, but in our hands this method has a low (<10%) success rate. Therefore, identification of uniglomerular MGC PNs (uPNs) and, especially, discrimination of these PNs from LNs and multi-glomerular PNs, relies on the physiological attributes of previously recorded and intracellularly stained PNs. Three criteria were used: a bursting spontaneous firing pattern, response specificity to pheromone components, and a multi-phasic response pattern. Uniglomerular MGC PNs respond with excitation to one of the two key pheromone components, inhibition or no response to the other, and excitation to a mixture of both components (Lei et al. 2002, Heinbockel et al. 1999, Heinbockel et al. 2004, Christensen et al. 1987). uPNs with dendrites in the cumulus respond with excitation to EEZ, and inhibition of background spiking or no response to BAL. Similarly, uPNs with dendrites in the toroid respond with excitation to BAL, and inhibition of background spiking or no response to EEZ (see Figure 2.3.1B). Both types respond with excitation to mixtures of both components. Responses to the pheromone blend are multi-phasic: odor stimulus provokes a brief 32 period of inhibition (I1), followed by a burst of spikes, and finally a longer period of inhibition (I2) before returning to a baseline firing rate (Christensen and Hildebrand 1987). While I1 is not visible in extracellular recordings, I2 is reliably present and is used to characterize uPNs. Finally, the background firing pattern of MGC PNs is characterized by random bursts of spikes, while LNs exhibit more regular, constant background firing, and this trait can be used to reliably discriminate between cell types (Lei et al. 2011). Data acquisition and analysis The analog signal was digitized at 25 kHz using Datapak (Run Technologies, Mission Viejo, CA). All spike data were processed and analyzed using custom MATLAB software (The MathWorks, Inc.). For each individual neuron, the timestamps of each spike were extracted, and a peristimulus time histogram and interspike interval histogram were produced for each stimulus. From these, the peak firing rate, mean instantaneous firing rate, and interspike-interval variability (coefficient of variation, CV) (Softky and Koch, 1993) were calculated. For each paired recording, the shift-predictor subtracted cross-correlogram and cross-interval histogram (Perkel et al., 1967a) were produced as measures of the synchronous firing of neurons. A correlation-based similarity metric was calculated as an additional measure of synchrony sensitive to fine-scale spike timing (Schreiber et al., 2003, Lyttle and Fellous, 2011). Briefly, spike trains are convolved with a Gaussian filter of width σ, or alternatively a causal decaying exponential function h(t) = exp(-t/τ)u(t), where u(t) is the Heaviside step function. This latter kernel is chosen to mimic the membrane properties of a coincidence detector, and is more sensitive to small differences in synchronous firing (Paiva et al. 2010). The resulting filtered spike trains 33 are represented as vectors . The inner product is taken between all paired spike trains for each trial, and divided by the norms of each individual spike train in the trial. Similarity between simultaneously recorded spike trains 1 and 2 for trial i in response to a stimulus is therefore computed as: The mean similarity is calculated across trials for each stimulus. In analogy to the shiftpredictor method for histograms, for each stimulus we calculate similarity between all non-simultaneous pairs of spike trains in response to the same stimulus and subtract the mean of this value from the mean similarity between simultaneous pairs. This provides a measure of “residual” similarity, i.e. the similarity that is greater than expected by chance coincidence in spike trains with a given firing rate and large-scale firing pattern. For each response measure, we quantify selectivity for particular stimuli by computing the lifetime sparseness (Sel): where ri is the response to stimulus i, and n is the number of stimuli in the series (Vinje and Gallant, 2000). This nonparametric statistic takes values between 0 (nonselective) and 1 (highly selective). For each stimulus series and response measure, we also tested overall stimulus effects and pairwise comparisons by a one-way repeated measures ANOVA and Fisher least-squares distance. Local field potential recordings 34 For local field-potential (LFP) recordings, animals were prepared as above. Electrodes were similar to those used in juxtacellular experiments, but modified to have a larger opening and thus lower resistance (<10 MΩ). A single electrode was inserted into the MGC area, and the antenna was stimulated with the same series of odorants as in the other physiological experiments. The signal was low-pass filtered at 100 Hz by an Axoprobe-1A amplifier. The peak frequency and amplitude was obtained from the peristimulus spectrogram (Neuroexplorer, Plexon, Inc., TX, USA). 2.3.3 Results Behavioral experiments To justify the search for neural activity that encodes the ratio between components of an odor blend, we first determined whether Manduca males exhibit an innate selectivity for a particular ratio of the two key components emitted by the female. Naïve male moths were tested individually in a wind tunnel (Figure 2.3.2A) for attraction to pheromone mixtures ranging from the natural 2:1 ratio (BAL:EEZ) emitted by female moths, to mixtures with a 100-fold difference between components (Table 1). The ratio between components did not affect (Chi-square test, P>0.05) the proportion of animals initiating upwind flight, nor the proportion that made an approach to the pheromone source (Figure 2.3.2B), indicating that detection, and longer-range attraction, were not influenced by this feature of the odor. For close-range behaviors, there was a significant interaction between component ratio and the proportion of moths exhibiting both close hovering over the odor source and contact with the filter paper emitting the odor (Chisquare test, P<0.05). For these behaviors, only the 2:1 and 0.2:1 ratios were significantly 35 different from control (cyclohexane, Fisher exact test P<0.05). In the final stages of a mating approach, the male adopts a mating posture with the abdomen curled under the female. This behavior was significantly affected by the ratio of pheromone components (Chi-square test, P<0.001), and the natural 2:1 ratio as well as the 0.2:1 and 2:0.1 ratios were significantly different from control (Fisher exact test P<0.001 and P<0.05). These data demonstrate a behavioral selectivity for pheromone mixtures within one order of magnitude from the natural 2:1 ratio emitted by females. To control for the decrease in total concentration effected by changing the ratio of pheromone components, we performed additional experiments testing animals against serial dilutions of the 2:1 ratio mixture. Total concentration did not significantly affect the proportion of animals exhibiting all scored behaviors (Figure 2.3.2C ), close hover and abdomen curl shown, Chi-square test, P>0.05) and behavior was significantly different from control at all concentrations (Fisher exact test, P<0.05). Electrophysiological experiments Having established a behavioral selectivity for a particular ratio of pheromone components, we then examined how varying the ratio of the two key pheromone components influenced the neural representations of these mixtures in the AL. First, we calibrated the stimuli delivered in the electrophysiology rig to correspond to the intensity of stimuli encountered in the behavioral experiments. Electro-antennogram recordings in the wind tunnel, taken at a distance from the source where both “approach” and “close hover” behaviors were observed, reveal a highly-variable response at the antenna (mean amplitude of 0.09 mV, range from 0.015 mV to 0.33 mV, n = 10 antennae)(Figure 36 2.3.2D). In analogous recordings in the electrophysiological rig used for all of the following experiments, all but the highest concentrations of the 2:1 ratio mixture (0.3, 3, 30, 300, and 3000 ng/µl) produced EAG deflections within the range observed in the wind tunnel (0.057 ± 0.016 mV, 0.086±0.047 mV, 0.107±0.097 mV, 0.209±0.097 mV, 0.604±0.224 mV) (Figure 2.3.2D). The intensities of stimuli used in the electrophysiological experiments are therefore comparable to those used in the behavioral experiments. We made 19 simultaneous paired recordings of PNs in the MGC of Manduca. These neurons can be readily identified by a background firing pattern characterized by random bursts, and a triphasic response to odor stimulation (see Materials and Methods). We focus on MGC PNs that arborize in one of the two main glomeruli of the MGC, the cumulus or toroid (Hanson et al. 1991) (see Figure 2.3.1A). ORCs that are highly selective for one of the two key components of the female pheromone (EEZ or BAL) confer their specificity to the PNs arborizing in that glomerulus (cumulus or toroid, respectively; Figure 2.3.1A) (Kalinova et al, 2001, Hansson et al. 1991, Heinbockel et al., 1999) such that PNs respond with excitation to only one component (Figure 2.3.1B). Stimulation with the other key component of the pheromone activates ORCs that project to the neighboring glomerulus, where they synapse on LNs that feed forward to the PNs not directly excited by that component (Figure 2.3.1A) (Heinbockel et al., 1999). PNs either show inhibition of background firing in response to this stimulation, or no response (Figure 2.3.1B). We use this response specificity to identify uniglomerular MGC PNs. For simplicity, we refer to PNs that arborize in the cumulus and respond with excitation 37 to EEZ as ‘EEZ-PNs’ and PNs that arborize in the toroid and respond with excitation to BAL as ‘BAL-PNs.’ Of the 19 recorded, 13 were pairs of EEZ-PNs, 2 were pairs of BAL-PNs, and 4 were mixed EEZ/BAL pairs. As only one of the mixed EEZ/BAL pairs exhibited significant synchrony in response to odors, they were not included in analyses. The significance of the lack of synchrony between glomeruli is explored in the discussion. For unknown reasons, this method was biased toward recordings from EEZPNs, and we successfully recorded from only two pairs of BAL-PNs. Both populations exhibited similar responses in all experiments, but since the low number of BAL-PNs prohibits analysis of each population separately, we excluded these cell pairs from the initial analysis. In a final step, we included the two pairs of BAL-PNs in the analysis and found that this did not affect the statistical significance of the results with the 13 pairs of EEZ-PNs alone. Hereafter, we will refer to the component that produces a spiking response in a cell as the “excitatory component” and the component that produces spike suppression or no response as the “inhibitory component.” This distinction is purely from the perspective of a single cell in a particular glomerulus, and does not imply a behavioral consequence. Responses of individual PNs Across all recorded individual cells, stimulation with mixtures containing increasing concentrations of the excitatory component mixed with a single concentration of the inhibitory component produced responses with decreasing latency and increasing frequency in all cells (see Figure 2.3.3A, top panel for example). This concentration- 38 dependent response was similar to responses to stimulation with increasing concentration of the excitatory component alone (Figure 2.3.3B). For both series, responses saturated such that the three highest concentrations evoked statistically indistinguishable responses in BAL and EEZ cells (Tukey test, P <0.05). However, response variability across all recorded cells was generally reduced in the responses to the mixtures and was significantly reduced at the 2:1 ratio (Levene’s test, P<0.05). Thus the context of the mixture reduced the variability of firing rate responses in a ratio-dependent manner. Mixtures with varying concentrations of the inhibitory component produced responses with similar time course and peak firing rate (see Figure 2.3.3A, bottom panel for example). Across the population, varying the relative proportion of the inhibitory component did not significantly alter the mean firing rate across all recorded cells (Figure 2.3.3C, thick lines, Friedman repeated measures ANOVA, P>0.05) nor the responses of individual cells (Figure 2.3.3C, grey lines). This is consistent with previous observations that the inhibitory connection between MGC glomeruli does not simply decrease firing rate (Heinbockel et al. 1999; Waldrop et al. 1987; Christensen and Hildebrand 1997, Lei et al. 2002). Finally, we tested the hypothesis that the ratio between components may be encoded in the relative firing rate between cells responding to each component. For this, we randomly paired cells and calculated the ratio between the mean instantaneous firing rates of each cell in response to different concentrations of the cell’s excitatory component. The result is a shallow curve, that while generally increasing with the ratio between components, exhibits no statistically different responses at any ratio (Figure 39 2.3.3D, Friedman repeated measures ANOVA, P>0.05). This reflects both the variability and saturation of individual responses to concentrations of an individual component. Thus while some information about the ratio between components is available in the relative responses of individual PNs, the signal-to-noise level of this representation is quite low. Synchrony in PN pairs As synchrony between PNs in the MGC has previously been demonstrated to rely on the presence of both components of the pheromone blend, we next tested whether synchrony may further encode the ratio between components in the blend. To quantify synchrony, we used both the cross-interval histogram (Perkel et al. 1967b), which reports the probability of observing spikes in two cells with a particular latency, and a “binless” method that computes the similarity between spike trains using a correlation-based metric (Schreiber et al. 2003). The latter method confers several benefits over the more widely used histogram-based methods: it does not require the choice of an arbitrary bin size, performs better in discriminating between responses that vary only in the degree of synchronous firing (Paiva et al. 2010), and, depending on the choice of kernel used in the method, can make predictions about characteristics of the cell decoding information contained in synchronous spikes (see Discussion). An example of these measures for a pair of MGC PNs is in Figure 2.3.4, panels C and D. Although the results shown in this example are from an experiment where the inhibitory component (BAL) was varied, and the excitatory component (EEZ) held constant, similar results were obtained from the 40 corresponding series in which the excitatory component is varied, and in the population data (Figure 2.3.5) we report results from both experiments. The cross-interval histogram for an example pair reveals a greater proportion of spikes with intervals between 0 and 4 ms for all mixtures compared to the excitatory component presented alone (Figure 2.3.4A and B). The ratio between components in the mixture affects chiefly the proportion of spikes with intervals between 0 and 2 ms, with the greatest proportion of spikes in these intervals observed in response to the natural 2:1 mixture. The binless method reveals a similar effect of component ratio on similarity between simultaneously recorded spike trains from an example pair (Figure 2.3.4C and D). For both, we utilized a shift-predictor subtraction method (Perkel et al. 1967), wherein the average synchrony between non-simultaneous responses is used to estimate the coincidence between spikes expected by chance, i.e. attributable to factors such as the firing rate of each cell. Subtracting this expected coincidence from the observed coincidence between simultaneously recorded spike trains yields a measure of the synchrony generated by the activity of the neural circuitry (Perkel et al., 1967; Nowak et al., 1995; de Oliveira et al., 1997; Das and Gilbert, 1999; Steinmetz et al., 2000; Usrey et al., 2000; Bair et al., 2001). For the example pair in Figure 2.3.4, both methods result in a larger difference between the shift-predictor subtracted synchrony in response to the 2:1 mixture than to other mixtures (Figure 2.3.4B and D). The binless method, however, allows a finer scale of analysis, and reveals a peak in shift-predictor subtracted synchrony at a particular kernel size (the width of the function convolved with the spike trains, 41 comparable to the interval in the cross-interval histogram) for each mixture. Results were comparable for both measures of synchrony. We proceed using the shift-predictor subtracted similarity as our measure for synchronous firing between pairs of neurons, and now analyze the representation of the ratio between components of the pheromone using this measure. For each kernel size, the synchrony-based selectivity is calculated as a measure of the “tuning” of the pair of neurons to a particular ratio between components (Figure 2.3.4F). Of 19 pairs recorded, 12 exhibited a peak in selectivity at a particular kernel size for stimuli in which the proportion of either the excitatory or inhibitory component was manipulated. Of the 10 pairs that were tested with both manipulations, eight pairs exhibited selectivity for a particular ratio when either the excitatory or inhibitory component was altered. For the remaining two pairs, a peak in selectivity was only observed when the excitatory component was altered. Figure 2.3.4E illustrates the tuning of a PN pair’s response to particular ratios of the pheromone components at kernel widths for the peak (black line) and minimum (grey line) selectivity. Though a plurality of pairs (8 out of 12) had maximal shift-subtracted synchrony to the natural 2:1 ratio, other pairs exhibited tuning to mixtures with other proportions of either excitatory or inhibitory components. For both manipulations, averaging across all selective pairs yields a population tuning curve with a peak at the natural 2:1 ratio. Though we recorded from pairs mainly in one glomerulus, we assume that synchronybased tuning operates simultaneously in both responding glomeruli, i.e. as PNs in one MGC glomerulus respond to mixtures with varying proportions of their excitatory 42 component, PNs in the neighboring glomerulus are responding to mixtures with varying proportions of their inhibitory component. Indeed, mixtures with varying proportions of either EEZ or BAL produce maximal synchronous activity in the population of recorded cells at the natural 2:1 ratio (Figure 2.3.5A and B). There is a significant interaction between component ratio and shift-predictor subtracted similarity for both sets (One-way repeated measures ANOVA, P<0.05). In parallel to the behavioral data, the 2:1 ratio is significantly different from all but the neighboring ratios (Fisher’s least significant difference, α = 0.05). The neural selectivity represented by shift-predictor subtracted similarity is not correlated with the behavioral selectivity exhibited by animals in performing either of the two long-distance flight behaviors (Pearson’s coefficient: upwind flight, R = 0.47, and approach, R = 0.42, P>0.05), but is strongly correlated with the three near-distance flight and mating behaviors (Pearson’s coefficient: close hover, R = 0.88, contact, R = 0.97, and abdomen curl, R = 0.97, P<0.05). We therefore conclude that synchrony, as measured by shift-predictor subtracted similarity, constitutes a representation of the ratio between components of the pheromone blend. Furthermore, the ratio is represented not as an absolute value, but relative to the 2:1 ratio released by female moths and most attractive to males. Synchrony and the local field potential oscillation Finally, we make some inferences about the mechanism underlying the observed synchrony. Synchrony is typically associated with oscillations of the local field potential, the concerted activity of cells in the circuit entrained to a particular frequency. Though oscillations have been observed in the AL of Manduca (Heinbockel et al. 1998, 43 Christensen et al. 2003, Ito et al. 2009, Daly et al. 2011), spikes in PNs are poorly entrained to the oscillation, and typically fire faster than the LFP frequency (Christensen et al., 2003; Ito et al., 2009; Daly et al., 2011). We confirm the observation of LFP oscillations in response to stimulation with pheromone mixtures (Figure 2.3.6B), but neither the power nor the peak frequency was modulated by the ratio between components (One-way repeated measures ANOVA, P>0.05, data not shown). PNs exhibited regular firing (Figure 2.3.6A and C) at a rate dependent on the concentration of the excitatory component, often above or below the observed range of LFP oscillations. Our results confirm other reports (Christensen et al. 2003; Daly et al. 2011) that oscillations do not entrain the output of the AL as is observed in the locust (Laurent and Davidowitz, 1994; Wehr and Laurent, 1996). However, as discussed below, oscillatory activity may not entrain PN spikes to a particular frequency but may still play a role in the synchrony observed in these experiments. For this reason, we include an oscillatory mechanism in our preliminary model of the circuit underlying synchrony-based encoding of odor component ratios (Figure 2.3.7). 2.3.4 Discussion We have demonstrated that the output of the AL of Manduca sexta encodes the natural, behaviorally-effective ratio between components of this species’ pheromone blend in the synchronous activity between PNs in the same glomerulus. Male moths exhibited increasing behavioral selectivity for the natural 2:1 ratio as they neared the source of the odor, ultimately exhibiting the greatest selectivity when adopting a typical mating posture. Firing rate changes of PNs in the AL were largely insensitive to 44 component ratios, and there was little representation of the ratio between components in the ratio between the mean instantaneous firing rates of individual PNs responding to different concentrations of each component. However, synchronous spikes between pairs of PNs in the same glomerulus were increased for ratios in a range of plus or minus 1 order of magnitude surrounding the 2:1 ratio. Individual pairs were selective for a particular ratio in this range, and all selective pairs showed reduced selectivity to higher or lower ratios and to the individual components alone. Male Manduca exhibit selectivity for the natural component ratio Many moth species share their habitats with closely related species that rely on the same pheromone components for male-female communication, differing only in the ratios produced by females and attractive to males (Yang et al. 2009; Dunkelblum and Mazor 1993; Ming et al. 2007; Minks et al. 1973; Löfstedt et al. 1991, Schlyter et al. 2001). As yet, the composition of pheromones released by closely related species sympatric with Manduca have not been described, and in particular the EEZ component has not been identified in the pheromone of any other insect (El-Sayed 2011). However, the behavioral selectivity for the natural 2:1 ratio between the BAL and EEZ components we observed in our wind tunnel tests (Figure 2.3.2B) is similar to the selectivity determined by wind tunnel and pheromone trap results in other species. The range of acceptable pheromone ratios, or the “response window” (Roelofs 1978), in this species was considerably wider than in many others (Schlyter et al. 2001) but was comparable to the response window for Agrotis segetum (Löfstedt et al. 1985) and species of the genus Adoxophyes (Yang et al. 2009). Several factors could account for this relatively wide 45 response window. First, the width of the male response window roughly correlates with the range of ratios produced by females, but is typically wider (Shlyter et al. 2001, Löfstedt 1990). The width of the response window is also determined by (1) a trade-off between overall sensitivity to a particular ratio and a greater chance of mating success, i.e. fewer false-negative errors, with a wider response window (Svensson 1996), and (2) a stabilizing or directional selective pressure by the existence of other, sympatric species or races occupying different ecological niches in the animal’s range (Phelan 1992). As yet there is no data on the individual variability of female production, i.e. the range of ratios males should find attractive to maximize mating success, and the role of the above ecological constraints in shaping the response window of Manduca. Additionally, for practical reasons we used animals from a laboratory-reared population, which are not subject to selection pressures from sympatric species. For this reason, we hypothesize that the response windows for wild-caught moths may be narrower. The selectivity for the 2:1 ratio increased for behaviors exhibited nearer the odor source (Figure 2.3.2B). Similar sharpening of the behavioral response was observed for similar behavioral steps in other moth species (Schlyter et al. 2001, Baker et al. 1981, Linn and Roelofs 1983, Linn and Roelofs 1985). There was no difference between ratios in eliciting upwind flight towards the odor source, and little difference in approach to the odor, suggesting that the presence of pheromone components, regardless of their configuration, exceeds the “orientation threshold” for odor-mediated flight, and the abrupt change in behavioral selectivity between the “approach” and “close hover” steps suggests that the ratio between components determines an “arrestment threshold” at this 46 point (Roelofs 1978). Whether these changes in selectivity reflect internal processing that triggers long-distance behaviors for a wider range of ratios, or if animals cannot perceive the ratio of components at a distance is not clear. EAG responses in the physiology rig are comparable to those recorded under behavioral conditions In calibrating the stimuli used in the electrophysiological experiments, we took EAG measurements from a distance at which ratio-selective behaviors were observed (Figure 2.3.2A and B). The range of amplitudes of the EAG deflections observed at this distance corresponded to all but the highest concentrations of pheromone mixtures used in the electrophysiological experiments (Figure 2.3.2D). EAG deflection amplitudes we observed concur with previous measurements from stationary antennae in the wind tunnel (Vickers et al. 2001). Previous studies also concluded that the amplitude of EAGs in a flying animal were 2-3 times greater than in a stationary antenna, perhaps attributable to active sampling via wing beats (Vickers et al. 2001). Along with the calibrations of emission rate and plume structure performed by Lei et al. (2009) in identical conditions, we are confident that the wind tunnel conditions simulate odor-mediated location of a calling female and that the stimuli delivered in the physiology rig are within the range experienced by a freely-behaving animal. Firing rate responses of individual PNs are mostly insensitive to ratio We begin the investigation of ratio coding in this system with hypotheses involving the firing rate responses of MGC PNs. We tested two hypotheses: (1) The ratio between components in the blend is represented by a ratio-dependent modulation of PN 47 firing rate, such that output is maximal at the behaviorally effective ratios (as observed in Najar-Rodriguez et al. (2010) for host-plant odors), (2) the concentration-dependent responses of individual PNs form the basis of a ratio code that is read out at higher levels of processing, i.e. the ratio of components is reflected in the ratio between firing rates of PNs in each MGC glomerulus. PNs in the AL of insects typically encode the concentration of volatiles to which they are responsive in a firing-rate code (Bhandawat et al., 2007; Bichao et al., 2005; Reisenman et al., 2004, 2005; Schlief and Wilson, 2007; Silbering et al., 2008; Wilson et al., 2004; Dacks et al. 2008; though see Stopfer et al., 2003 for a notable exception). Pheromone-responsive MGC PNs have unusually high sensitivity, but similarly encode in their firing rates the concentration of the component to which they are selective (Christensen et al., 1991; Hartlieb et al., 1997; Kanzaki et al., 1989; Jarriault et al. 2009; Figure 2.3.3B). When combined with a constant concentration of the inhibitory component, so as to generate a series of component ratios (Table 2), we observed no selective, increased response to a particular ratio (Figure 2.3.3B). This confirms previous reports that the firing rate response of a PN encodes the concentration of the excitatory component, and not the ratio between components (Heinbockel et al. 2004). Although we were careful to omit multi-glomerular PNs (mPNs) that arborize in both MGC glomeruli from this study (see above), mPNs have occasionally been observed to exhibit blend-specific responses (Christensen et al. 1995b; Hansson et al. 1994; Hansson et al. 1991) and in a few recorded instances, discriminate between component ratios of closely related species (Wu et al. 1996; Anton et al. 1997). These examples of a firing rate-based code for blend ratios may represent an additional channel for this 48 information. As our primary interest is in how communication between separate output channels (i.e. uPNs) can facilitate encoding of the ratio between components, we did not examine mPNs in this study. We observed a small, but not significant, increase in responses across a range of excitatory component concentrations in the mixture, and a significant decrease in the variability of responses across the population for the 2:1 component ratio (Figure 2.3.3B). The primary effect of the addition of the inhibitory component was a normalization of firing rates. While PNs responded with concentration-dependent firing rate curves of varying steepness and saturation levels when presented with excitatory component alone, these curves became more similar to each other in the context of the mixture (individual PN data not shown), reducing the variability of firing rate responses at each concentration, with the greatest reduction at the natural 2:1 ratio. The implications of this result for the mechanism underlying ratio coding are further discussed below. Finally, because of the small increase in the mean firing rate response across concentrations, the firing rate representation of the excitatory component concentration saturated at a lower level in the context of the blend (Figure 2.3.3B). We next tested whether the concentration of the inhibitory component alters the response of a PN to a fixed concentration of its excitatory component. PN responses were unaffected by the concentration of the inhibitory component in the mixture, and exhibited neither inhibition of the response to the excitatory component, nor a selective response for a particular ratio between components (Figure 2.3.3C). Though communication between glomeruli is GABAergic and hyperpolarizing, inhibition does not typically result 49 in a reduction in firing rate response to excitatory input (Heinbockel et al. 1999; Waldrop et al. 1987; Christensen and Hildebrand 1997, Lei et al. 2002). As the firing rate response of MGC PNs was not particularly sensitive to the ratio of components in the mixture, we examined whether information about component ratios was simply passed on to higher levels of processing, encoded in the ratio of firing rate responses of PNs responding to different concentrations of their excitatory components. Two observations make this encoding scheme unlikely. First, as the concentration of the excitatory component increases over 4 orders of magnitude, the firing rate increases by a factor of four, and the variability at each concentration can be 50% or more of the mean (Figure 2.3.3B). Second, responses saturate within the range of concentrations encountered in the behavioral experiments (Figure 2.3.3B and 2.3.2D). While the ratio between pairs of PN responses to different concentrations of their excitatory components (recorded non-simultaneously) generally increased with greater difference between component concentrations, the range of firing rate ratios was small and nearly entirely encompassed by the variability at any one component ratio (Figure 2.3.3D). We conclude that while some information about component ratio is available in this dimension of the output, it is neither selective for behaviorally effective ratios, nor does it facilitate discrimination between ratios well. Synchronous firing of PNs is selective for the behaviorally effective ratio In previous work, more synchronous spikes were produced by pairs of PNs in the same glomerulus in response to the 2:1 mixture of pheromone components than to the excitatory component alone. Synchrony between output neurons in the same (Puopolo & 50 Belluzzi, 2001; Schoppa & Westbrook, 2001; Lei et al. 2002; Kazama and Wilson, 2009) or different (Schoppa 2006a, 2006b; Kashiwadani et al. 1999, Lei et al. 2004; Christensen et al. 2000; Riffell et al., 2009a; Riffell et al., 2009b) glomeruli is a common feature of olfactory processing in the AL and OB. In this study, we utilize a “binless” method of synchrony measurement (Schreiber et al. 2003). This method avoids problems inherent in other, histogram-based measures of synchrony, e.g. crosscorrelograms (Perkel et al. 1967b), or joint peri-stimulus time histograms (Aertsen et al. 1989): quantization noise that increases the variability of measurements (Kruskal et al. 2007) and boundary effects that limit the utility of the measure for very small spike time differences (Grün et al. 1999). The highly regular (Figure 2.3.6C) responses of MGC PNs and the response pattern of a single burst in response to the short-duration stimuli used in these experiments (Figure 2.3.3A) allowed us to focus on patterns of spike-to-spike synchrony and did not necessitate methods sensitive to more complex patterns (reviewed in Lyttle and Fellous, 2011; Paiva et al., 2010; Kreuz et al. 2007). The binless method chosen has been shown to be highly sensitive in discriminating spike trains that differ only in degree (precision and prevalence) of synchrony (Paiva et al., 2010). Using this binless method, we found that pairs of MGC PNs exhibited maximum shift-predictor subtracted synchrony to pheromone mixtures with component ratios near the natural, 2:1 ratio (Figure 2.3.5). The high temporal resolution of this method also allowed us to determine the selectivity of the response of a given pair for increasing size of the kernel used (Figure 2.3.4F). Kernel size is comparable to bin width, and provides a measure of the precision of synchronized spikes (Kruskal et al. 2007). The majority of 51 PN pairs exhibited a peak in selectivity for a particular ratio between 0 and 5 ms. The position of this peak was variable, but most commonly was found between 0 and 1 ms (Figure 2.3.4F). This indicates a high degree of precision, comparable to the “sharpest” synchrony observed in the mammalian brain (Mastronarde, 1983; Schnitzer and Meister, 2003; Alonso et al., 1996; Dan et al., 1998; Takahashi and Sakurai, 2009; Swadlow et al., 1998). A particular benefit of the use of this binless method is that as the convolving kernel was chosen to approximate the dynamics of a post-synaptic potential at a receiver neuron (van Rossum 2001), it makes possible specific predictions about the membrane time constant of the coincidence detector neurons that integrate information about component ratios (Koch et al. 1996). Excitatory post-synaptic potentials (EPSPs) in the Drosophila mushroom body (MB, an associative and integrative center of the insect brain) decay fairly rapidly, but still well outside of the range predicted by our data (11.5 ms, Turner et al. 2008). Our data predicts that cells receiving input from MGC PNs, acting as high-pass filters with EPSP decay constants less than 1–2 ms, would respond selectively to behaviorally effective ratios of pheromone components. Cells with such rapid dynamics are found in the vertebrate auditory system (c.f. Macleod and Carr 2004), suggesting that it is not outside of the range of biological possibilities. However, as the coincident arrival of synchronous spikes is generally more effective at depolarizing membranes, a receiver cell with slower membrane dynamics could convert the arriving synchrony code into a rate code for component ratios. Although integration of glomerular channels in the MB has been well-studied, odor coding at this level is sparse, (Szyszka et al. 2005 Perez-Orive et al., 2002; Broome et al., 2006; Murthy et al., 2008; Turner et al., 52 2008, Honegger et al. 2011) and non-selective (Honegger et al. 2011), consistent with its role as a general associative network. Ratio-selective cells would be more likely to be found in areas of the lateral protocerebrum that receive overlapping input from MGC glomeruli (Homberg et al. 1988, Seki et al. 2005). Oscillations and synchrony Synchrony-based codes in olfaction have been most often investigated in the locust AL (Laurent and Davidowitz, 1994; Wehr and Laurent, 1996) and the mammalian olfactory bulb (Kay et al. 2009), where spikes in output neurons are typically entrained to circuit oscillations. In the moth AL, although oscillations are observed, spikes in PNs are weakly entrained to the oscillatory frequency, and oscillations are not coherent across the AL (Christensen et al., 2003; Heinbockel et al., 1998; Ito et al., 2009; Daly et al. 2011). We have recently argued that in moths and other flying insects that follow odor plumes to their sources, strict oscillatory synchrony is not observed because the system emphasizes rapid responses to unpredictable encounters with odors in the plume (Christensen 2005; Martin et al. 2011). However, the mechanics of oscillation-based synchrony do not preclude it from playing a role in synchronizing moth PNs in this system. We confirm previous reports (Christensen et al. 2003, Daly et al. 2011) of odorevoked LFP oscillations in the moth AL (Figure 2.3.6B). Neither the frequency nor the power of this oscillation was modulated by the ratio of pheromone components, suggesting that the LFP reflects sub-threshold membrane oscillations (as observed in other systems: Neville and Haberly, 2003; Margrie and Schaefer, 2003) rather than synchronized spikes. The responses of PNs were themselves oscillatory (Figure 2.3.6A 53 and C), at a frequency determined by the concentration of the excitatory component independent of the LFP frequency. This observation resembles the characteristics of type 1 neural oscillators, which can fire at arbitrary frequencies, dependent on input current (Hodgkin 1948; Hanzel et al. 1995; Rinzel and Ermentrout 1998). In contrast, type 2 oscillators tend to fire only at a particular frequency, and readily phase-lock with an LFP oscillation (Galan et al. 2007). The mitral cells of the OB are type 2 oscillators (Galan et al. 2005), and the rate-invariant firing and high degree of phase-locking observed in locust PNs suggests that they may have similar dynamics (Laurent and Davidowitz, 1994; Wehr and Laurent, 1996; Stopfer et al., 2003). The available evidence from the moth AL suggests that the network generating the LFP oscillations and the intrinsic oscillations of PNs are separate and weakly connected, similar to a model suggested by Jefferys et al. for networks in the mammalian hippocampus (1996). Type 1 oscillators can be influenced, though not necessarily entrained, by pulses of inhibition that delay the phase of their intrinsic oscillation (Ermentrout 1996; Hansel et al. 1995) (for a schematic of this effect, see Figure 2.3.6D). As the degree of delay is dependent on the relative phase between a neuron’s intrinsic oscillations and the network oscillations (Ermentrout 1989), a common oscillatory signal can synchronize PNs firing at much higher frequencies. Consistent with this, we found that the frequency of synchronized spikes correlated with firing frequency of the participating neurons (Pearson’s coefficient: R = 0.74, P < 0.01). Alternatively, differences in entrainment between PNs in the moth and locust AL may be related to the strength of the inhibitory connection between PNs and LNs. Ito et al. (2009) found that in 54 a simple model of the moth AL, both the frequency of LFP oscillations and the degree of PN entrainment were dependent on the strength of the LN to PN synapse. Altering the strength of this connection converted the model from a “moth-like” response to a “locustlike” response. In models of coupled oscillators, coherence and entrainment of a neuron to the oscillation also depends on inhibitory connection strength (Breakspear et al. 2010). Either mechanism can explain how an oscillatory mechanism can synchronize spikes in the moth AL in the context of the weak entrainment observed in this system (Ito et al. 2009, Daly et al. 2011). This conceptual model suggests testable hypotheses about the difference between membrane and network properties in the ALs of two very different insects. For example, in models of cortical neurons, model cells can be switched from type 1 to type 2 oscillators by changing the density of ion channels (Zeberg et al. 2010). Such small changes in parameters may have been selected by the particular requirements each animal’s environment placed on its olfactory system, radically altering the encoding of odors (Martin et al. 2011). In previous reports, synchrony of spikes 0-5 ms apart was increased in response to the mixture of both components (Lei et al. 2002). Synchrony was most prevalent at the onset of response to an odor, and was positively correlated in an individual pair with the strength of a brief hyperpolarization (termed ‘I1’) that is characteristic of PNs in the moth AL (Lei et al. 2002; Christensen et al. 1996, Christensen et al. 1993; Christensen and Hildebrand 1997). We confirm that synchrony between 0 and 5 ms is increased in response to the pheromone mixtures of several ratios over an individual component alone. More precise synchrony (0–2 ms intervals) is increased in response to behaviorally 55 effective ratios (Figure 2.3.4A and B), and is not confined to the onset of the response (data not shown). A similar mechanism, however, may account for both findings. A large burst of inhibition (I1) at the onset of the response may initially align the intrinsic oscillations of PNs, producing a train of synchronous spikes. Throughout the response, oscillatory inhibition occasionally realigns PNs, increasing the precision of synchronous spikes. A model of ratio-selective synchrony in the AL We propose a model for ratio-dependent synchronization in the moth AL (Figure 2.3.7). This model is conceptual, and the data presented here motivates the structure of the proposed circuit, however several details are not based on findings in this study. We present this model as a tool for generating predictions for future investigations. In this model, three populations of local interneurons are driven by ORC input to a glomerulus (Figure 2.3.7A). The first population (LN1) receives only excitatory input from one population of ORCs, and its output reflects the concentration of the excitatory component of that population. The second LN population (LN2) is driven by input from one population of ORCs, and is inhibited by input from the LN1 population of the other glomerulus. The output of an LN2 is maximal for stimulation with its excitatory component alone, and decreases with the addition of increasing concentration of their inhibitory component in the mixture (Figure 2.3.7B). A third population of LNs (LN3) produces oscillatory activity in response to olfactory stimulation, either through reciprocal inhibitory connections (as observed in central pattern generators, reviewed in Selverston 2010) or through reciprocal connectivity with the PN population (Ito et al. 56 2009), although the independence of the LFP suggests the former mechanism. In this model, the output of the LN2 population does not directly control the activity of either the PNs or the LN3 oscillators, but controls the connection between them, possibly through shunting inhibition (Alger and Nicoll, 1979; Andersen et al., 1980) localized to the LNPN synapse. As depicted here, input from the LN2 population is at a minimum, and thus the effective strength of the LN3 oscillator on PNs is maximum, when input from the EEZ and BAL ORCs is equal (Figure 2.3.7B). The position of this minimum can be controlled by the relative strength of the excitatory and inhibitory inputs to the LN2 population, thus providing a mechanism for shifting the selectivity of PNs for particular ratios between components. A computational model with similar architecture has recently been proposed to explain ratio detection in the moth MGC (Zavada et al. 2011). Although in this model, the ratio of pheromone components modulates the firing rate of blendselective PNs resembling mPNs, the basic components (ratio-selective and singlecomponent specialist LNs, facilitation of ratio-selective response through disinhibition, dependence on a particular balance of synaptic weights) are comparable to our proposed model. Though the physiological details of this model are mostly untested, LNs in the moth AL exhibit both excitation and inhibition in response to odors (Reisenman et al. 2010). The 3 populations of LNs proposed may correspond to the observed categories of “restricted” LNs that connect specific glomeruli (LN1 and LN2) and “wide field” LNs that arborize throughout the AL (LN3) (Reisenman et al. 2011). We have depicted the oscillatory network as local and glomerulus-specific, reflecting the low coherence and 57 variable frequencies of simultaneously-recorded LFP oscillations in the AL (Christensen et al. 2003, Daly et al. 2011). Encoding via synchrony In this study, we found that firing rate of individual PNs in an MGC glomerulus reflected the concentration of their excitatory component, but the synchronous activity between pairs of PNs reflected the ratio between components in the blend. In effect, firing rate and synchrony are separate output channels carrying different information to higher-order centers of integration. This finding parallels results in this system (Christensen et al. 1998) and other systems (Meister 1996; Biederlack et al. 2006; Riehle et al. 1997; deCharms and Merzenich, 1996, Dan et al. 1998) and supports the concept of “multiplexed coding” in sensory systems (Panzeri et al. 2010). Friedrich et al. similarly observed that the minority synchronous, phase-locked spikes in the zebrafish OB carried information about the general odor category, i.e. patterns of synchronous spikes are similar in responses to monomolecular odors with similar molecular features, while the firing rate patterns of “residual,” non-synchronous spikes are informative about the identity of a monomolecular odor. As complex, natural odors can be characterized by dimensions including the identity of the components, their individual concentrations, and the relative proportions between them, so can the activity or silence of PNs, their firing rate, and the synchronous activity between them provide semi-independent dimensions for encoding information about an odor. Synchrony between PNs in the moth MGC encodes information about the relationship between features of an odor object, and thus resembles the “binding” of 58 features of visual objects by the synchronous activity between channels responding to each feature (Singer 1999). However, our results and those presented earlier (Lei et al. 2002) differ from the standard model of binding in that synchrony between neurons within a channel, instead of across channels, encodes information about the configuration of features in the odor object. Future work may explore whether this arrangement represents an additional dimension, separate from synchrony across channels, or a method of encoding relations between features when synchronizing across channels is undesirable or impossible. Population variability versus individual variability Finally, in interpreting these results, we rely on an assumption that is common in behavioral neuroscience: that the variability observed across several animals is equivalent to the variability that would be observed if a single animal was tested multiple times. This assumption is required both in interpreting the behavioral data, where animals are tested only once against a single stimulus, and in the physiological data, where a single pair of cells is recorded from each animal. In both types of experiments, the sampling strategy eliminates or reduces the interference of effects such as adaptation, habituation, and other types of learning that would alter responses to repeated tests, which is beneficial to the analysis. However, the possibility remains that pooling population data may under- or over-estimate individual variability. As in other studies (e.g. Schlyter et al. 2001 and references therein), we interpret the response window obtained from testing a population of individuals as the range of selectivity for blend ratios that an individual animal would exhibit. Similarly, we interpret the synchrony-based selectivity of pairs of neurons 59 recorded in different animals as a form of “range fractionation” (Cohen 1963) in which different units respond selectively to a range of values of the sensory stimulus. Though these assumptions may hold, other interpretations are possible. If individual animals exhibit variability in their preferred ratios (i.e. the peak of the response window is different for each individual), and corresponding variability in the ratio-selective activity in the AL, then the data presented here may reflect that individual variability. A recent finding in the field of pheromone traps for control of pest moths suggests that the latter interpretation may be possible. After long-term use of pheromone traps utilizing the most effective ratio of pheromone components, subsequent testing with traps using a range of ratios revealed the distribution was now bimodal: few moths were collected in the trap that was previously most effective, yet the number of moths collected in ratios outside of that range was unaffected (Yongjun Du, personal communication). That is, trapping at a particular ratio reduced the sub-population of males that preferred that ratio, and males that preferred other ratios survived and proliferated. This suggests that the “response window” we and others describe for a species may not simply reflect the variability of individual choice in a homogeneous population. Individuals may have a heritable response window over a subset of the response window for the population, and the variability we observed in the encoding of ratios in the AL may not reflect population coding, but rather this individual heterogeneity in the species. To resolve this issue, parallel or serial recordings from the AL of individual moths with known behavioral selectivity would be required. 60 We have demonstrated that the output of the AL of male Manduca encodes the behaviorally effective ratio between pheromone components in the degree of synchrony between PNs in the MGC. This work confirms our previous findings that synchrony in the AL encodes the innate attractiveness of odors (Lei et al. 2002, Riffell et al. 2009, Martin and Hildebrand 2010). Furthermore, this work adds to the growing understanding (Atick, 1992; Dusenberry, 1992; Rieke et al., 1995; Machens et al., 2001; Yu et al., 2005; Garcia-Lazaro et al., 2006) that neural circuits are adapted to the structure of stimuli important to an animal’s survival. 61 2.3.5 Tables Table 2.3.1 Stimuli used in behavioral experiments Mixture (BAL:EEZ) cyclohexane control 2:0.01 2:0.1 2:1 0.2:1 0.02:1 Serial Dilutions 10-1 (2:1) 10-2 (2:1) 10-3 (2:1) Concentration of component BAL EEZ 0 ng/µl 0 ng/µl 200 ng/µl 1 ng/µl 200 ng/µl 10 ng/µl 200 ng/µl 100 ng/µl 20 ng/µl 100 ng/µl 2 ng/µl 100 ng/µl tested n = 13 n = 12 n = 17 n = 29 n = 13 n = 14 20 ng/µl 2 ng/µl 0.2 ng/µl n = 12 n = 12 n = 12 10 ng/µl 1 ng/µl 0.1 ng/µl Moths 62 Mixture Component alone Table 2.3.2 Stimuli used in physiological experiments BAL Mixtures cyclohexane 0.02:0 0.2:0 2:0 20:0 200:0 0.02:1 0.2:1 2:1 20:1 200:1 Concentration of component BAL 0 ng/µl 0.2 ng/µl 2 ng/µl 20 ng/µl 200 ng/µl 2000 ng/µl 0.2 ng/µl 2 ng/µl 20 ng/µl 200 ng/µl 2000 ng/µl EEZ 0 ng/µl 0 ng/µl 0 ng/µl 0 ng/µl 0 ng/µl 0 ng/µl 10 ng/µl 10 ng/µl 10 ng/µl 10 ng/µl 10 ng/µl Concentration of component EEZ Mixtures cyclohexane 0:0.01 0:0.1 0:1 0:10 0:100 2:0.01 2:0.1 2:1 2:10 2:100 BAL 0 ng/µl 0 ng/µl 0 ng/µl 0 ng/µl 0 ng/µl 0 ng/µl 20 ng/µl 20 ng/µl 20 ng/µl 20 ng/µl 20 ng/µl EEZ 0 ng/µl 0.1 ng/µl 1 ng/µl 10 ng/µl 100 ng/µl 1000 ng/µl 0.1 ng/µl 1 ng/µl 10 ng/µl 100 ng/µl 1000 ng/µl 63 2.3.6 Figures Figure 2.3.1 Anatomy of the antennal lobe, and characterization of PN responses to stimuli. 64 Figure 2.3.1 Anatomy of the antennal lobe, and characterization of PN responses to stimuli. A. Schematic of the AL, illustrating input from two different populations of ORCs to the two largest glomeruli of the MGC, the cumulus and the toroid, inhibitory communication between these glomeruli via a network of LNs, and a pair of uniglomerular BAL-PNs innervating the toroid. B. Characteristic response pattern of a PN selective for BAL. The period of post-response inhibition (I2) is indicated in the first and third trace. 65 Figure 2.3.2 Behavioral responses of male moths to mixtures with various ratios of pheromone components. 66 Figure 2.3.2 Behavioral responses of male moths to mixtures with various ratios of pheromone components. A. Schematic of the wind tunnel used for behavioral experiments, with scored behaviors indicated along a simulated flight path: Upwind flight, Approach, Close hover, Contact, and Abdomen curl. The pheromone source is indicated by an asterisk. B. Frequency of scored responses to control (cyclohexane) and each of 5 mixtures with varied ratios between the two components. Bars with the same letter are not significantly different (at P <0.05, Fisher exact test). Differences among treatments: Upwind flight, Χ 2 = 3.133, P = 0.679; Approach, Χ 2 = 6.483, P = 0.262; Close hover, Χ 2 = 16.773, P = 0.005; Contact, Χ 2 = 16.181, P = 0.006; Abdomen curl, Χ 2 = 22.888, P <0.001. C. Behavioral responses to dilutions of the 2:1 mixture (BAL:EEZ). Behavioral frequencies were not different from each other (at P<0.05, Fisher exact test), nor was the difference among treatments significant (at P<0.05, Chi square test). D. EAG waveforms recorded in the wind tunnel evoked by the 2:1 (BAL:EEZ) mixture at a distance of 30 cm (grey traces), overlaid by the average EAGs recorded in the physiological rig evoked by 0.03, 0.3, 3, 30, and 300 ng of the 2:1 mixture (BAL:EEZ) (purple, blue, green, orange, and red traces). 67 Figure 2.3.3 Firing rate responses of individual PNs form a poor representation of the ratio between components in a pheromone mixture. 68 Figure 2.3.3 Firing rate responses of individual PNs form a poor representation of the ratio between components in a pheromone mixture. A. Peri-stimulus histograms of a representative PN selective for the EEZ component of the pheromone. Top: Responses to mixtures with increasing concentration of the EEZ (excitatory) component. Bottom: Responses to mixtures with increasing concentration of the BAL (inhibitory) component. B. Population averages of the mean instantaneous firing rate of individual PNs (BAL-PNs, top, n = 20; EEZ-PNs, bottom, n = 4) in response to mixtures with increasing proportion of the excitatory component (BAL, top; EEZ, bottom) alone (blue line) and in a mixture with a constant concentration of the inhibitory component (EEZ, top; BAL, bottom) (red line). Letters indicate means that were not significantly different (Tukey test, P <0.05, n = 21). The variance was significantly reduced in response to the 1X concentration of the excitatory component mixed with the inhibitory component relative to the excitatory component alone for both types of PNS (asterisk, Levene’s test, P<0.05). C. Population averages of the mean instantaneous firing rate of individual PNs (BAL-PNs, top, n = 20; EEZ-PNs, bottom, n = 4) in response to mixtures with increasing proportion of the inhibitory component (EEZ, top; BAL, bottom) (thick, black line) and the responses of a selection of individual PNs (thin, grey lines). The effect of the proportion of the inhibitory component was not significant (Friedman repeated measures ANOVA, P>0.05). D. Firing-rate ratios calculated between randomly paired PNs from the entire population responding to different concentrations of their excitatory component. The effect of the relative ratio between components was not significant (Friedman repeated measures ANOVA, P>0.05). 69 Figure 2.3.4 Synchrony between pairs of PNs is modulated by the ratio between pheromone components in a mixture. 70 Figure 2.3.4 Synchrony between pairs of PNs is modulated by the ratio between pheromone components in a mixture. A-B. Raw and shift-predictor subtracted crossinterval histograms for a representative PN pair selective for the EEZ component in response to mixtures with increasing concentration of the inhibitory (BAL) component. C-D. Raw and shift-predictor subtracted similarity index from the PN pair in (A) and (B). E. Synchrony responses of the PN pair in (A-D) to a series of pheromone component ratios at the maximum (black line) and minimum (grey line) selective kernel widths. F. Selectivity index for the similarity-based measure of synchrony between the PN pair in (A-D) (thick line) and a selection of 5 other PN pairs (thin, grey lines). 71 Figure 2.3.5 Synchrony across the recorded population of PNs is maximal in the behaviorally effective range of pheromone component ratios. 72 Figure 2.3.5 Synchrony across the recorded population of PNs is maximal in the behaviorally effective range of pheromone component ratios. The mean shiftpredictor subtracted similarity (±SEM) across all pairs of PNs exhibiting ratio-selectivity in response to mixtures in which either BAL (A) or EEZ (B) was altered (n = 12 pairs). 73 Figure 2.3.6 MGC PNs oscillate at a higher frequency than the LFP. 74 Figure 2.3.6 MGC PNs oscillate at a higher frequency than the LFP. A-B. Perievent spectrograms of a representative PN spiking response to the natural 2:1 mixture of pheromone components (A) and a representative LFP recorded in the MGC under identical conditions in separate animals (B). C. The autocorrelogram of the PN in (A), taken over a 1 second post-stimulus window. D. Schematic representation of phaseresetting of two independently oscillating PNs (dark and mid-grey lines) by input from an inhibitory network oscillating at a lower frequency (light grey line). 75 Figure 2.3.7 A possible network underlying ratio-selective synchrony in the MGC. Figure 2.3.7 A possible network underlying ratio-selective synchrony in the MGC. A. Schematic model of the proposed circuit. ORC input from populations selective for the BAL component (blue) or the EEZ component (red) synapse on PNs (in grey) that internally generate an oscillatory spiking output. ORCs also synapse on three populations of LNs: LN1s, which respond selectively to one component; LN2s, which are also 76 inhibited by LN1s; and LN3s, which generate the observed LFP through reciprocal interconnections. Inhibitory input from the LN2 population modulates the effective connectivity between LN3 and the PN population. B. Idealized responses of the LN2 and LN3 populations to mixtures in which the proportion of BAL (left) or EEZ (right) is altered. 77 3. Conclusions 3.1 Summary of results and future directions This thesis investigated how the form and function of the olfactory system of insects is adapted to process information crucial to the survival and reproduction of the animal. From the expression of receptor proteins, to the first steps of sensory processing in the AL, to the integration across modalities and the organization of behavior in higherorder centers, the particular mechanisms used by an animal’s nervous system have been shaped by that animal’s place in the natural world, and understanding that place facilitates research into the principles of neurobiology. In this concluding section, I review the major arguments and findings of the thesis, highlight connections between the sections, and suggest the future direction of research motivated by this work. 3.1.1 Olfactory mechanisms of species-specific behavior In the first section, we argue that the diversity of insect olfactory systems available to neurobiologists is a boon to the field, providing a series of natural experiments that can test hypotheses about the role of particular neural mechanisms in the olfactory system. A few examples from this review highlight the utility of this approach. At the peripheral level (Appendix A, Section 4) we review evidence olfactory receptor sequences are highly conserved between closely-related species, with speciation dependent on small changes in receptor specificity, the abundance or sensitivity of a particular receptor, or the glomerular targets of receptors. We argue that this provides evidence that olfactory receptors comprise a “basis set” (Pouget and Sejnowski, 1997), a set of coding units that can be combined to produce a unique code for any of the odors 78 with importance to all species in a closely-related group. The majority of olfactory receptors are therefore preserved between these species, while divergence through, for example, specialization on a food source is acquired through increased sensitivity or selectivity of one or a few receptors in the set. I expand in more detail on this concept in the second section, in which I review the evidence that changes in receptor sensitivity, selectivity, and the target glomeruli of pheromone ORCs underlies reproductive isolation of species in moths. This reduced system of 2-4 ORCs and their target glomeruli furthermore provides evidence that the AL network in the MGC constitutes a patternrecognition unit that responds to patterns of input from ORCs. Altering the activity of ORCs or their connection to the MGC glomeruli produces predictable changes in the moth’s selectivity for the proportion of components in pheromone blends released by females: when the response of an ORC to a component is increases, males are attracted to blends with a lower proportion of that component; when the target glomerulus of ORCs sensitive for the major and minor components is switched, males are selective for pheromone blends in which the major component is now the minor, and vice-versa. While the sensitivity and abundance of a particular ORC is positively correlated with the proportion of that component in the natural mixture (see Appendix B), these data suggest that the features of the AL network determine the attractive proportion of components in the mixture and therefore which is the major or the minor component. In the third section of the thesis, I investigate how these ratios between components are encoded in the AL via the synchronous output of MGC PNs. In future studies, this coding mechanism can be investigated in closely-related species. I hypothesize that PNs in these species will exhibit 79 synchronous output to the ratio between components in a pheromone blend attractive to that species. Extrapolating from the proposed model for a network that processes the ratio between pheromone components (Figure 2.3.7) I hypothesize that the difference between the networks in these species will be found in the strength of certain synapses in the network. By altering the strength of the ORC to LN1 and LN2 synapses, the point at which inhibition overtakes excitation in LN2 neurons in this model shifts, changing the component ratio at which the LN3 input to PNs is disinhibited and thus can facilitate synchrony between PNs. A similar effect could also be produced in this model by increasing the sensitivity or abundance of ORCs. The data in section 2.3 therefore suggests testable hypotheses about how small changes in the set of ORCs common between species, or the AL mechanisms into which they feed, can produce speciesspecific olfactory behavior. Direct utilization of the comparative approach we propose in Section 2.1 can expand and strengthen the results presented in this thesis. 3.1.2 Synchrony in olfactory coding In Section 2.1 and Appendix A, we review the diversity in the anatomy of the central olfactory pathways in insects, and the connections between anatomy and the mechanisms of information processing and coding in the olfactory system. Of particular importance to the other sections of this thesis is the discussion of synchrony in the AL (Appendix A, Section 6.3). The initial paradigm for the encoding of odors via synchronous PN output came from studies in the AL of locusts. In this animal, PN spikes are phase-locked to the oscillation of the AL network, and synchronous spikes occur only at the frequency of the LFP. In several other insects studied, synchronous PN spikes are 80 not phase-locked to the network oscillations. We argue that these differences arise from two related sources. The proximate cause is the different anatomy of the AL: locusts have an aglomerular AL, LNs that do not produce spikes, and a divergence of ORCs onto multiple populations of PNs, while the ALs of most other species studied have distinct glomeruli, LNs that spike, and ORCs expressing a particular receptor synapse onto PNs in a single glomerulus. We propose that ultimately, the difference is attributable to the olfactory environment and behavior of these animals. Locusts are adapted to feed from many diverse plants and integrate information about the relative value of each, and are afforded long, continuous contact with odors while feeding. Moths and other insects, however, typically feed from fewer food sources (bees, we argue, can be considered serial specialists in this regard, who seek out one type of flower while it remains rewarding), and rely on brief, stochastic encounters with odors in a plume while finding food. In the locust, therefore, the oscillatory mechanism has developed to take advantage of the large encoding space afforded by evolving patterns of synchrony. In section 2.2, I argue that synchrony in the moth AL, and perhaps in other species, is utilized to encode another, specific dimension of odors: the configuration of a complex odor that signifies an innately attractive odor source. Finally, I test this hypothesis in Section 2.3, where I demonstrated that synchrony specifically encodes the innately attractive ratio between components in the pheromone blend. This mechanism of encoding via synchrony is adapted in these two very different species for two encoding schemes that suit the requirements for survival and reproduction in each animal. I further argue, however, that the mechanism for generation of synchrony in these two animals is likely not very 81 different, and that small changes in the physiology of the AL can produce the phaselocked, oscillatory synchrony observed in locust or the non-oscillatory synchrony observed in moths. The comparative approach to this problem again generates testable hypotheses to further our understanding of sensory coding. 3.1.3 Ratio coding in the main AL The results obtained in Section 2.3 of this thesis relied on the many benefits of research in the pheromone system of the moth: A simple, two-component odor, processed by a reduced system of two glomeruli, innervated by highly-specific ORCs, with reciprocal inhibition between them. However, moths and other insects exhibit innate attraction to floral odors (Raguso and Willis 2002; Riffell et al. 2009a, 2009b; Riffell 2011) and to the leaf volatiles of host plants (reviewed in Bruce et al. 2005). These odors are typically more complex, with many more components and therefore many more relative proportions. ORCs that respond to plant odors are also less selective, responding to many volatiles with varying sensitivity to each (de Bruyne et al. 2001, Hallem and Carlson 2006, Shields and Hildebrand 2000). The main, sexually isomorphic AL in moths and other insects contains many more glomeruli, with a heterogeneous LN population connecting them (Olsen et al. 2007; Olsen and Wilson 2008; Reisenman et al. 2008; Chou et al. 2010). However, attraction to floral odors is dependent on the character of the mixture, not on single volatiles (Riffell et al. 2009a) and is apparently dependent on the ratio of components (Bruce et al. 2005; Tasin et al., 2006a; Visser, 1986). Finally, future research in the plant-odor processing system of the main AL can test an important hypothesis: does synchrony between PNs also encode the salience of an odor with a 82 learned association? Although Manduca sexta exhibits strong, innate attraction to the floral odor of one of the host plants in its range (Datura wrightii, ‘Datura’) (Raguso and Willis, 2002), when the flowers of that plant are scarce, the animals can learn to utilize other flowers (Riffell et al. 2008). Future work will address whether the ratio between components of a floral odor, whether innately attractive or learned, are similarly encoded via synchrony in the output of the main AL. In preliminary work (Appendix C), I have begun to address this question. I have collected and analyzed the floral volatiles from 103 flowers of Datura, to characterize the range of variability of each component. I then produced a synthetic mixture that contains 7 of the physiologically effective components (Riffell et al. 2009a) of the Datura floral odor, including the 3 components required for attraction to the odor source (Riffell et al. 2009b), in proportions matching the mean percentage of the components in the natural floral odor as verified by gas chromatography. I have also developed a method of identifying units recorded on a multi-channel electrode as PNs or LNs, based on the characteristics of their unstimulated, background firing (Appendix C, Figure 1). Using this method, preliminary results suggest that PNs encode the ratio of components of the Datura floral odor relative to the whole mixture. An example main AL PN responds in a dose-dependent manner to methyl salicylate alone, similar to the response of a MGC PN to its excitatory component (Figure 2.3.3B). The mean instantaneous firing rate of this PN (Appendix C, Figure 2A) is maximal for the mixture in which methyl salicylate is present in the proportion it is found in the natural Datura odor. This stands in contrast to the MGC PN data, which shows no significant effect of 83 changing ratios on firing rate (Figure 2.3.3B and C). The degree of synchrony between another pair of PNs recorded simultaneously (Appendix C, Figure 2B) is maximal in response to a mixture in which benzaldehyde is present in the naturally-occurring proportion, although this pair did not respond to benzaldehyde alone. The response of this pair resembles the selectivity of synchronous activity in MGC PNs for particular ratios of pheromone components (Figure 2.3.5). Future research will attempt to characterize the encoding of floral odor component ratios in the main AL by PNs that respond to multiple components of the mixture. The increased complexity of this sub-system offers an intriguing counterpart to the MGC in the same animal. Furthermore, as learning has been demonstrated to induce changes in the pattern of synchronous PNs (Daly et al. 2004), this research can be extended to test whether ratio-selectivity of synchronous spikes is also induced when a new odor is associated with reward. These studies benefit from a comparative approach, in this case between olfactory sub-systems in the same animal that are adapted to process different olfactory information, and are well-grounded in the natural behavior of the animal. 3.1.4 Final thoughts Finally, the progression of analysis in this thesis from the broad, evolutionary and ecological context of a sensory-guided behavior, to the behavioral analysis of individuals in the laboratory, to the search for a neural code underlying the behavior, owes much to the growing understanding in the field of neuroscience of the connection between an animal’s brain and the environment in which it exists. The concept of “efficient 84 encoding” (Attneave 1954; Barlow 1961, 2001) encapsulates the observation that brains are not universal computers, but are constrained by the energy required for computation (Laughlin, 2001; Niven and Laughlin 2008) to encode the stimuli that an animal is most likely to encounter in the natural world (Atick 1992). Although this concept has been advanced mostly in research into coding in visual systems (c.f. Simoncelli 2003; Simoncelli and Olshausen 2001; Wark et al. 2007), it is conceptually relevant to the olfactory research presented here. The MGC of the male moth is a form of “matched filter” (Wehner 1987) for the ratio of the conspecific female: the synchrony output does not faithfully encode the exact input, but preferentially responds to the configuration that is relevant to the animal. This work on olfactory neurobiology relied on previous work in this lab and others that characterized the natural moth pheromone, and the role this stimulus plays in the isolation of species in their natural environment. The recognition of the interplay between research into natural stimuli, and the brain mechanisms to process and respond to those stimuli has long been the defining feature of the field of neuroethology (Hoyle 1970, 1974; Ewert 1980; Roeder 1963; Bullock 1990). 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In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright 107 Reprinted from Progress in Neurobiology, Vol. 95, Martin, J. P., Beyerlein, A., Dacks, A. M., Reisenman, C. E., Riffell, J. A., Lei, H., Hildebrand, J. G., 427-47, 2011, with permission from Elsevier Author's personal copy 108 Progress in Neurobiology 95 (2011) 427–447 Contents lists available at SciVerse ScienceDirect Progress in Neurobiology journal homepage: www.elsevier.com/locate/pneurobio The neurobiology of insect olfaction: Sensory processing in a comparative context Joshua P. Martin *, Aaron Beyerlein, Andrew M. Dacks 1, Carolina E. Reisenman, Jeffrey A. Riffell 2, Hong Lei, John G. Hildebrand Department of Neuroscience, College of Science, University of Arizona, 1040 East Fourth Street, Tucson, AZ 85721-0077, USA A R T I C L E I N F O A B S T R A C T Article history: Received 5 June 2011 Received in revised form 10 September 2011 Accepted 19 September 2011 Available online 24 September 2011 The simplicity and accessibility of the olfactory systems of insects underlie a body of research essential to understanding not only olfactory function but also general principles of sensory processing. As insect olfactory neurobiology takes advantage of a variety of species separated by millions of years of evolution, the ﬁeld naturally has yielded some conﬂicting results. Far from impeding progress, the varieties of insect olfactory systems reﬂect the various natural histories, adaptations to speciﬁc environments, and the roles olfaction plays in the life of the species studied. We review current ﬁndings in insect olfactory neurobiology, with special attention to differences among species. We begin by describing the olfactory environments and olfactory-based behaviors of insects, as these form the context in which neurobiological ﬁndings are interpreted. Next, we review recent work describing changes in olfactory systems as adaptations to new environments or behaviors promoting speciation. We proceed to discuss variations on the basic anatomy of the antennal (olfactory) lobe of the brain and higher-order olfactory centers. Finally, we describe features of olfactory information processing including gain control, transformation between input and output by operations such as broadening and sharpening of tuning curves, the role of spiking synchrony in the antennal lobe, and the encoding of temporal features of encounters with an odor plume. In each section, we draw connections between particular features of the olfactory neurobiology of a species and the animal’s life history. We propose that this perspective is beneﬁcial for insect olfactory neurobiology in particular and sensory neurobiology in general. ß 2011 Elsevier Ltd. All rights reserved. Keywords: Olfaction Neuroethology Comparative anatomy Antennal lobe Protocerebrum Sensory coding Contents 1. 2. 3. 4. 5. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olfactory world of an insect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chemical composition of natural olfactory stimuli . . . . . . . . 2.1. Spatio-temporal structure of olfactory stimuli . . . . . . . . . . . 2.2. Olfaction-based behavior of insects . . . . . . . . . . . . . . . . . . . . . . . . . Evolution and speciation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Evolution of olfactory receptors . . . . . . . . . . . . . . . . . . . . . . . Olfactory specialization and speciation . . . . . . . . . . . . . . . . . 4.2. 4.2.1. Peripheral adaptations for olfactory specialization Central adaptations for olfactory specialization . . . 4.2.2. Central olfactory pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antennal lobe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. 5.1.1. Inhomogeneous interactions between glomeruli . . Higher-order olfactory centers . . . . . . . . . . . . . . . . . . . . . . . . 5.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 429 429 429 430 431 431 431 432 432 432 432 433 435 Abbreviations: OR, olfactory receptor protein; ORC, olfactory receptor cell; Orco, olfactory coreceptor; AL, antennal lobe; LN, local interneuron; PN, projection neuron; OBP, odorant-binding protein; MGC, macroglomerular complex; MB, mushroom body; LH, lateral horn of the protocerebrum; APT, antenno-protocerebral tract; MBC, mushroom body calyx; LH, lateral horn; KC, kenyon cell; LAL, lateral accessory lobe; LFP, local ﬁeld potential; AC, antennal commissure; ILPC, inferior lateral protocerebrum. * Corresponding author. Tel.: +1 520 621 6643; fax: +1 520 621 8282. E-mail address: [email protected] (J.P. Martin). 1 Present address: Department of Neuroscience, Mount Sinai School of Medicine, New York, NY 10029, USA. 2 Present address: Department of Biology, University of Washington, Seattle, WA 98195, USA. 0301-0082/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.pneurobio.2011.09.007 Author's personal copy 109 428 6. 7. J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 5.2.1. Antenno-protocerebral tracts . . . . . . . . . . . . . Mushroom-body calyx. . . . . . . . . . . . . . . . . . . 5.2.2. Lateral horn . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3. Protocerebrum and beyond. . . . . . . . . . . . . . . 5.2.4. Comparative coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gain control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Tuning/coding transformation . . . . . . . . . . . . . . . . . . . . 6.2. Sharpening . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1. 6.2.2. Broadening . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sharpening versus broadening in two species 6.2.3. Synchrony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. 6.4. Encoding temporal features of olfactory stimuli . . . . . . Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction Olfaction plays a central role in insect behaviors of interest to humans. Feeding and reproduction of insect pests, pollinators, and vectors of disease are strongly regulated by the volatile organic compounds (hereinafter ‘‘volatiles’’) an insect encounters. The toll of malaria and other diseases mediated by insect vectors, famine attributable to insect crop pests, and the alarming collapse of a major pollinator species (honey bees) impels investigations into the neurobiology underlying the sense of smell in insects (van Naters and Carlson, 2006). As a topic of study in neurobiology per se, olfaction shares the beneﬁts of all neurobiological research in insects and other invertebrates: reduced numerical complexity of the nervous system, the corresponding advantages of identiﬁable nerve cells (Comer and Robertson, 2001) and neuropil structures, and a strong connection between neurobiology and behavior. The shared principles of organization and function of olfactory systems in invertebrates and vertebrates (Ache and Young, 2005; Hildebrand and Shepherd, 1997; Kaupp, 2010) make discoveries in insects of general interest to researchers studying other animals. In this review, we argue that the diversity of insect species provides another beneﬁt for the study of olfactory neurobiology. Insects are believed to be the most speciose and diverse class of animals on earth: approximately one million species are currently described (and many more are expected to be found) in 30 orders, separated by more than 400 million years of evolution (Grimaldi and Engel, 2005 – see Fig. 1), adapted to more environments and niches, and correspondingly expressing more diverse behaviors, than any other closely related group of animals. Most insect behavior relies to some degree on chemosensory information. Research on the olfactory neurobiology of insects has focused on ﬂies, cockroaches, honey bees, moths, and locusts, as well as other representative species from at least seven orders spanning holometabola and hemimetabola (respectively, insect taxa that undergo complete or incomplete metamorphosis – see Fig. 1). Converging evidence from multiple species maps out the relatively simple, elegant structure of a ‘canonical’ insect olfactory system. A single type of olfactory receptor protein (OR) with characteristic afﬁnity for volatiles is expressed in a subset of the olfactory receptor cells (ORCs) housed in a head appendage (e.g. antenna or palp). The axons of all ORCs expressing the same receptor converge in a single glomerulus within the antennal lobe (AL), processing and propagating olfactory information through the ﬁrst level of processing in the CNS. Glomeruli interact through a population of local interneurons (LNs), many or most of which are inhibitory, that shape the output of the AL conveyed by projection neurons (PNs) to down-stream olfactory foci elsewhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 435 436 437 437 437 439 439 439 439 440 441 442 442 442 in the brain. At subsequent stages of neural processing, glomerular output is integrated, and olfactory information is formatted for memory and association with other modalities and ultimately drives or modulates the activity of circuits that control behavior. For nearly every one of these canonical principles of structure and function of insect olfactory systems, an exception can be found among insect species. Some ORCs express multiple receptors (Benton et al., 2009; Couto et al., 2005; Fishilevich and Vosshall, 2005; Vosshall et al., 2000), and in a few species ORC axons terminate within multiple, overlapping sets of glomeruli (Anton and Hansson, 1996). Glomeruli in the honey-bee AL interact through a network of histaminergic local neurons not found in insects outside of the Hymenoptera (Dacks et al., 2010). Glomeruli may excite rather than inhibit their neighbors, partially decoupling the output of the AL from the input patterns of ORCs (Olsen et al., 2007). Output from the AL to higher-order centers follows nonhomologous pathways in different species, with an underlying organization that almost certainly has consequences for olfactory processing (Galizia and Rössler, 2010). Far from impeding investigations into the neurobiology of olfaction, the diversity of insect models offers a suite of ‘‘natural experiments’’ wherein olfactory systems have evolved to perform efﬁciently the tasks most crucial to the animal’s survival. Natural behavior reveals how an animal uses olfactory information: what is emphasized, what is ﬁltered out, and what is integrated or extracted from the background. Insects present perhaps the greatest range of behaviors mediated by volatile chemicals. Included in this single class are species that use olfaction very little or perhaps not at all and have correspondingly reduced brain structures (c.f. Strausfeld et al., 1998), species highly specialized on a plant or animal food source or host (c.f. Dekker et al., 2006), and species with remarkable abilities to associate new food resources with corresponding olfactory cues (c.f. Dukas, 2008; Giurfa, 2007). Correlating unique or exaggerated olfactory behavior with unique or exaggerated features of olfactory systems can be a powerful tool. Insects offer exceptionally fertile grounds for such a comparative approach. In this review, we consider the remarkable progress of research on the anatomy, physiology, and information processing in the olfactory systems of insects in a comparative context. We begin with a discussion of the quality and temporal structure of olfactory stimuli important to various insects (Section 2). We survey the olfaction-based behaviors exhibited by insects in their natural environments, especially those that demonstrate the challenges faced by an olfactory system (Section 3). Next we examine recent evidence of how the OR repertoire is shaped by both the salience of volatiles in an insect’s ecological niche and the roles of those compounds in the insect’s evolutionary history (Section 4). Author's personal copy 110 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 429 Fig. 1. The phylogeny of the neopteran insects (after Grimaldi and Engel, 2005), highlighting orders most commonly studied in olfactory neurobiology. The four orders most commonly used in studies of central olfactory neurobiology (Orthoptera, Hymenoptera, Diptera, and Lepidoptera) are highlighted in large font, and the lineage connecting them is bolded in the phylogenetic tree. Three additional orders, less commonly studied but nonetheless important, are highlighted in bold (Blattaria, Hemiptera, and Coleoptera). Numbers along the timeline indicate deep time events for reference (Grimaldi and Engel, 2005; Williams et al., 2010). Model animals from each of the four most commonly used orders are depicted in the inset photos: (A) S. americana (‘‘locust’’) (B) A. mellifera (‘‘honey-bee’’) (C) D. melanogaster (‘‘fruit ﬂy’’) and (D) M. sexta (‘‘hawkmoth’’). Photos: Charles Hedgcock. Information is passed and processed from ORCs, through the primary olfactory center (the AL), to higher-order foci in the protocerebrum. We highlight idiosyncrasies in structure and function of these areas and the insights about olfactory processing they provide (Section 5). In the ﬁnal section (Section 6), we suggest that insects employ neural codes for olfactory stimuli and processing mechanisms that reﬂect the demands presented both by their environment and by the structure and function of the neural systems that produce those codes. 2. Olfactory world of an insect Natural, behaviorally signiﬁcant olfactory stimuli typically are mixtures of volatiles whose concentrations co-vary dynamically in time and space. The volatiles released by conspeciﬁcs, food sources, and oviposition hosts are chemically diverse, and mixtures often contain compounds unique to their source, as well as volatiles common to the chemical signature of multiple sources (c.f. Bruce et al., 2005; Raguso, 2008). The olfactory world is also an arena of constant movement and ﬂux. Once emitted by a source, volatiles are dispersed, mixed, and diluted by the ambient motion of air to form a shifting and ﬁlamentous plume. In this section, we review new insights into the physico-chemical properties of selected volatiles that have known roles in insect behavior. Identiﬁcation and description of behaviorally relevant volatiles as they are received by insects establish additional parameters for investigation of olfactory neurobiology. 2.1. Chemical composition of natural olfactory stimuli Contemporary analytical technology has revealed considerable chemical diversity in the olfactory world of insects. Analytical tools are particularly powerful when directly combined with neurophysiological (e.g. Ghaninia et al., 2008) or behavioral (e.g. Allmann and Baldwin, 2010; Turlings et al., 2004) assays to identify effective volatiles in a complex mixture released by a source. These studies also reveal commonality among olfactory stimuli. For example, olfactory systems of phytophagous insects across many orders respond to many of the same, ubiquitous plant volatiles despite biases toward different hosts, suggesting that complex, higher-order properties of olfactory stimuli are necessary for volatile source identiﬁcation (Bruce et al., 2005; Raguso, 2008). 2.2. Spatio-temporal structure of olfactory stimuli Immediately following their emission from a source, volatiles become subject to the physical forces of moving air that impose structure on them. At spatial scales greater than 1 cm the olfactory environment is dynamic. Both ﬂuid-dynamic analysis and analytic technologies have shown that a plume of volatiles is not a uniform concentration gradient but rather a ﬁlamentous and discontinuous structure containing regions of volatile-laden air interspersed with regions of volatile-free air (Murlis and Jones, 1981; Riffell et al., 2008a). Insect olfactory systems apparently have evolved to process the resulting intermittent olfactory Author's personal copy 111 430 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 stimuli, and similarities in behavior and neural processing are noteworthy in phylogenetically distant species (Budick and Dickinson, 2006; Cardé and Willis, 2008). The ability of many insects to track a discontinuous olfactory stimulus and locate its unseen source is based on a common search strategy comprising alternating crosswind ‘casts’ to locate volatiles, and upwind ‘surges’ following contact with volatiles (Murlis et al., 1992). In searching for the source of behaviorally signiﬁcant volatiles, moths and ﬂies use this strategy over landscape scales (Reynolds and Frye, 2007; Reynolds et al., 2007), and their ﬂight paths resemble a mathematically optimal search strategy (Reynolds, 2005) for following a plume of volatiles. Insects rely on the information contained in plume structure to varying degrees when searching for a source: in an artiﬁcially homogeneous plume that lacks spatio-temporal structure, ﬂies navigate upwind to the source (Budick and Dickinson, 2006), but many moths are unsuccessful (Willis and Baker, 1984). In addition, the temporal patterns in which insects encounter volatiles are affected by the size and velocity of the animal (Koehl, 2001, 2006), active interaction with the plume by wing beating (Loudon and Koehl, 2000; Sane, 2006; Sane and Jacobson, 2006), and antennal ﬂicking (Hillier and Vickers, 2004; Nishiyama et al., 2007; see also Dethier, 1987). Olfactory stimuli that are important for insects thus have chemical and temporal characteristics particular to each species. Investigations in olfactory neurobiology should consider these features in designing and interpreting experiments. 3. Olfaction-based behavior of insects A goal of efforts to understand the olfactory neurobiology of insects is to analyze neural mechanisms that underlie olfactionmodulated behavior. In addition, a clear description of the problems an olfactory system must solve in the natural world provides the parameters for investigations into neural processing of olfactory information. In general, olfaction-based behavior critical for survival and reproduction ﬁrst involves recognition (either innate or learned) of a signiﬁcant olfactory stimulus. Subsequent processing takes into account the context of the sensation, both the external environment and the internal state of the animal. Much has been gained by studying the innate responses of insects to olfactory cues. Heritable, stereotyped, robust behavioral output, and the consistent neural architecture that underlies it, are hallmarks of insect neurobiology and of olfactory neurobiology in particular. The pheromonal signals that guide many insects to conspeciﬁc mating partners have been studied most thoroughly, and recent work has continued to expand on the complexity and diversity of these signals. The chemical identity of insect pheromones can be as simple as the monomolecular compound 11-cis-vaccenyl acetate, an apparently multifunctional pheromone that mediates aggregation in Drosophila melanogaster (Bartelt et al., 1985; Wertheim et al., 2002), indicates to males the mating state of a female (Ejima et al., 2007; Ha and Smith, 2006) and increases female receptiveness to male courtship attempts (Kurtovic et al., 2007), or as complex as the mixtures of sex-pheromone components released by some adult female moths (Baker, 2008; Vickers et al., 1998) and beetles (Leal, 1996; Yuko and Walter, 2008). The Hymenoptera employ perhaps the greatest variety of social olfactory signals studied thus far (Le Conte and Hefetz, 2008; Slessor et al., 2005). In honey bees, for example, recent work has elaborated how pheromones organize the defense of the hive (Hunt, 2007), recruit foragers (Thom et al., 2007), and reinforce the primacy of the queen (Strauss et al., 2008; Vergoz et al., 2007). The diversity of volatile semiochemicals and cuticular hydrocarbons used by ants (Hymenoptera: Formicidae), and their roles in behaviors ranging from recognition and navigation to alarm and courtship remain an active area of study (c.f. Endler et al., 2004; recently reviewed in Hölldobler and Wilson, 2009). Recent advances in the ﬁeld have extended to the neuroanatomy (Zube et al., 2008) and neurophysiology (Yamagata and Mizunami, 2010) of the olfactory system in ants. Evidence of cues for intraspeciﬁc communication in other insects, such as stimuli described as stress-, aggregation-, and sex-related pheromones in D. melanogaster (Bartelt et al., 1985; Ejima et al., 2007; Suh et al., 2007, 2004), has been accumulating. Two common threads emerge: these signals often are complex mixtures of volatiles, with multiple physical and behavioral effects (Ferveur, 2005; Siwicki et al., 2005; Slessor et al., 1990), and processing of the stimuli may be segregated anatomically (Jefferis et al., 2007; Seki et al., 2005) or by the expression of common genes, as in the case of D. melanogaster stress (Suh et al., 2004) and sex pheromones (Manoli et al., 2005). Finally, insects are innately attracted to the complex odors of certain host animals (e.g. Zwiebel and Takken, 2004) or plants (e.g. Raguso and Willis, 2002), suggesting that foraging, like social behavior, relies on heritable responses to olfactory stimuli. Insects also can learn associations between volatile stimuli and rewards in the natural environment. These abilities often are related to the demands of the animal’s environment, e.g. the diversity of food sources. Honey bees can learn the scent of ﬂowers that are proﬁtable to visit (i.e. are yielding nectar and/or pollen) at any time, either through direct experience (Chittka and Raine, 2006) or by exposure to the volatiles associated with nectar or pollen transferred to prospective foragers by returning bees during the waggle-dance (Farina et al., 2007; Gil and De Marco, 2005). Locusts are voracious, generalist feeders, with a robust ability to associate olfactory stimuli with the quantity and nutritional quality of food sources (Behmer et al., 2005). The sphinx moth Manduca sexta exhibits an innate attraction to, and preference for, certain host ﬂowers but can learn to feed from others when the preferred ﬂowers are scarce (Riffell et al., 2008b). Finally, in two related species of parasitic wasps, the ability to associate volatiles with the presence of host caterpillars correlates with the behavioral plasticity demanded by the distribution of their respective host species (Bleeker et al., 2006; Smid et al., 2007). Although the particular form and capacity for olfactory learning vary among the insects, the beneﬁts for growth (Dukas and Bernays, 2000) and mating success (Dukas, 2005) suggest that learning is widespread. Whether an olfaction-based behavior is innate or learned, its expression may be affected by the context in which the olfactory stimulus is experienced. A single volatile often has little effect on insect behavior when detected in isolation but gains behavioral signiﬁcance in the context of other volatiles. In response to herbivory by the sawﬂy Diprion pini, the Scots pine tree emits elevated quantities of the terpenoid (E)-b-farnesene (Mumm and Hilker, 2005). While neither (E)-b-farnesene nor the other headspace volatiles of the Scots pine tree are attractive to parasitoids of D. pini, mixtures of (E)-b-farnesene with other head-space volatiles are highly attractive to the parasitic wasp Chrysonotomyia ruforum (Mumm and Hilker, 2005). Volatiles also may elicit different behaviors in different contexts. For example, 11-cisvaccenyl acetate acts as an aggregation pheromone for D. melanogaster in the context of plant volatiles (Bartelt et al., 1985) and inhibits mating in the context of other males or mated females (Jallon et al., 1981). Olfactory stimuli can elicit different, even antagonistic responses based on the internal physiological state of an individual insect. Many factors, such as age, time in the circadian cycle, and feeding and mating status, may change the salience or association of stimuli that are important for the survival of an individual insect. Thus the nervous system must temper its sensitivity, resolution, or precision to suit the needs of the individual based on the current Author's personal copy 112 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 431 Insects have evolved richly diverse olfaction-based behaviors as adaptations to diverse ecological challenges and opportunities and exhibit correspondingly diverse olfactory structures and mechanisms. A ﬁrst step toward understanding the differences in the olfactory neurobiology of different insect taxa is to question how these differences may have arisen through evolution. Recent work in closely related species has described changes in olfactory processing that accompany speciation, and a picture of olfactory system evolution in insects is emerging. From this common point, the OR repertoire of insects diverges. Intraspecies OR sequences are very diverse. The amino-acid sequences of ORs in D. melanogaster are only 15% similar on average (Robertson et al., 2003), illustrating the diversity of ORs needed to detect and discriminate a wide variety of volatiles. The response of an ORC expressing one of these ORs, processed through synaptic circuitry in the olfactory pathways of the CNS, forms the basis for encoding all relevant volatile stimuli in the animal’s environment. How OR tuning in animals with widely different olfactory environments has evolved is still poorly understood, but much progress has been made in describing how the OR repertoire of species within a group diverges. An insect’s ORs are products of evolutionary pressures and processes that enhance, remove, or alter receptor-protein expression in ORCs. The growing number of genomes available for species in the genus Drosophila provides the best example of this process. Odorant-binding proteins (OBPs; Vieira et al., 2007) and ORs (Nozawa and Nei, 2007) are subject to ‘‘birth and death’’ evolution, wherein new genes are produced through duplication and modiﬁcation, and some genes ‘‘die off’’ by deletion or mutation to pseudogenes. Certain species of fruit ﬂies in the genus Drosophila, however, exhibit remarkable conservation of orthologous OR groups, sharing 22–79% sequence similarity (Guo and Kim, 2007). Many of the differences between orthologous ORs are attributable to synonymous substitution, suggesting that the function of these ORs is conserved between groups. In fact, the molecular receptive ranges (olfactory ‘‘tuning’’) of ORCs in the large basiconic sensilla of several Drosophila species appear to be nearly identical (Stensmyr et al., 2003). In comparison, while the mosquito species A. gambiae and Aedes aegypti share 21 orthologous ORs, only one of these is shared with D. melanogaster (Bohbot et al., 2007). Based on these ﬁndings, Guo and Kim (2007) proposed that the ORs conserved between species constitute a ‘‘basis set’’ (Pouget and Sejnowski, 1997), in analogy with the three cone receptors for color vision. A basis set is a set of coding units that, together, can be combined to produce a unique code for any olfactory stimulus ‘object’ an animal may encounter. Although we make no formal claim that ORs ﬁt all of the requirements of a basis set, we note that coding schemes involving true basis functions have been proposed for olfaction (Hopﬁeld, 1995). 4.1. Evolution of olfactory receptors 4.2. Olfactory specialization and speciation Insect ORs appear to be functionally and genetically distinct from those discovered in other taxa (Touhara and Vosshall, 2009). The topology of insect ORs is inverted within the cell membrane with respect to the N-terminal orientation of the classical Gprotein-coupled receptor structure exhibited by mammalian ORs. Binding of volatile molecules by insect ORs is accomplished by a heterodimeric complex of one ‘typical’ insect OR and one ‘common’ receptor expressed in nearly all ORCs, designated Or83b in D. melanogaster (Benton et al., 2006). Or83b and its orthologues, hereafter ‘‘Orco’’ (after Vosshall and Hansson, 2011), are necessary for both ORC function (Jones et al., 2005; Larsson et al., 2004) and trafﬁcking of ORs to and insertion in the membrane of ORC cilia. Recent work has suggested that this heterodimeric complex itself serves as an ion channel that might be both ligand- and cyclicnucleotide-activated (Nakagawa and Vosshall, 2009; Sato et al., 2008; Wicher et al., 2008). Both the heterodimeric pairing of an insect OR and an Orco protein, and the sequence of the Orco gene, are highly conserved among insects studied to date (Jones et al., 2005; Krieger et al., 2003; Melo et al., 2004; Nakagawa et al., 2005; Pitts et al., 2004; Robertson et al., 2010). Experiments in which Orco genes from other insects rescue receptor-neuron responses in mutant D. melanogaster lacking copies of Or83b demonstrate that the function of Orco proteins is conserved. (Jones et al., 2005). The marked dependence of many insect species on one or a few plant species for feeding and/or oviposition is a common basis for reproductive isolation and thus is likely to accompany speciation (Berlocher and Feder, 2002). In many of these insect–plant host relationships, volatiles play a key role in location of hosts. In the D. melanogaster species subgroup, an extreme example of a hostplant specialist is Drosophila sechelia, a species endemic to the Seychelles Islands that lays eggs exclusively on the local Morinda plant, whose fruit is toxic to the larvae of other Drosophila species (Tsacas and Baechli, 1981). Adult D. sechelia exhibit multiple adaptations of their olfactory system to Morinda volatiles (discussed further in Sections 4.2.1 and 4.2.2). The geographic isolation of D. sechelia from most other Drosophila species is sufﬁcient, albeit not necessary, for host specialization of the olfactory system. For instance, the apple maggot Rhagoletis pomonella and its sibling species complex constitute a welldocumented example of sympatric speciation (i.e. divergence without geographic isolation) that is driven by species-speciﬁc behavioral preferences and adaptations of the olfactory system (discussed further in Sections 4.2.1 and 4.2.2) for the fruit volatiles of host plants that ﬂower at different times (Linn et al., 2003). Should closely related insect species share both a geographic range and common food sources, reproductive isolation might be physiological or behavioral context (Anton et al., 2007; Bodin et al., 2008). Attraction to olfactory stimuli can be diminished after mating (e.g. male moth Agrotis ipsilon; Barrozo et al., 2010; Gadenne et al., 2001) or feeding (e.g. female mosquito Anopheles gambiae; Klowden, 1996). The neural mechanisms underlying these similar behavioral changes appear, respectively, to be central (i.e. decreased response of AL neurons; Gadenne et al., 2001) and peripheral (i.e. decreased antennal sensitivity; Takken et al., 2001) owing to down-regulation of olfactory-receptor proteins (Fox et al., 2001). Innate attraction to food odors in D. melanogaster is mediated chieﬂy by a single glomerulus (Semmelhack and Wang, 2009). Recent work has demonstrated a mechanism in which hunger, signaled by low insulin, upregulates a peptide receptor on the ORCs innervating this glomerulus, facilitating vesicle release and ultimately inﬂuencing the animal to search for food (Root et al., 2011). Finally, locusts exhibit state-dependent valuation of food resources, such that the locust Schistocerca gregaria prefers volatiles associated with food last encountered when the animal was nutrient stressed (Pompilio et al., 2006). Thus, different neural mechanisms can produce similar behavioral effects, and the internal state of an insect likely alters olfactory function at multiple levels, in different ways for different species. Insects offer numerous case studies, each revealing connections among olfactory stimuli, neural mechanisms, and behavior. A comprehensive description of natural, olfaction-based behaviors and of the volatiles that control or modulate them can guide experimental analysis of the neural mechanisms that underlie olfactory guided behaviors. 4. Evolution and speciation Author's personal copy 113 432 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 maintained by multi-component sex-attractant pheromone mixtures that evoke mate-seeking behavior, a well-studied phenomenon among the Lepidoptera (moths and butterﬂies), although considerably fewer volatile pheromones have been identiﬁed among butterﬂy species than in moths. Reproductive isolation is maintained among several sympatric moth species of the genus Heliothis by a system in which females of multiple species release a common 16-carbon aldehyde (Z-11-hexadecenal) as the main component of their sex pheromone, but each species-speciﬁc sexpheromone mixture contains different secondary components. These minor components have been shown both to evoke mateseeking behavior (Vetter and Baker, 1983, 1984) and to antagonize such behavior in sister species (Vickers and Baker, 1997). Studies of the reliance of insect species on a limited set of plants and on species-speciﬁc pheromones is of particular interest to neurobiologists, as specialized behavior often is reﬂected in specialization of the peripheral, and even, central chemosensory system (de Bruyne and Baker, 2008). 4.2.1. Peripheral adaptations for olfactory specialization The peripheral olfactory systems of closely related species of insects are likely to exhibit multiple structural and functional homologies. However, recent work using insect groups that are either undergoing or have relatively recently undergone shifts in host-plant specialization demonstrates that a variety of adaptations of the olfactory system can subserve exclusive host relationships. Male orchid bees (Euglossini) express novel mating behavior in which they harvest blends of orchid volatiles and subsequently release them in order to attract mates (reviewed in Cameron, 2004). Distinct ‘collections’ of volatiles attractive to different females may act as isolating mechanisms during incipient speciation, and two male morphotypes have been shown to harvest volatile mixtures that differ by the inclusion of a major component. Divergent behaviors among males are reﬂected in olfactory function, as GC-EAD experiments have shown distinct responses to the differing component between male morphotypes (Eltz et al., 2008). In the apple maggot ﬂy R. pomonella, ORCs from three races that exhibit distinct behavioral preferences for three different host plants are differentiated, not by functional type or relative abundance in the antenna (Olsson et al., 2006a) but by their response thresholds and temporal ﬁring patterns to volatiles emitted by their preferred host plant (Olsson et al., 2006b). In contrast, specialization of D. sechelia on Morinda is reﬂected not only in a shift of the abundance of a single type of sensillum (ab3), apparently at the expense of other types of sensilla found in heterospeciﬁcs (Stensmyr et al., 2003), but also in the altered afﬁnity of an ab3 ORC for a host volatile. This drastic shift in ORC tuning corresponds to a change of only nine amino acids in the ab3 ORC receptor sequence (Dekker et al., 2006). Although frequent, adaptations of the peripheral olfactory system that emphasize representations of host- and mate-related volatiles at the expense of other volatiles may take on diverse forms: shifts in receptor abundance, shifts in receptor afﬁnity, or both acting in parallel. 4.2.2. Central adaptations for olfactory specialization Common organizational principles of insect olfactory systems permit prediction of differences in the anatomy and neural circuitry of the AL based on peripheral olfactory adaptations in closely related species. Owing to convergence of axons of ORCs of a single receptor phenotype in a particular AL glomerulus, an increase in the abundance of one type of ORC is likely to result in an increase in the volume of its target glomerulus. The same convergence is also likely to transform a change in the molecular receptive range of an ORC to a change in the tuning properties of PNs arborizing in the corresponding glomerulus. In D. sechelia, the DM2 glomerulus, in which ab3 ORC axons terminate, is correspondingly enlarged relative to the homologous glomerulus in D. melanogaster (Dekker et al., 2006). Although ab3 ORCs can detect their preferred volatile, methyl hexanoate, at amounts as low as ﬁve fg, DM2 PNs may beneﬁt from convergence of ORC axons to exhibit an even greater sensitivity by improvement of the signal to noise ratio in response to the compound (Dekker et al., 2006). Male moths of two closely related species, Heliothis virescens and Heliothis subﬂexa, have a subset of antennal ORCs that are morphologically identical but sensitive to chemically different secondary pheromone components (Baker et al., 2004). Nevertheless, the male-speciﬁc macroglomerular complexes (MGCs) in the ALs of both species share identical volumes and a common spatial organization (Vickers and Christensen, 2003). Within the MGCs of H. virescens and H. subﬂexa, the PNs originating in an orthologous large glomerulus (the cumulus) are tuned to a major pheromone component common to both species (Vickers and Christensen, 2003). The PNs of an adjacent glomerulus (the DM glomerulus) in each species, however, are functionally different, dedicated to processing information about a secondary component unique to the conspeciﬁc pheromone blend. DM tuning may have a complex genetic basis, as hybrid males of these two species exhibit responses to both secondary components while maintaining parental cumulus tuning (Vickers, 2006a,b). Divergent mateseeking behavior in H. virescens and H. subﬂexa relies on combinatorial activation of a glomerular array, providing a model in which the AL exploits small differences in the quality and proportion of the olfactory stimulus in order to execute speciesspeciﬁc behavior (Lei and Vickers, 2008). 5. Central olfactory pathways Recent progress in describing the architecture of the major centers for olfaction in the insect brain – the AL, mushroom body (MB), and lateral horn (LH) of the protocerebrum – in several species has suggested that olfactory centers at all levels are not homogeneous processing units. Instead, in different species they are divided into anatomical or functional subsystems that interact to varying degrees. Here we review ﬁndings on the structure and function of olfactory brain organization in insect taxa investigated to date. 5.1. Antennal lobe ORCs are anatomically and functionally isolated from one another at the periphery (but see Getz and Akers, 1994, and Andersson et al., 2010, for possible exceptions), and axons of ORCs expressing the different ORs terminate in different glomeruli in the AL, the ﬁrst brain region in which olfactory information channels typically interact. In D. melanogaster, it is well established that glomeruli receive input from receptors expressing one OR, and all ORCs expressing a particular OR terminate in one glomerulus (Couto et al., 2005; Fishilevich and Vosshall, 2005; Gao et al., 2000; Vosshall et al., 2000). Where more than one receptor is expressed in ORCs that terminate in a glomerulus, the combination is still unique to that glomerulus (Couto et al., 2005; Fishilevich and Vosshall, 2005; Goldman et al., 2005; Vosshall et al., 2000). Experiments with transplantation of antennae between male and female moths (Rössler et al., 1999) or related species of moths (Vickers et al., 2005), demonstrate that the identity of the ORC determines the functional identity of the glomerulus in these animals as well. Evidence for ORC tuning governing glomerular function in other insects is currently sparse (Ghaninia et al., 2007; Kelber et al., 2006; Robertson and Wanner, 2006), but it is reasonable to assume that the principle is conserved among insects, with the exception of some hemipteran and orthopteran Author's personal copy 114 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 species (Ignell et al., 2001; Kristoffersen et al., 2008a). Glomeruli thus represent olfactory information channels with unique response proﬁles (Hallem and Carlson, 2006) that interact via a local network in the AL. Certain insect taxa lack this anatomical segregation of input channels. At one extreme, the reduced ALs of the Psyllidae (plant lice) and members of the Aphidoidea (both Hemiptera: Sternorrhyncha) apparently do not have glomeruli, despite their reliance on olfaction to locate plant hosts (Kristoffersen et al., 2008a). The ALs of locusts (Orthoptera: Acrididae), exhibit microglomerular structure but not segregation of ORC input (Ignell et al., 2001) (Fig. 2A). Antennal ORCs, likely expressing different ORs, project to 1–3 AL ‘‘microglomeruli’’ (Hansson et al., 1996), and the dendrites of PNs sample from 10 to 25 anatomically distant microglomeruli (Anton and Hansson, 1996). Dendrites of PNs arborize in roughly concentric rings from the outside to the inside of the AL, but the functional signiﬁcance of that pattern is unclear (Anton and Hansson, 1996; Jortner et al., 2007). Several species of the Acrididae exhibit a novel aglomerular region within the AL, of unknown function in olfactory processing (Ignell et al., 2001). Locusts and some other hemimetabolous insects also have an anatomically separate chemosensory center, the glomerular lobe, which receives primary afferents from ORCs in the maxillary palp (Frambach and Schürmann, 2004; Schachtner et al., 2005). Among holometabolous insects, the glomerular lobe is fused to the AL, and glomeruli receiving inputs from non-antennal sources (e.g. the palps) are distributed among ‘antennal’ glomeruli. In a majority of insects that have been studied, AL glomeruli receive sensory input from a single type of ORC and relay output mainly through uniglomerular PNs (Hildebrand and Shepherd, 1997). In this manner, input to glomeruli from the periphery represents an ensemble of independent sensory channels. However, higher-order organization of glomerular innervation and clustering of behaviorally related glomeruli complicate the ‘independence’ of glomeruli by regrouping them in the AL into anatomical, and perhaps functional, subdivisions. In honey bees and ants (Kirschner et al., 2006; Zube et al., 2008, respectively), the antennal nerve divides into distinct tracts upon entering the AL, each tract feeding a cluster of neighboring glomeruli (Fig. 2C). Similarly, the ALs of several lepidopteran species exhibit MGCs— regions in which sex-pheromonal information is processed in enlarged, neighboring glomeruli in males (see Christensen and Hildebrand, 2002 for a review) (Fig. 2B)—as well as sexually dimorphic regions in the female AL, possibly related to volatiles important for oviposition (Reisenman et al., 2009; Rospars and Hildebrand, 2000). The AL of D. melanogaster exhibits no obvious anatomical divisions, but groups of glomeruli are distinguished by developmental origins (Jefferis et al., 2001), expression of common genes, or the type of sensilla housing the ORCs that innervate the glomerulus (Benton et al., 2007; Couto et al., 2005). Among these groups is a trio of glomeruli that in male ﬂies are sites of pheromone-information processing (Datta et al., 2008). Both ORCs and PNs in these glomeruli express fruitless, a gene involved in courtship behavior (Manoli et al., 2005). Divisions in an AL can also represent areas of thermo- or hygro-sensitive input (Nishino et al., 2003; Zeiner and Tichy, 2000; Ruchty et al., 2010). Thus, within the AL, channels devoted to different modalities or to volatiles associated with different behaviors may be segregated, although some divisions are likely parallel systems in which olfactory stimuli activate many glomeruli across divisions (reviewed in Galizia and Rössler, 2010). The neural circuitry of the AL in different species provides varied mechanisms for interaction among glomeruli. LNs typically are multiglomerular, with neurites that extend to many or all glomeruli (often termed ‘homo’ or ‘global’ LNs) or that 433 interconnect only a smaller number of speciﬁc glomeruli (‘oligoglomerular’ or ‘hetero’ LNs) (Abel et al., 2001; Galizia and Kimmerle, 2004; Matsumoto and Hildebrand, 1981; Reisenman et al., 2008; Seki and Kanzaki, 2008; Seki et al., 2010; Wilson and Laurent, 2005). In moths (M. sexta [Hoskins et al., 1986] and Bombyx mori [Seki and Kanzaki, 2008]), ﬂies (D. melanogaster [Seki et al., 2010]), and cockroaches (Periplaneta americana [Distler, 1989]), a majority of LNs are global and GABAergic. Hymenopterans have a comparatively novel population of LNs; ‘hetero’ LNs are more common (Fonta et al., 1993), may receive ORC input in only one glomerulus through dense innervation of its apical zone, and project sparsely to several other glomeruli (Dacks et al., 2010; Galizia and Kimmerle, 2004). LNs produce Na+-based action potentials in response to olfactory stimulation in most insects in which they have been studied (c.f. Christensen et al., 1993; Galizia and Kimmerle, 2004; Wilson and Laurent, 2005). By contrast, LNs in locust ALs (MacLeod and Laurent, 1996), as well as a subset of LNs in cockroach ALs (Husch et al., 2009), respond to antennal stimulation with subthreshold membrane oscillations and voltageactivated calcium currents. Notably, a majority of such nonspiking cells in the cockroach (P. americana) AL are not GABAergic (Husch et al., 2009). LNs also exhibit additional, diverse neurotransmitter phenotypes. All but the most basal hymenopterans (Dacks et al., 2010), and some cockroaches (Leucophaea maderae) (Lösel and Homberg, 1999; Nässel, 1999), have a number of histaminergic LNs. Histamine inhibits responses to olfactory stimuli in the glomeruli of honey bees, possibly by presynaptic inhibition of ORCs (Sachse et al., 2006). A class of excitatory, cholinergic LNs has been identiﬁed in D. melanogaster (Huang et al., 2010; Shang et al., 2007). Excitatory LNs have yet to be demonstrated decisively in other insect species, but many of the functions of excitatory interconnections can be performed by disinhibition based on serial inhibitory synapses formed by LNs (Christensen et al., 1993; Avron and Rospars, 1995). Small groups of LNs are further differentiated by the expression of one or more putative neuropeptides, likely coreleased with GABA (Nässel and Homberg, 2006). Finally, ALs are innervated by various centrifugal modulatory neurons, affecting the function of the AL according to circadian, appetitive, or associative information from other brain areas (Dacks et al., 2005, 2006; Ignell, 2001; Nässel, 1999; Sinakevitch et al., 2005). 5.1.1. Inhomogeneous interactions between glomeruli There is increasing evidence that the network of glomerular interactions, while stereotyped across individuals, is not a homogenous all-to-all network. Inhomogeneity of interglomerular connections may provide a basis for species-speciﬁc glomerular interactions that facilitate processing of sensory information about natural volatile stimuli. The strength of local excitatory inputs to PNs (Olsen et al., 2007) and presynaptic inhibition of ORC inputs (Olsen and Wilson, 2008) are not correlated in different glomeruli responding to the same olfactory stimuli, as would be expected if both glomeruli were receiving only inputs carrying information about total afferent input. LNs in D. melanogaster branch with heterogeneous density patterns in multiple glomeruli and exhibit a degree of olfactory selectivity (Wilson and Laurent, 2005). Imaging of a population of LNs in D. melanogaster reveals patterns of synaptic activity that are speciﬁc to particular olfactory stimuli (Ng et al., 2002). Finally, the expression of GABAB-like receptors in ORC terminals is heterogeneous throughout the D. melanogaster AL, and is, for example, particularly dense in pheromone-responsive glomeruli and nearly absent in a CO2-responsive glomerulus (Root et al., 2008). These studies suggest that inhibitory and excitatory inputs carry additional information that is glomerulus- and stimulus-speciﬁc. Consistent with this idea, a model of the honey-bee AL most accurately predicts the experimentally Author's personal copy 115 434 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 Fig. 2. Schematic representations of the olfactory systems of four commonly studied animals. In each, the AL, (black circle) MBC, and LH (black ovals) are depicted, along with the l-, ml-, and m-APT (dark grey, light grey, and medium grey lines, respectively), which comprise the output of the AL (after Galizia and Rössler, 2010). Particular, characteristic features of the organization of the olfactory system of each order are illustrated in color. Features are chosen to illustrate differences between model insects, and do not represent an exhaustive representation of the organizational principles of each olfactory system. (A) Orthoptera (S. americana, locust). The AL of the locust is characterized by ORCs (in blue, redrawn from Hansson et al., 1996) which project to 1–3 microglomeruli (grey circles), and exclusively multiglomerular PNs (red, redrawn from Anton and Hansson, 1996) arborizing in 10–25 microglomeruli. This is in contrast to the uniglomerular projections of ORCs and majority uniglomerular PNs in the other orders illustrated. PNs project via the mAPT to the MBC and LH, and diffusely though the ml-APT to as-yet-unknown targets in the lateral protocerebrum. The MBC and LH of locusts exhibit no obvious anatomical or functional subdivision. (B) Lepidoptera (B. mori, M. sexta, S. littoralis, others). The AL of male moths is typically subdivided into a ‘‘main’’ AL (green-shaded circles) and a macroglomerular complex consisting of pheromone-responsive glomeruli (blue-shaded ovals). Uniglomerular PNs in the main AL project through the m-APT and arborize throughout the MBC and LH, while uPNs from the MGC arborize chieﬂy in a more conscribed region of the MBC and in the inferior lateral protocerebrum (ILPC), neighboring the LH. (C) Hymenoptera (A. mellifera, C. ﬂoridanus, others). ORCs enter the ALs of bees and ants in several tracts (four tracts illustrated, as found in A. mellifera). Each tract terminates in a distinct set of neighboring glomeruli (cyan, blue, orange and yellow circles, numbers not representative). The AL is divided into two hemi-lobes (magenta and green shading) by associated output tract. uPNs from glomeruli in one hemi-lobe send axons through the m-APT (magenta line), arborizing (magenta shading), in the outer portion of the lip (l) and the middle portion of the basal ring (br) of the MBC (one calyx depicted) before terminating in the LH. uPNs from the other hemi-lobe send axons through the l-APT (green line), arborizing (green shading) in the LH before terminating in the inner portion of the lip and the basal ring of the MBC. In the LH, each tract occupies a segregated region with some overlap between them. (D) Diptera (D. melanogaster). ORCs in ﬂies send axons to single glomeruli in the Author's personal copy 116 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 observed output patterns of PNs, not when inhibitory connections are weighted homogeneously or when they vary with spatial proximity but instead when they scale according to the similarity of ORC input to the glomerulus (Linster et al., 2005). In the moth M. sexta, sexually dimorphic glomeruli have strong, inhibitory connections to the main AL but apparently do not receive reciprocal inhibition from isomorphic glomeruli (Reisenman et al., 2008), and in D. melanogaster, wide-ﬁeld LNs are less likely to innervate a glomerulus associated with social communication (Chou et al., 2010; Wilson and Laurent, 2005). Finally, the strength of connections between glomeruli may be affected by learningrelated plasticity observed in the AL (Daly et al., 2004; Thum et al., 2007; Yu et al., 2004), perhaps reinforcing relations between output channels associated with reward or punishment. LNs appear to provide a network of inhomogeneous inhibitory connections among glomeruli, reﬂecting underlying principles of computation not yet fully understood. The AL network has an inhomogeneous, species-speciﬁc architecture allowing for interactions among units, ranging from individual glomeruli to functionally or anatomically related clusters of glomeruli. Information processing is likely to be similarly complex and species-speciﬁc, with common functions mediated by different mechanisms in different species, as well as functions adapted for the particular olfactory world of a species. 5.2. Higher-order olfactory centers From the AL, information about olfactory stimuli is relayed to several higher-order centers for subsequent processing. Recent work has produced maps of the pathways through which information ﬂows in the olfactory system and allows for a preliminary analysis of the input/output characteristics of each level of processing. Across species, these maps show varying degrees of segregation of information channels. The organization of the AL proper and its output tracts, as well as the projections of axons of AL PNs to other brain areas and the integration of information from the AL by third-order neurons, all are organized according to characteristics speciﬁc to groups of insects. In this section, we review those organizing principles. Chief among them is the emerging evidence that, as in the AL, higher-order regions exhibit diverse patterns of segregation of olfactory channels. 5.2.1. Antenno-protocerebral tracts AL PNs project to higher-order centers through several axonal tracts. In the following sections, we employ nomenclature suggested by Galizia and Rössler (2010), who identify three antenno-protocerebral tracts (APTs) with a common position, although not necessarily common function or homology, across many insects (Fig. 2). As the traditional nomenclature of ‘‘antennocerebral tracts’’ differs among species, this newer nomenclature facilitates comparison across taxa. The largest tract is the medial APT (m-APT), which is present in the Archaeognatha and Zygentoma, the most basal insects examined (Strausfeld, 2009; Strausfeld et al., 2009). In ﬂies, moths, ants, honey bees, and cockroaches, most uniglomerular PNs send axons through the mAPT, have collateral projections to the mushroom-body calyces (MBCs), and terminate in the lateral horn (LH) of the protocerebrum (Homberg et al., 1988; Malun et al., 1993; Marin et al., 2002; Rø et al., 2007; Stocker et al., 1990). The multiglomerular PNs of the AL of locusts also follow this tract (Ignell et al., 2001). The lateral 435 APT (l-APT) is composed of the less-common multiglomerular PN axons in moths and cockroaches and a mixture of uni- and multiglomerular PNs in ﬂies. In ants and bees uniglomerular PNs from one hemi-lobe of the AL project through the l-APT, constituting a distinct, parallel olfactory system thus far described only in the Hymenoptera (Galizia and Rössler, 2010; Kirschner et al., 2006; Zube et al., 2008) (Fig. 2C). This system is also present in some basal, non-social hymenoptera and is thought to be an adaptation to complex olfactory environments and behaviors (Rössler and Zube, 2011). The l-APT in cockroaches and ﬂies terminates in the LH, but in moths, bees, and ants it also projects to the MBCs. Finally, additional, minor tracts compose a mediallateral APT (ml-APT) that carries axons of a variety of uni- or multiglomerular PNs and projects in species-speciﬁc patterns to the MBCs, LH, or other regions in the protocerebrum (reviewed in Galizia and Rössler, 2010). Although insects share a common Bauplan of APTs, each taxon exhibits variations on the theme. Tracts sharing a projection pattern but composed of uni- or multiglomerular PNs in different species, for example, can facilitate the testing of hypotheses about the functions of different types of PNs. Similarly, features unique to a taxon, e.g. the parallel uniglomerular PN system in bees and ants, may well be correlated with behavior particular to those species. 5.2.2. Mushroom-body calyx Upon reaching their synaptic targets, PN axons terminate with varying degrees of segregation. We consider ﬁrst the calyces of the mushroom bodies (MBCs), where PNs synapse with Kenyon cells (KCs). Recent work has provided new insights into the structure of MBCs, and especially the compartmentalization of PN inputs and the KCs with which they synapse, demonstrating that the MBC is not a homogeneous structure. Instead, in many insects it contains multiple subsystems, with various architectures of segregation and integration in different species. Here, we consider recent work describing olfactory networks in the MB. We propose that a detailed understanding of these networks is required for investigations of olfactory information processing in this region, as well as processing in the AL that shapes the input to this associative center. In locusts (Schistocerca americana, Jortner et al., 2007; Laurent and Naraghi, 1994) and several moth species (Homberg et al., 1988; Rø et al., 2007), axon collaterals of AL PNs spread throughout the MBC, but a detailed analysis of their branching patterns is not available. In contrast, certain pheromone-responsive PNs of the moth B. mori terminate in circumscribed regions of the MBCs (Kanzaki et al., 2003; Seki et al., 2005) (Fig. 2B). For D. melanogaster, the only insect species studied systematically to date, axons of PNs from the same glomerulus terminate in remarkably similar locations in the MBCs, in apparently fewer and more circumscribed regions than in moths or locusts (Jefferis et al., 2001, 2007; Lin et al., 2007; Marin et al., 2002; Wong et al., 2002). MBCs of ants and bees are subdivided into three regions: lip, collar, and basal ring (Ehmer and Gronenberg, 2002; Gronenberg, 1999, 2001; Gronenberg and Hölldobler, 1999; Mobbs, 1982). Axons of AL PNs projecting in both the l-APT and m-APT terminate in the lip and basal ring (Abel et al., 2001; Ehmer and Gronenberg, 2002; Gronenberg, 1999, 2001; Gronenberg and Hölldobler, 1999; Kirschner et al., 2006; Zube and Rössler, 2008) (Fig. 2C). The basal ring has three concentric layers, receiving (respectively from inside to outside) input from the l-APT, m-APT, and visual neurons from the medulla (Gronenberg, 1999; Kirschner et al., 2006; Zube et al., ipsilateral AL, and through a unique antennal commissure (AC, dark blue lines) to the corresponding glomerulus in the contralateral AL. Glomeruli in the AL are variously related by neuroblast origin of PNs, the type of sensilla that houses the ORCs that innervate the glomerulus, and the MBC regions that the PNs target (red, cyan, yellow, green and purple shading in AL). The majority of uPNs project through the m-APT, and arborize in regions of the MBC deﬁned by the KC type that inhabit them (corresponding red, cyan, yellow, green and purple shading in MBC). The m-APT continues to the LH, where uPNs terminate in conscribed regions, in groupings related but not identical to those in the MBC (grey ovals in the LH). A segregated region (indicated by an asterisk) receives innervation from pheromone-responsive PNs. Author's personal copy 117 436 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 2008). Little overlap is evident between the layers. In the lip region, l-APT PNs innervate the inner core, while m-APT PNS innervate the outermost layer and produce diffuse arborization throughout the MBCs (Kirschner et al., 2006). Several species of hemimetabolous insects also exhibit functional division of the MBCs. In crickets (Gryllus bimaculatus), output neurons from the glomerular lobe (anatomically distinct from the AL and receiving chemosensory input from the mouth parts) terminate in the posterior calyx, while PNs from the AL terminate in the anterior (Frambach and Schürmann, 2004). The MBCs of cockroaches (P. americana) are divided into compartments receiving input from two distinct populations of uniglomerular PNs, male-speciﬁc PNs, and some neurons from the optic lobes (Strausfeld and Li, 1999). Anatomical separation of input channels may not indicate separation of information if the third-order neurons sample across channels. Characteristics of integration depend on the overlap of PN axonal terminals and KC dendritic ﬁelds. At one end of the spectrum, locust KCs have wide dendritic ﬁelds covering a large portion of the MBC (Jortner et al., 2007; Perez-Orive et al., 2002). Each KC is estimated to receive input from ca. 50% of PNs, which Jortner et al. (2007) identiﬁed as approaching the theoretical ideal for a general associative network (Shannon and Weaver, 1964). This suggests a lack of segregation in the locust KC network. In the moth species Spodoptera littoralis and B. mori, KCs with wide dendritic ﬁelds send axons to the a/b and a0 /b0 lobes of the MBs, while KCs with narrower ﬁelds project to the g lobe (Fukushima and Kanzaki, 2009; Sjoholm et al., 2006). The calyx is further subdivided by classes of KCs distinguished by immunoreactivity, also with distinct projection patterns (Fukushima and Kanzaki, 2009; Sjoholm et al., 2005). Given the projection patterns of PNs, KCs of different classes may integrate information from varying numbers of PNs, but it appears that they may sample from any combination of PNs. This simpliﬁcation may yet be challenged by a more detailed study of the MBC in these insects. KCs are arranged in the MBC of worker honey bees in a pattern similar to that in moths. Class-II KCs integrate across the lip, basal ring, and collar of the MBC and thus can receive input from all uniglomerular PN tracts (m- and l-APT) as well as from the visual system (Strausfeld, 2002), while dendrites of class-I KCs are restricted to regions exclusively receiving projections of axons from the l-APT, the m-APT, or both tracts. All classes project to the vertical lobe (analogous to a/b and a0 /b0 lobes of other insects), but the upper two-thirds of the lobe is divided into strata innervated by axons originating in either the lip, collar, or basal ring of the MBC, and the lower third, proposed to be analogous to the g lobe in D. melanogaster, is innervated by the axons of class-II KCs (Strausfeld, 2002). The vertical lobe thus contains strata that receive projections of KCs that integrate either within or between the two main input tracts. Further subdivision according to AL divisions is not apparent, and KCs may sample across divisions that share a tract. Each class-II KC receives input from an estimated 10 PNs, consistent with a greater degree of segregation between input channels in this animal (Szyszka et al., 2005). The MBC of D. melanogaster exhibits a further degree of organization (Fig. 2D). As mentioned above, PNs of the same glomerulus have terminals in corresponding, circumscribed regions of the MBC. The MBC can be divided into four vertical sections, according to neuroblast origin (Ito et al., 1997; Technau and Heisenberg, 1982), each of which is laterally divided into regions containing KCs exclusively from one of ﬁve groups distinguished by target lobe and developmental origin: g lobe, a0 /b0 lobe, and pioneer, early, and late a/b lobe KCs (Lee et al., 1999; Zhu et al., 2003). A detailed analysis of a subset of PNs from 13 glomeruli revealed that PNs target KCs across the vertical divisions, but typically target KCs of one group (Lin et al., 2007) and contribute to distinct, isolated clusters of PN–KC synapses called microglomeruli (Leiss et al., 2009). Extension of these results to the PN groups identiﬁed by Jefferis et al. (2007) suggests that the KCs integrate input only within PN groups and that different groups of PNs are associated with each lobe of the MB. Each KC is estimated to receive input from 10 PNs (ca. 5%) (Turner et al., 2008). If each class of KCs integrates information from only a small group of PNs, this arrangement might produce a system of parallel associative networks. Within each subset, connectivity may more closely resemble the locust MBC, with ca. 50% of input channels making connections with each KC. Detailed ﬁndings about ﬁne divisions of the MBC, directly comparable to those in D. melanogaster, are not yet available for other insects. Available evidence, however, describes a spectrum of input/output segregration within the MBC. At one pole, D. melanogaster PNs and KCs are isolated in multiple, parallel subsystems with little or no overlap, as opposed to locust MBCs in which interaction between any PN and any KC is anatomically possible. As a ﬁrst approximation, this spectrum suggests functional consequences of neural architecture that can inform comparison between species. 5.2.3. Lateral horn In contrast to the MBC, the lateral horn of the protocerebrum in many insects appears to be a diffuse, aglomerular neuropil (e.g. Sun et al., 1997; Yasuyama et al., 2003). In D. melanogaster, PNs terminate in the LH in a stereotyped pattern resembling that in the MBC (Jefferis et al., 2007; Marin et al., 2002; Tanaka et al., 2004; Wong et al., 2002) (Fig. 2D). As in the MBC, PNs with a common neuroblast origin project to similar regions of the LH (Marin et al., 2002). Consistent with this, PNs that terminate in the same region of the LH tend to originate in neighboring glomeruli, although the reciprocal arrangement is not observed (Marin et al., 2002). In addition, PNs cluster in similar groups in the MBC and LH (Jefferis et al., 2007). Finally, PNs that receive input from ORCs in certain types of sensilla project to similar areas of the LH (Jefferis et al., 2007). Thus at least in D. melanogaster, PNs terminate in stereotyped but overlapping patterns. Segregation of inputs in the LH is largely correlated with the input tract through which axons project, or by a distinction between foodderived and social or pheromonal stimuli. The m-APT and l-APT PNs in honey bees and ants terminate in distinct regions of the LH and share a diffuse region of overlap between them (Kirschner et al., 2006; Zube et al., 2008) (Fig. 2C). The LH is similarly divided by input tracts in species of moths (Homberg et al., 1988; Rø et al., 2007) (Fig. 2B) and D. melanogaster (Wong et al., 2002) (Fig. 2D). PNs supplying the ml-APTs in bees and ants produce a unique ‘‘lateral network’’ located between the g lobe of the MB, the LH, and other areas of the protocerebrum (Kirschner et al., 2006; Zube et al., 2008). Some PNs in this group terminate in the LH, exclusively in the area also innervated by the l-APT. The function of this network is not yet understood, but it provides a substrate for interaction between the AL, MB, and LH not present in other insect species. Input to the LH from pheromone-responsive PNs in several species is segregated to varying degrees. In D. melanogaster, axons from two glomeruli project to a restricted region of the LH that contains few arborizations of other PNs (Jefferis et al., 2007) (Fig. 2D). These glomeruli express a male-speciﬁc form of the fruitless gene required for male sexual behavior (Demir and Dickson, 2005; Manoli et al., 2005; Stockinger et al., 2005). This region is enlarged in males and contains an additional PN arborization produced when the male isoform of fruitless is expressed both in PNs and other cells in the brain (Datta et al., 2008). The pheromone-responsive PNs in moths project to a region that is more isolated from PNs originating in the main AL (Homberg et al., 1988; Kanzaki et al., 2003; Seki et al., 2005) (Fig. 2B). In silkworm moths (B. mori), this region is further divided into two Author's personal copy 118 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 partially overlapping segments receiving input from each of two MGC glomeruli (Kanzaki et al., 2003; Seki et al., 2005). Finally, in the LH of the cockroach, PNs responding to cold, dry, or moist air, respectively, terminate in a distinct region of the LH, with areas overlapping those innervated by uni- and multiglomerular PNs from other glomeruli (Nishino et al., 2003). It has been hypothesized that the LH supports integration of more ‘‘stereotyped’’ olfactory information than the MB (Jefferis et al., 2007). Comparing the recent, detailed ﬁndings about the anatomy of the LH and MBC (Jefferis et al., 2007; Lin et al., 2007), we suggest that this distinction lacks adequate support. KCs in the MBC of D. melanogaster integrate information from a restricted subset of AL glomeruli and exhibit pronounced input/output segregation. Similarly to what is observed in the MBC, groups of LH neurons in D. melanogaster integrate input from PNs that arborize in limited numbers of glomeruli and terminate in spatially restricted zones (Jefferis et al., 2007; Tanaka et al., 2004). Thus, available information suggests that neither structure supports an entirely general integration scheme in which information originating in any one glomerulus may be integrated with that originating in any other glomerulus by third-order neurons. Recent investigation of a small, genetically identiﬁable group of KC neurons, however, reveals that members of the group do not respond similarly to a battery of volatile stimuli (Murthy et al., 2008). Thus, while groups of KCs may sample from the same restricted subset of PN inputs, each may receive inputs from a particular combination of PNs within that subset, leading to unique, non-stereotyped responses. The ﬁne structure of the MBC, consisting of many microglomeruli that segregate small groups of input, output, and local circuit synapses (Leiss et al., 2009; Steiger, 1967; Ganeshina and Menzel, 2001), supports this hypothesis. These structures are a common feature of the MBC in neopteran insects (Groh and Rössler, 2011), and they represent an order or organization not seen in the LH. Physiological and behavioral experiments suggest an additional, distinct role for the LH, different from that of the MB and possibly species-speciﬁc. In locusts (S. americana) the LH is the source of a strong, feed-forward inhibition to KCs (Perez-Orive et al., 2002). A cluster of GABAergic LH interneurons (LHIs) reportedly connects the LH to the MBC and likely underlies this feed-forward inhibition. LHIs are driven by the same PN inputs that drive the KCs but with a greater latency. LHIs then inhibit KCs, closing the temporal window during which they can integrate input. GABAergic neurons that may be analogous to the locust LHIs also have been described in ﬂies (D. melanogaster; Yasuyama et al., 2003), moths (M. sexta; Homberg et al., 1987), and cockroaches (P. americana; Nishino and Mizunami, 1998). No evidence of feedforward inhibition in D. melanogaster KCs has been found, however (Turner et al., 2008), and GABAergic LHIs in P. americana are inhibited by multimodal stimulation and thus are likely to subserve a different function (Nishino and Mizunami, 1998). While honey-bee KCs exhibit periodic inhibition similar to that in locust KCs, it is likely driven by KCs themselves, via feedback neurons of the MB peduncle (Grünewald, 1999; Szyszka et al., 2005). It remains to be seen whether the LHI function described in locusts is conserved in other species. 5.2.4. Protocerebrum and beyond Little is known about olfactory information processing beyond the MB and LH. The extrinsic neurons of the MB and LHNs in D. melanogaster project to overlapping regions in diverse parts of the brain (Tanaka et al., 2008). Olfactory information spreads throughout the brain and participates in the organization of numerous behaviors. The participation of MBs in associative memory has been well documented (reviewed in Heisenberg, 2003; Margulies et al., 2005). The varying degrees of segregation 437 within and integration across the parallel divisions of olfactory input suggest that the underlying rules of association are complex and species-speciﬁc. The LH is similarly organized to integrate within or between input channels and may perform multiple processing and feedback functions controlling behavior. Further downstream, a pair of symmetrical brain regions has been closely linked to an important olfactory behavior. Circuits in the lateral accessory lobe (LAL) and the ventral protocerebrum (VPC) of moths (primarily investigated in M. sexta and B. mori) generate a ‘‘ﬂip-ﬂopping’’ pattern of activity in descending neurons that have arborizations in the LAL (Kanzaki et al., 1991, 1994; Olberg, 1983). These neurons switch between high- and lowactivity phases on each successive stimulus pulse of sex pheromone. This pattern of activity corresponds to the ‘‘zig-zagging’’ turns that moths, and many other insects, make when searching for the source of an odor plume (reviewed in Cardé and Willis, 2008) and furthermore is correlated with the activity of motor neurons responsible for head movements during a turn (Kanzaki and Mishima, 1996). Recent work has described the circuitry of the LAL-VPC system as a pair of mutually inhibitory units that alternate, generating long-lasting activity in one hemisphere and quieting the other on each encounter with a pulse of olfactory stimulus (Iwano et al., 2010). Because diverse insects exhibit similar behavior when searching for the unseen source of an odor plume, this mechanism may be conserved across species. Evidence that some insects can locate odor sources in the absence of a plume (e.g. D. melanogaster, Budick and Dickinson, 2006), however, suggests that additional mechanisms speciﬁc to other odor-search strategies may be revealed by comparative work. 6. Comparative coding By combining ﬁndings about how an animal interacts with volatile chemical stimuli in its environment with knowledge of the functional organization of the olfactory system, we can begin to understand how different animals encode olfactory information. Here, we consider current progress on olfactory information processing, focusing on these processes in the AL: gain control and broadening or sharpening of tuning curves between input and output, synchrony between AL PNs, and encoding of the temporal features of the stimulus. Animals may use different coding schemes, or variations of similar schemes, that are proximally determined by the particular structure and mechanisms present in the species and ultimately determined by adaptation to a particular niche and evolution of solutions to speciﬁc challenges. 6.1. Gain control The range of output of an ORC is a function of both the identity and intensity of an olfactory stimulus. ORCs may respond weakly to a wide range of volatiles or to a low concentration of a preferred compound, yet respond robustly to higher concentrations of certain volatiles (c.f. Bhandawat et al., 2007; Bichao et al., 2005; de Bruyne et al., 2001; Ito et al., 2009; Kristoffersen et al., 2008b; Ochieng and Hansson, 1999; Pelz et al., 2006; Schlief and Wilson, 2007; Wilson et al., 2004). Between these extremes of activation, the circuitry of the AL needs to maintain sensitivity, prevent saturation, encode stimulus intensity, and maintain a consistent representation of a stimulus across a range of concentrations. Underlying these functions is the control of gain, or the conversion of signal strength between input and output to make efﬁcient use of the dynamic range of PN output (Fig. 3A). The efﬁcient use of a PN’s dynamic range begins at the ORC-toPN synapse. In D. melanogaster, a spike in an ORC produces a strong, reliable response in the postsynaptic PN (Bhandawat et al., 2007). This synapse has a high probability of neurotransmitter release and Author's personal copy 119 438 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 Fig. 3. Principles of processing in the antennal lobe. (A) Gain. The circuitry of the antennal lobe and synaptic properties of AL neurons determine the relationship between ORC input to a glomerulus and PN output. Gain denotes two distinct, yet related concepts: (i) the nonlinear relationship between the ﬁring rate of individual ORCs and the ﬁring rate of the PNs on which it synapses (from Bhandawat et al., 2007), which underlies, in part, (ii) the relationship between odor concentration and PN output. The slope of these functions is the gain. Neuromodulation can alter this relationship (dark grey and light grey lines in ii). (B) Sharpening and broadening of the population output. (i) Lateral inhibition can produce an activity-dependent reduction in the ﬁring rate of PNs across the responding population. Glomeruli receiving weak input (orange line ORC) may be completely silenced (grey dashed line PN). (ii) The effect is to ‘‘sharpen’’ the representation of the stimulus from the input (light grey curve) to the output (dark grey curve) layers. Population activity is represented as a Gaussian curve with a central peak, in analogy to other sensory systems, although patterns of activity in insect ALs may take a different form. (iii) Lateral excitation spreads activity throughout the AL, increasing the response of some PNs, and in some cases producing output in PNs (orange line) that receive no input from their cognate ORCs (dashed grey line). (iv) The population response in the PN output layer (dark grey line) is ‘‘broadened’’ with respect to the ORC input layer (light grey line). therefore depresses quickly during high-frequency spiking (Kazama and Wilson, 2008). As a result, the transformation between ORC input and PN output is highly nonlinear, amplifying weak and suppressing strong inputs (Bhandawat et al., 2007) (Fig. 3Aii). In response to a given stimulus, input to the AL is typically a combination of weak activation of many ORCs and strong activation of only a few (Bhandawat et al., 2007). The nonlinear transformation ampliﬁes small differences between the weaker inputs, making PN responses equally likely across all possible ﬁring rates, a phenomenon known as histogram equalization that also is observed in the visual system of insects (Laughlin, 1981). Similar conclusions can be drawn for the ORC/PN transformation in moths (Ito et al., 2009; Jarriault et al., 2010) and honey bees (Krofczik et al., 2009). Inhibitory and excitatory synaptic connections in the AL, mediated by LNs that may integrate inputs in many or all glomeruli, can adaptively regulate the dynamic range of PN responses relative to the total input to the AL. In D. melanogaster, both presynaptic (Olsen et al., 2010; Olsen and Wilson, 2008; Root et al., 2008) and postsynaptic (Silbering and Galizia, 2007) inhibition in glomeruli scales with the total primary-afferent input to the AL. Similar global inhibition has been observed in honey bees (Deisig et al., 2006, 2010; Sachse and Galizia, 2002, 2003), and the presence of wide-ﬁeld LNs in the ALs of many insect taxa suggests that this function may be universal. Because global inhibition increases with total ORC input, the representation of an olfactory stimulus is maintained by the relative activation of PNs across the AL over a wide range of concentrations (Sachse and Galizia, 2003). However, the differential expression of GABAB-like receptors that mediate presynaptic inhibition provides for independent gain control in each glomerulus (Root et al., 2008). Indeed, there is evidence that the weighting of global, interglomerular inhibition varies between glomeruli (Olsen et al., 2010). A class of excitatory LNs in D. melanogaster also encodes total afferent input across glomeruli and may increase PN output in weakly activated glomeruli to a level that can be detected by downstream neurons (Huang et al., 2010; Olsen et al., 2007; Shang et al., 2007). These competing, global excitatory and inhibitory inputs may be balanced in a PN, such that they increase together with the concentration of an olfactory stimulus (Root et al., 2007). The simultaneous adjustment of balanced, background excitatory and inhibitory inputs can serve as a form of gain modulation as has been hypothesized for mammalian cortical neurons (Chance et al., 2002), such that gain is increased with low background and decreased with high background. Weak inputs to a PN thus would be ampliﬁed relative to the total input to the AL, enhancing weak portions of the representation of a stimulus at low concentration but suppressing weak, likely noisy inputs at high total stimulus concentration. Such dynamic modulation of gain in the AL has yet to be demonstrated. Finally, serotonin (5-hydroxytryptamine) has been shown to increase the gain and enhance the responses of PNs in the ALs of moths (Dacks et al., 2008) (Fig. 3Aiii). In adult M. sexta (Kloppenburg et al., 1999) and B. mori (Gatellier et al., 2004), levels of serotonin in the AL and protocerebrum peak during the insect’s active phase in the circadian cycle. Similar work on D. melanogaster found glomerulus- and odor-speciﬁc enhancement of PN responses but no effect of serotonin on gain, suggesting subtle differences between species (Dacks et al., 2009). Serotoninenhanced presynaptic inhibition is known to be involved in gain control (see above), however, and might play an additional role in modulating gain in fruit ﬂies. Serotonin-immunoreactive neurons have been described in the ALs of many insect species (Dacks et al., 2010; Ignell, 2001; Kent et al., 1987; Salecker and Distler, 1990; Siju et al., 2008; Wegerhoff, 1999). Depending on the species, these Author's personal copy 120 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 apparently serotonergic cells innervate a variable number of glomeruli and have species-speciﬁc connections with other areas in the brain. Gain in the ALs of diverse insects thus might be modulated through the inﬂuence of species-speciﬁc brain areas, and under conditions speciﬁc to each species. 6.2. Tuning/coding transformation Olfactory stimuli elicit characteristic patterns of ORC input across glomeruli (c.f. Deisig et al., 2006; Hallem and Carlson, 2006; Ng et al., 2002; Sachse et al., 1999; Silbering and Galizia, 2007; Silbering et al., 2008; Wang et al., 2003). The ﬁdelity with which ORC input is transformed to PN output, however, is debatable. Does the dense interconnection of glomeruli by AL interneurons perform a transformation, either sharpening or broadening, between the ORC input and the PN output of the AL? In this section, we review the evidence that inhibitory and excitatory circuits in the AL shape olfactory codes. In addition, we suggest that apparently conﬂicting results stem from either volatile- or species-speciﬁc interactions and likely could be resolved by a comparative approach considering the natural olfactory ecology of each species. This discussion necessarily assumes that information about odors is encoded in the pattern of activity across the AL. It is useful to note at this point that some innate behaviors, e.g. mate-seeking in moths (B. mori; Kanzaki and Shibuya, 1983) and ﬂies (D. melanogaster; Kurtovic et al., 2007) and food-seeking in ﬂies (D. melanogaster; Semmelhack and Wang, 2009), depend on single glomeruli that apparently provide all information necessary to drive those behaviors. 6.2.1. Sharpening A primary effect of inhibition in a neural circuit is to prevent under-stimulated units from participating in the output. In this way, a stimulus is represented by the most strongly activated neurons, and overlap between codes for different stimuli is reduced by a ‘‘sharpening’’ of the population code (Urban, 2002) (Fig. 3Bi and ii). Ca2+-imaging studies in ALs of both honey bees (Apis mellifera) and fruit ﬂies (D. melanogaster) have shown that volatile-evoked activation patterns of ORCs largely match the activation patterns observed in PNs (Ng et al., 2002; Sachse and Galizia, 2002, 2003; Silbering and Galizia, 2007; Wang et al., 2003). In both species, global inhibition reduces the responses of all activated glomeruli (Sachse and Galizia, 2002, 2003; Silbering and Galizia, 2007). In A. mellifera, however, PN output is suppressed by inhibition despite weak or intermediate activation of ORCs projecting to the same glomerulus (Sachse and Galizia, 2002) (Fig. 3Bi). Similar suppression of ORC-to-PN transmission by global inhibition is not seen in D. melanogaster (Silbering and Galizia, 2007; Silbering et al., 2008). Global inhibition reduces PN responses in both A. mellifera and D. melanogaster but sharpens the population code only in the former. A second form of inhibition exerts glomerulus- and stimulusspeciﬁc effects on ORC-to-PN output in D. melanogaster (Wilson and Laurent, 2005). In A. mellifera, blockade of GABAA-like receptors fails to eliminate suppression of ORC-to-PN transmission (Sachse and Galizia, 2002), and the remaining odor- and glomerulus-speciﬁc suppression of responses may be mediated by histaminergic LNs (Sachse et al., 2006). Inhibitory interaction between glomeruli in ALs of D. melanogaster is most evident in patterns of mixture-suppression of PN output: activation of ORC input by one mixture component inhibits PN output in glomeruli activated by the complementary component of a binary mixture (Silbering and Galizia, 2007). Similar mixture effects are observed in A. mellifera (Deisig et al., 2010; Joerges et al., 1997; Silbering and Galizia, 2007). In both species the effect is volatile- and glomerulus-speciﬁc, consists primarily of suppression of PN 439 output, and decreases the similarity between responses for different mixtures. Complex, heterogeneous, inhibitory networks appear to shape PN output similarly in both D. melanogaster and A. mellifera but may utilize species-speciﬁc cell types. 6.2.2. Broadening The response proﬁles of several PNs in D. melanogaster are broader than those of their cognate ORCs (Schlief and Wilson, 2007; Wilson et al., 2004, but see Root et al., 2007; Wang et al., 2003). This remarkable result suggests that network mechanisms broaden the molecular receptive range of PNs with respect to that of the ORCs that provide sensory input to them (Fig. 3Biii and iv). The molecular receptive range of a neuron can be broadened in two distinct ways: (1) the PN may respond more strongly to olfactory stimuli that weakly activate the cognate ORCs, or (2) the PN may respond to volatiles that do not activate cognate ORCs. These two effects likely are mediated by two network mechanisms. First, ORC-PN synapses amplify small, probabilistic inputs from multiple, weakly responsive ORCs to a greater degree than the large, sustained input of multiple ﬁring ORCs (Bhandawat et al., 2007; Kazama and Wilson, 2008). The nonlinear transformation between ORC and PN responses to volatiles thus broadens the molecular receptive range of a PN and underlies the ﬁrst type of broadening. In D. melanogaster, excitatory LNs (Chou et al., 2010; Shang et al., 2007) provide indirect, excitatory input to PNs from other activated glomeruli (Fig. 3B). Such excitatory LNs receive direct input from ORCs (Huang et al., 2010) and are broadly (Yaksi and Wilson, 2010) or more narrowly (Huang et al., 2010) responsive to olfactory stimuli. Excitation is spread to PNs across the AL through electrical synapses (Huang et al., 2010; Yaksi and Wilson, 2010). The broadening of PN responses from their cognate ORC input occurs for speciﬁc stimulus-glomerulus pairs, while in response to other volatiles there remains a direct, linear relationship between ORC and PN activity within the glomerulus (Silbering et al., 2008). Such excitatory lateral interactions have not been observed in other species, but LNs that are not GABA-immunoreactive have been observed in the AL of a moth (B. mori; Seki and Kanzaki, 2008) and a cockroach (P. americana; Husch et al., 2009) and may belong to a similar class of eLNs. Finally, disinhibition, involving serial inhibitory synaptic connections between glomeruli, might effectively broaden the molecular receptive range of a PN (c.f. Christensen et al., 1993; Avron and Rospars, 1995). 6.2.3. Sharpening versus broadening in two species An apparent contradiction exists in the different transformations imposed upon ORC inputs in the ALs of honey bees (A. mellifera) and fruit ﬂies (D. melanogaster). The dominant mode of interaction in A. mellifera appears to be inhibition that suppresses weakly activated glomeruli and sharpens the representation of an olfactory stimulus, while in D. melanogaster evidence suggests that lateral excitation spreads activation and broadens the representation. These two modes of interaction differ, one seemingly decreasing the similarity between representations of different olfactory stimuli and the other spreading activation across the AL and reducing the chemosensory speciﬁcity of the output channels. A possible resolution of the difference can come from considering the anatomy and natural context of olfaction in these animals. As discussed above, the olfactory system of D. melanogaster may represent extreme segregation, as PNs from subsets of glomeruli have synaptic connections with subsets of KCs in the MBC, producing segregated, parallel systems that interact via local circuitry. If these channels did not interact, any subsystem would receive information from only the ORCs that directly feed into it. The molecular receptive range of ORCs in turn is limited by ligand– receptor interactions, further reducing the information available to an already impoverished system. The widespread excitatory Author's personal copy 121 440 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 interactions reported in the D. melanogaster AL can share information between subsystems in the AL, expanding the receptive range of each subsystem while allowing the KC networks to perform parallel, separate functions that are still poorly understood. For instance, activation of the VA71 glomerulus, whose PNs project to g-lobe KCs, excites both another glomerulus (DL1) connected to the g-lobe and glomerulus VA1d, whose PNs project to KCs associated with the a/b lobe (Lin et al., 2007; Olsen et al., 2007). By contrast, the AL of A. mellifera contains 150–180 glomeruli, nearly four times the number found in an AL in D. melanogaster (Galizia et al., 1999). Each of the main AL output tracts in A. mellifera contains PNs axons from more glomeruli than the total number in the D. melanogaster AL, and while some KC classes receive information from only one tract, others apparently can integrate across all glomeruli (Kirschner et al., 2006). With all available information feeding into the system, separation of overlapping representations becomes more critical and perhaps accounts for the predominance of inhibitory interactions between glomerular channels in ALs of A. mellifera. A. mellifera and other hymenoptera use an exceptionally wide range of olfactory cues, because of the number and diversity of ﬂowers they visit throughout their range, and an additional, complex array of social odors. These insects also can perform remarkable feats of olfactory discrimination, including learning to identify rewarding from unrewarding volatile stimuli from different cultivars of the same ﬂower based on the relative ratios of the volatiles common to each (Wright et al., 2005). Although ﬂies of the genus Drosophila include generalists (e.g., D. melanogaster) and specialists in wide variety, including a species adapted to live exclusively on the mouthparts of crabs (reviewed in Stensmyr and Hansson, 2007), individual species likely do not employ ﬁne discrimination over a wide range of olfactory stimuli like that observed in A. mellifera. Efﬁcient coding in a network is a function of the number of encoding units, the molecular receptive range of each, and most importantly for our analysis, the number and similarity of stimuli that must be encoded (c.f. Olshausen and Field, 2000; Simoncelli and Olshausen, 2001). The olfactory system of A. mellifera encodes a larger number of olfactory stimuli with a larger number of receptors and glomeruli, while D. melanogaster employs a less numerically complex AL, and perhaps even smaller subsystems within the AL, to encode a smaller range of behaviorally important stimuli. The AL network of each animal likely adjusts the receptive range of each channel to achieve optimal coding. Theoretical studies have demonstrated that optimal tuning of neurons in a population is a function of stimulus-dependent behaviors (Salinas, 2006), stimulus dimensionality (Zhang and Sejnowski, 1999), and the degree of noise in the sensory signal (Pouget et al., 1999). The inﬂuence of these factors on olfactory coding can be approximated by analysis of the interaction of the animal with volatiles in its environment, placing coding in the proper, natural context. The neurobiology of olfaction in insects demonstrates how evolution, from a common starting point of input, output, and interneurons, can adjust the properties of the network to suit the particular demands of a species’ life history and environment. 6.3. Synchrony Synchronous production of action potentials by the output neurons of a neural circuit is increasingly recognized as a critical feature of coding in many sensory systems (Singer, 1999). Synchronous spikes in separate output channels can ‘‘bind’’ the elements of the representation of a stimulus, allowing higherorder centers to process the inputs as a single percept (Engel et al., 1997; Engel and Singer, 2001). Among insect olfactory systems, those of locusts, moths, and ﬂies have become favorable models for studies of spiking synchrony. Synchronous activity is organized differently in these systems, but in all three synchrony is associated with inhibitory local circuitry (Ito et al., 2009; Lei et al., 2002; MacLeod and Laurent, 1996; Tanaka et al., 2009). In contrast to LNs in most other insects that have been studied, LNs in the ALs of several locust species produce only graded, oscillating potentials in response to olfactory stimuli (Anton and Hansson, 1996; Laurent and Naraghi, 1994). Strong, oscillatory inhibition from these neurons constrains PNs to ﬁring mostly in phase with the 20-Hz oscillations of the local ﬁeld potential (LFP) (Laurent and Davidowitz, 1994; Wehr and Laurent, 1996) and produces slower temporal patterns of excitation and inhibition during prolonged stimulation (Bazhenov et al., 2001; Laurent and Davidowitz, 1994; Wehr and Laurent, 1999). The representation of olfactory stimuli in the locust AL thus is distributed across an evolving ensemble of PNs, a coding space that employs both the identity of responding neurons and the temporal evolution of the response eventually to produce a unique output for each of several similar, correlated inputs (Laurent, 1999, 2002; Mazor and Laurent, 2005; Stopfer et al., 2003). As described above (Section 5.1), each ORC in S. americana terminates in 10–25 small glomeruli, and the dendrites of a PN sample from 10 to 25 of these channels. Although it is not yet known how many different ORs are expressed in the ORCs of this species of locust, this arrangement theoretically could produce a unique ORC input proﬁle for each of the approximately 830 PNs in the AL (Laurent et al., 1996a,b; Leitch and Laurent, 1996). The lack of glomeruli representing unique channels with redundant output neurons thus allows for a huge expansion of the available coding space between input to and output from the AL. The tuning of an individual ORC is necessarily limited by the interaction between its OR and the volatile ligands it binds. A PN that samples from several ORCs thus can have a greatly broadened molecular receptive range. Furthermore, a unique combination of responsive PNs, synchronized across many cycles of a common oscillatory signal, can be produced for stimuli with small differences in the ORC response (Laurent, 2002). Although LFP oscillations have been recorded in the ALs of moths and ﬂies, most spikes in AL PNs in these species are not fully entrained to LFP oscillations (Christensen et al., 2003; Heinbockel et al., 1998; Ito et al., 2009; Tanaka et al., 2009; Turner et al., 2008; Wilson and Laurent, 2005). Importantly, while locust PNs produce spikes at or below the 20-Hz frequency of the LFP oscillation, PNs in M. sexta (c.f. Heinbockel et al., 1998; Ito et al., 2009; Reisenman et al., 2005; Vickers et al., 1998), D. melanogaster (Bhandawat et al., 2007), and A. mellifera (Müller et al., 2002) can respond to olfactory stimuli at much higher frequencies, up to ca. 200 s1. When responding to a natural plume of volatiles, M. sexta PNs typically respond with short bursts of spikes and an instantaneous spiking frequency of ca. 100 Hz (Vickers et al., 2001). The ﬁring rate of single PNs in several insects other than locusts roughly scales with stimulus concentration (see Section 6.1) (Bhandawat et al., 2007; Bichao et al., 2005; Reisenman et al., 2004, 2005; Schlief and Wilson, 2007; Silbering et al., 2008; Wilson et al., 2004), while individual S. americana PNs respond at roughly the same ﬁring rate across concentrations (Assisi et al., 2007; Stopfer et al., 2003). Instead, ensembles of S. americana PNs respond to different concentrations of the same olfactory stimulus along trajectories that, while similar, evolve into distinct representations over time (Stopfer et al., 2003). Thus, while LFP oscillations exist in the ALs of several species, the strict phase-locked code observed in S. americana is not evident in other insects and furthermore is not easily compatible with frequency coding of stimulus concentration. Firing synchrony among PNs in moths (M. sexta: Christensen et al., 2003, 2000; Ito et al., 2009; Lei et al., 2002) and ﬂies (D. Author's personal copy 122 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 melanogaster—the only other species studied in detail: Tanaka et al., 2009; Turner et al., 2008; Wilson and Laurent, 2005) is not fully entrained to the LFP. In response to a brief olfactory stimulus, mimicking a pulse of volatiles encountered in a natural plume (Vickers, 2006c), PNs in moth ALs produce high-frequency spikes early in the response that are synchronized, and then synchrony falls off quickly thereafter (Christensen et al., 2003; Lei et al., 2002). This has been examined most closely in the sex-pheromone speciﬁc MGC in ALs of male M. sexta. In this subsystem, each of the two key components of the female’s sex-pheromone mixture activates PNs in one of the two largest MGC glomeruli and inhibits PNs in the other (Christensen and Hildebrand, 1997; Lei et al., 2002). Stimulation with the natural mixture, thus activating both the inhibitory and excitatory inputs to MGC PNs, increases ﬁring synchrony (Lei et al., 2002). Similar synchrony has been observed in the main AL in response to natural mixtures of ﬂoral volatiles, depends on the identity and intensity of volatiles, and occurs in a unique pattern for individual odors (Christensen et al., 2000; Lei et al., 2004; Riffell et al., 2009). Synchrony may subserve yet another function in the ALs of moths. Synchronous ﬁring between neurons is enhanced for pheromone (Lei et al., 2002) or plant-odor (Riffell et al., 2009) stimuli that are innately attractive. This may represent a common encoding dimension for behaviorally signiﬁcant odors from diverse sources such as food or conspeciﬁc mates (Martin and Hildebrand, 2010). Multiple stimuli of longer duration (>1 s) at long (20 s) intervals evoke a two-stage response in PNs in the plant-odor responsive glomeruli of M. sexta (Ito et al., 2009). After a brief period of high-frequency ﬁring, the PN exhibits prolonged, temporally complex, lower-frequency ﬁring. The LFP in the mushroom body similarly shifts from high- to low-frequency. Under similar stimulus conditions, PNs in the ALs of D. melanogaster also transition from high- to low-frequency spiking (Tanaka et al., 2009). In both species, individual PNs often ﬁre in phase with these oscillations but are much less entrained than are PNs of S. americana and typically ﬁre at higher frequency than that of the LFP. It seems, therefore, that control of ﬁring phase by inhibition from LNs is much weaker in ALs of M. sexta and D. Melanogaster than in those of S. americana and at most only weakly entrains PN ﬁring to a global oscillation. We further discuss the implications of the stimulus regimen necessary to generate phaselocking in these animals, similar to that in locusts, in Section 6.4. Finally, although administration of the GABAA-receptor antagonist picrotoxin (PTX) disrupts LFP oscillations in the AL of A. mellifera and increases the likelihood that A. mellifera generalizes between closely related odors (Stopfer et al., 1997), more recent work has shown that PTX also allows additional, weakly stimulated glomeruli to respond to an olfactory stimulus (Sachse and Galizia, 2002). Based on this side-effect, treatment with PTX has the potential to disrupt a neural code based either on oscillatory synchronization of PNs or on the identity of responsive PNs, making behavioral pharmacological experiments difﬁcult to interpret. Similar work on moths (M. sexta) demonstrates that PTX disrupts discrimination (behaviorally distinct from increasing generalization) between both similar and dissimilar odors, a ﬁnding that is also consistent with physiological data (Mwilaria et al., 2008). Across species, behavioral evidence for the importance of LFP oscillations in olfactory coding remains elusive. 6.4. Encoding temporal features of olfactory stimuli A ﬂying insect experiences olfactory stimuli as brief, discontinuous pulses, as its antennae encounter ﬁlaments of volatiles in a plume. Encoding these spatio-temporal features of a stimulus is necessary for the ﬂying insect to follow a plume to its source (Cardé and Willis, 2008). In this section, we consider how the olfactory 441 system encodes such brief, stochastic stimuli. We also suggest how differences in the spatio-temporal structure of stimuli important to an insect may shape processing of olfactory information. The ALs of moths exhibit several adaptations that improve the resolution of pulses in a sex-pheromone plume, subserving the vital task of locating the source, a calling, conspeciﬁc female moth. PNs in A. ipsilon ALs respond with extremely short lag to ORC input (Jarriault et al., 2010). In M. sexta, the ability of an MGC PN to follow the frequency and duration of pulses of sex pheromone is correlated with the strength of inhibitory input to the neuron (Christensen and Hildebrand, 1988; Heinbockel et al., 1999, 2004). Temporal ﬁdelity is further improved when a moth is stimulated with the correct ratio of the two key pheromone components, eliciting a speciﬁc balance of excitation and inhibition (Christensen and Hildebrand, 1997; Heinbockel et al., 2004). This combination of excitatory and inhibitory inputs causes some MGC PNs to respond with a shorter latency and higher rate of spiking, compared to stimulation with the excitatory component alone. Temporal ﬁdelity is also enhanced by longer-lasting inhibition following a burst of spikes, during which spontaneous activity, but not responses to subsequent pulses of pheromone, is reduced (Christensen et al., 1998; Lei and Hansson, 1999). Disrupting this inhibition impairs a moth’s ability to follow a pheromone plume to its source (Lei et al., 2009). ORC-to-PN synapses in D. melanogaster also act as high-pass ﬁlters, combining a high probability of neurotransmitter release with rapid synaptic depression to long-lasting stimuli to emphasize the onset of an olfactory stimulus (Bhandawat et al., 2007; Kazama and Wilson, 2008). Intracellular recordings in D. melanogaster (Kazama and Wilson, 2008) and the moth A. ipsilon (Jarriault et al., 2010), as well as imaging of ORC and PN activity and intracellular recording of PNs in A. mellifera (Galizia and Kimmerle, 2004; Sachse and Galizia, 2003), reveal that PNs respond to the rising phase of the ORC response, and PN responses decline before the peak of ORC activity. It is likely that the inhibitory network, along with the properties of synapses in the AL, improves the ability to encode rapid spatio-temporal ﬂuctuations of a plume of volatiles. The responses of KCs in the MB also reﬂect the emphasis on the initial, brief contact with volatiles, and thus the brief availability of information about olfactory stimuli. The responses of KCs in M. sexta (Ito et al., 2008) and A. mellifera (Szyszka et al., 2005) occur within a few milliseconds of the onset of a stimulus pulse, followed by an unresponsive period. The output neurons of the MB in A. mellifera respond with patterns differentiating rewarded from unrewarded odors, delayed only tens of milliseconds from the AL input to the MB (Strube-Bloss et al., 2011). A coding scheme in which olfactory stimuli are recognized on the basis of the identity and relative activation of PNs associated with individual glomeruli, as in classic population coding (Sanger, 2003), consolidates much of the information in the initial response. This could allow rapid, robust classiﬁcation of olfactory stimuli in a plume, where brief, unpredictable pulses of volatiles are encountered. Evidence for rapid behavioral responses to olfactory stimuli in ﬂies (Budick and Dickinson, 2006) and honey bees (Fernandez et al., 2009; Wright et al., 2009) suggests that the information contained in short encounters with stimuli is all that is necessary for some behaviors. The responses of KCs in S. americana, however, are not limited to the onset of the stimulus. An individual KC requires a large number of coincident PN inputs in order to ﬁre, and this criterion may not be reached in most or even all cycles of PN responses to olfactory stimuli (Jortner et al., 2007; Perez-Orive et al., 2004, 2002). Oscillatory PN input also drives feed-forward inhibition to KCs in S. americana, preventing them from ﬁring in all but a brief part of the cycle (Perez-Orive et al., 2002). The net result of these processes is stimulus-driven KC activity consisting of one or a few spikes, in Author's personal copy 123 442 J.P. Martin et al. / Progress in Neurobiology 95 (2011) 427–447 stimulus-speciﬁc temporal patterns (Stopfer et al., 2003), during even a sustained olfactory stimulus. The identity of a stimulus thus is encoded ﬁrst in a distributed population of PNs and then in a sparse population of KCs, in which the membership of both populations evolves over several cycles of the oscillatory signal. An important outcome of this processing scheme is the theoretical ability of the olfactory system of S. americana to produce a unique code for each of many similar stimuli, which could in turn be associated with a variety of other information about a source of volatiles (Laurent, 2002). The locust S. americana therefore instantiates a unique code in a unique olfactory network, distinct from those of other insects such as sphinx moths, fruit ﬂies, and honey bees. While olfactory information is concentrated early in a stimulus pulse for those species, representations of olfactory stimuli become more distinct over time in both the AL and MBs of S. americana (Perez-Orive et al., 2002; Stopfer et al., 2003). We suggest that this difference may be adaptive in the natural contexts in which acridids such as locusts use olfactory information. The olfactory systems of moths, bees, and ﬂies appear to be adapted to encode olfactory stimuli upon brief, stochastic encounters with discontinuous pulses in plumes of volatiles. Locusts, however, impose an internal timing signal to produce a reliable temporal code throughout prolonged contact with a stimulus (Laurent, 2002). The addition of the temporal dimension in the olfactory coding scheme used by locusts can greatly expand the coding space allotted for classifying stimuli. As opposed to a code based on the identity and relative activation of responding glomeruli (Galan et al., 2004; Sachse and Galizia, 2003), integration over many cycles creates an exponentially large number of ‘virtual’ PN populations, allowing for a different active ensemble at each cycle (Laurent et al., 2001). This increased coding space may be adaptive for a voracious, generalist insect such as a locust. Locusts have a wide range and a diverse diet of vegetation (Simpson and Raubenheimer, 2000). Vegetative tissues of plants emit complex, species-speciﬁc mixtures of volatiles, both when intact and when damaged as the insects feed (Bruce et al., 2005). Locusts can associate volatiles with the nutritional content of their source (Behmer et al., 2005) and can use associations to balance their diet (Dukas and Bernays, 2000; Raubenheimer and Tucker, 1997). There is little evidence for olfaction-guided aerial navigation in the Acrididae (Helms et al., 2003), and several authors have suggested that locust foraging is primarily visually driven (Chapman, 1988). Thus, for locusts, exposure to signiﬁcant olfactory stimuli occurs primarily in the context of prolonged feeding bouts in close contact with the source of volatiles, allowing for full use of the coding space available in the temporal structure of olfactory responses. Although other insects encounter volatiles in similar circumstances, aerial foragers must rely more frequently on identiﬁcation of brief, stochastic pulses of volatiles and therefore require a coding system adapted to the spatio-temporal structure of a plume. Finally, observations of PN spikes phase-locked to LFP oscillations in D. melanogaster and M. sexta are instructive (Ito et al., 2009; Tanaka et al., 2009). Oscillations emerged only after multiple (>10), 2-s pulses of an olfactory stimulus delivered at a lower airspeed than used in other investigations. Oscillatory synchrony was also observed after an initial, phasic spiking response to olfactory stimuli, suggesting that the two coding schemes may operate in parallel in a single species. Perhaps encoding based on oscillations requires longer contact with a stimulus, such as may occur when an animal is feeding from a ﬂower. A ﬁnal reconciliation of these two schemes would require a mechanism wherein encoding of stimuli associated with reward under conditions of long, repeated exposure (facilitating oscillations) are transformed to allow identiﬁcation under odor-plume conditions that do not allow for oscillations to develop. Honey bees appear to be capable of this transformation. Bees trained to long pulses of an odor perform well in a discrimination test, even when tested with short pulses of the odor (Fernandez et al., 2009). In summary, we suggest that a trade-off is likely between a coding scheme optimized for discriminating fewer olfactory stimuli while ﬂying and one that can produce unique, decorrelated codes for volatiles from many, highly similar food sources upon prolonged exposure to the stimuli. 7. Concluding remarks Progress to date in insect olfactory neurobiology and neuroethology points to future direction for the ﬁeld. We suggest that the principles and models of insect olfaction will be expanded and elaborated as the range of variation among insect species is considered. A comparative approach, grounded in natural behavior, can resolve conﬂicting results and guide inquiry in fruitful directions. Future progress will be made through not only new discoveries but also direct comparison between species when possible. In evaluating comparative work, however, one must always be wary that ﬁndings in one species may not be applicable to another. 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Dev. 37, 469–479. Zwiebel, L.J., Takken, W., 2004. Olfactory regulation of mosquito-host interactions. Insect Biochem. Mol. Biol. 34, 645–652. 129 APPENDIX B: INNATE RECOGNITION OF PHEROMONE AND FOOD ODORS IN MOTHS: A COMMON MECHANISM IN THE ANTENNAL LOBE? Reprinted from Frontiers in Behavioral Neuroscience, Vol. 4, Martin, J. P., Hildebrand, J. G., Innate recognition of pheromone and food odors in moths: A common mechanism in the antennal lobe?, 2010. The author is the sole copyright holder of this work. 130 Review Article published: 24 September 2010 doi: 10.3389/fnbeh.2010.00159 BEHAVIORAL NEUROSCIENCE Innate recognition of pheromone and food odors in moths: a common mechanism in the antennal lobe? Joshua P. Martin* and John G. Hildebrand Department of Neuroscience, University of Arizona, Tucson, AZ, USA Edited by: Kathleen A. French, University of California San Diego, USA Reviewed by: Charles E. Linn, Cornell University, USA Christoph Kleineidam, University of Konstanz, Germany *Correspondence: Joshua P. Martin, Department of Neuroscience, University of Arizona, Gould-Simpson Room. 611, 1040 E 4th Street, Tucson, AZ 85716, USA. e-mail: [email protected] The survival of an animal often depends on an innate response to a particular sensory stimulus. For an adult male moth, two categories of odors are innately attractive: pheromone released by conspecific females, and the floral scents of certain, often co-evolved, plants.These odors consist of multiple volatiles in characteristic mixtures. Here, we review evidence that both categories of odors are processed as sensory objects, and we suggest a mechanism in the primary olfactory center, the antennal lobe (AL), that encodes the configuration of these mixtures and may underlie recognition of innately attractive odors. In the pheromone system, mixtures of two or three volatiles elicit upwind flight. Peripheral changes are associated with behavioral changes in speciation, and suggest the existence of a pattern recognition mechanism for pheromone mixtures in the AL. Moths are similarly innately attracted to certain floral scents. Though floral scents consist of multiple volatiles that activate a broad array of receptor neurons, only a smaller subset, numerically comparable to pheromone mixtures, is necessary and sufficient to elicit behavior. Both pheromone and floral scent mixtures that produce attraction to the odor source elicit synchronous action potentials in particular populations of output (projection) neurons (PNs) in the AL. We propose a model in which the synchronous output of a population of PNs encodes the configuration of an innately attractive mixture, and thus comprises an innate mechanism for releasing odor-tracking behavior. The particular example of olfaction in moths may inform the general question of how sensory objects trigger innate responses. Keywords: floral scent, moths, neuroethology, olfaction, pheromone, sensory coding, sensory object, synchrony Introduction The olfaction-based behavior of an animal is governed by a tradeoff: focusing on a narrow range of innately attractive odors efficiently guides the animal to mates and food sources that are most likely to be rewarding, while a flexible olfactory system that explores new odors and associates new rewards with them is resistant to changes in the availability of any one source (Waser et al., 1996; Chittka et al., 1999; Memmott et al., 2004). For a moth emerging from its pupal stage, with only a few days to a few weeks of adult life ahead of it to eat and mate, coming prepared with pre-programmed search images for the mates and night-blooming flowers they are likely to encounter, along with a capacity to learn new sources of food, could be highly valuable. Most moths take flight at night, when the utility of visual signals is at a nadir. As nocturnal flyers, moths rely mainly on olfaction (Balkenius et al., 2006). Chemical cues released into the air can guide moths to sources of food or mates over long distances (Wall and Perry, 1987), and are behaviorally active at remarkably low concentrations. For example, only a few molecules of pheromone or plant odors can trigger increased heart rates in moths (Angioy et al., 2003). Here, we review neuroethological findings about selected, innate mate-seeking and feeding behaviors of moths and ask if there is a common operating principle for a neural substrate underlying recognition of an innately attractive odor. We propose that for both sex pheromone and food odors, the circuitry of the antennal lobe (AL) produces a pattern of coordinated output for attractive odors Frontiers in Behavioral Neuroscience from either category. Our review is not exhaustive but intends to make a case for the existence of such a mechanism in many, if not all moths. Species Recognition by Pheromone Blends Animals are, by necessity, specialists when searching for mates. The consequences of pursuing a mate from a related but reproductively isolated species are dire for animals that expend much energy in long-distance flights (Bartholomew and Casey, 1978). Moths have evolved a system of sex-pheromonal communication between calling females (senders) and conspecific males (receivers), incorporating the production of a suite of chemicals by females. These consist, for the most part, of aliphatic acetates, alcohols, and/or aldehydes with hydrocarbon chains 10–22 carbon atoms in length and often with one or more double bonds (Byers, 2005). This chemical alphabet allows for numerous unique molecules to be used as components of signals. Considering only the possible variations of the most commonly used molecules, Byers (2005) estimates there are over 100,000 possible pheromonal volatiles. Here, we review the evidence from the peripheral reception of pheromone odors in moths that suggests the existence of a template in the AL for the recognition of complex odors. We particularly emphasize how changes in behavioral selectivity that follow changes in the periphery can be understood as an alteration of the input to a pre-existing AL template. In the proceeding sections, we will describe the evidence for the nature of this template. www.frontiersin.org September 2010 | Volume 4 | Article 159 | 1 131 Martin and Hildebrand Synchrony encodes innately attractive odors The use of fine variations on a single molecular theme requires a male receiver to have sufficiently specific receptors to accept certain variants and reject all others. The antennae of male moths carry olfactory organules – olfactory sensillacontaining olfactory receptor cells (ORCs) that respond with such specificity. For instance, ORCs of one phenotype present in a particular species respond specifically to a 14-carbon acetate with a double bond at carbon 11 in the cis configuration, and not to the trans isomer (Wanner et al., 2010). Pheromone-binding proteins in the lymph that bathes ORCs in antennal sensilla can increase the specificity of receptor responses further (GrosseWilde et al., 2006). Most moth species employ multi-component pheromone mixtures instead of monomolecular signals (Byers, 2006). Two factors likely necessitate this level of complexity. First, genera with closely related, sympatric species often have sex pheromones with at least one component in common (Byers, 2006). Second, there are limits to the specificity of an ORC, and there are many examples of ORCs in one species that respond to pheromone components used by other species (Grant et al., 1989; Lofstedt, 1990; Berg et al., 1995; Domingue et al., 2007b, 2008). This may reflect an upper limit to the ability of a receptor to reject molecules that are highly similar to the preferred ligand and would lead to ambiguity between a non-preferred ligand at high concentration and a preferred ligand at low concentration. Related moth species also may use pheromone mixtures with identical components, but in ratios specific to each species (Baker, 2008). A pattern is emerging that describes a large number of moth pheromone mixtures: a “major” component (providing a majority of molecules in the blend) and one or more “minor” components (often present at a much lower concentration but nevertheless required to elicit behavior in a recipient moth). Moreover, attraction to a pheromone blend may be inhibited by “antagonistic” compounds in the pheromone mixtures of other species (Baker and Heath, 2004). Species recognition thus requires a form of rudimentary pattern recognition, dependent on the arrangement of features in a complex stimulus, in the olfactory system of a male moth (Baker, 2008). Clues about the nature of the pattern-recognition mechanism come from several observations of individual moths for which the parameters of an acceptable pattern have shifted. Accompanying these behavioral shifts are some revealing changes in the responses of the ORCs to pheromone blends typically rejected by “normal” moths. In a laboratory colony of Trichoplusia ni, a strain spontaneously arose in which females emit roughly equivalent amounts of the major and minor pheromone components instead of the typical 1:100 ratio (Haynes and Hunt, 1990). Over several generations, males emerged that were attracted to the mutant blend (Liu and Haynes, 1994). In normal males, the ratio between the major and minor components is reflected in the ratio between responses of the corresponding “major” and “minor” ORCs (Domingue et al., 2009). In the males evolved to accept the mutant blend, the response of the minor ORC was decreased, such that the nearly equal ratio between components still produced an unequal ratio of responses between the types of ORCs (Domingue et al., 2009). The ratio of ORC responses of evolved males to the mutant blend was thus similar to that of normal males to the normal blend. Frontiers in Behavioral Neuroscience Another case study provides an additional clue. In males of the species Ostrinia nubilalis, ORCs for the major and minor components of the conspecific sex-pheromone blend also respond weakly to the pheromone components of a related species, O. furnacalis, which differ only in the position of the double bond (Domingue et al., 2007a). This illustrates the utility of encoding schemes that depend on more than just the presence of a particular pheromone molecule (i.e. “labeled-line” coding), as a high concentration of O. furnacalis components would be indistinguishable from a low concentration of conspecific components. In contrast, the relative activity of neurons responding to an odor is typically consistent across concentrations, and is hypothesized to underlie “concentration invariant” encoding of odor identity (Cleland et al., 2007; Uchida and Mainen, 2007; Asahina et al., 2009). Despite the incomplete specificity of their ORCs, only rare O. nubilalis males are attracted to the O. furnacalis pheromone, in which the major and minor components are present in an approximately 1:1 ratio (Linn et al., 2003). Major and minor ORCs in both normal and rare males have similar sensitivity to the major and minor components of the conspecific pheromone and thus produce a ratio of responses congruent with the 99:1 ratio of components in an attractive blend (Domingue et al., 2007a). Responses to the heterospecific O. furnacalis components in normal males also reflect the 1:1 ratio of components in the pheromone of that species, allowing the animal to discriminate between the blends. In the rare males, the response of the minor ORC to the heterospecific pheromone component is greatly diminished, producing a response closer to 99:1 when presented with a 1:1 stimulus and thus facilitating a behavioral response to the odor (Domingue et al., 2007a). These studies and others on peripheral changes in pheromone processing suggest the existence of an internal template for the ratio of components in a conspecific pheromone blend (Baker, 2008). In both examples, sensitivity in the periphery changed, producing a response to a new mixture that had a ratio of major and minor ORC activation similar to that observed in response to the conspecific blend. Where might the template for such a pattern be located? The axons of pheromone-responsive ORCs terminate in the AL in a set of large, male-specific glomeruli called the macroglomerular complex (MGC) (Matsumoto and Hildebrand, 1981). An additional clue from the periphery suggests that the template exists there, at least in part. The species O. nubilalis comprises two strains, differing only in the ratio of components produced by females and attractive to males: an “E-strain” and a “Z-strain,” named for the isomer of the major pheromone component (Carde et al., 1978; Anglade and Stockel, 1984). The major component for one strain is the minor component for the other, and vice versa. In both strains, major ORCs terminate in the larger of two glomeruli in the MGC, and minor ORCs in the smaller (Karpati et al., 2008). This arrangement is also found in male moths of the subfamily Heliothinae. Evidence from both the input ORCs (Berg et al., 1998, 2005; Galizia et al., 2000; Lee et al., 2006a,b) and output projection neurons (PNs) (Christensen et al., 1995b; Vickers and Christensen, 1998, 2003; Vickers et al., 1998) in the MGC demonstrates that the major component, shared across all four species studied in detail, is processed in the largest glomerulus, called the cumulus. One or more minor components, and in some species a component www.frontiersin.org September 2010 | Volume 4 | Article 159 | 2 132 Martin and Hildebrand Synchrony encodes innately attractive odors that antagonizes behavior, are processed in smaller glomeruli s urrounding the cumulus. The proximity of these glomeruli, and the conserved functional relationship across species, suggests that they are incorporated into a conserved network at the level of the AL that performs the initial processing necessary for species-specific pattern recognition. The network architecture of the AL consists primarily of inhibitory, GABAergic neurons that have arborizations throughout the AL (Anton and Homberg, 1999). These local interneurons (LNs) connect the glomeruli of the MGC and facilitate reciprocal inhibition between them (Waldrop et al., 1987; Christensen et al., 1993; Christensen and Hildebrand, 1997; Lei et al., 2002). This is best established in Manduca sexta, a species for which two pheromone components in a 1:2 ratio are necessary and sufficient for attraction of male moths to the source of the stimulus (Tumlinson et al., 1989). Stimulation with one component activates ORCs projecting to one glomerulus (Kaissling et al., 1989; Christensen et al., 1995a), PNs arborizing in that glomerulus (Christensen and Hildebrand, 1987; Hansson et al., 1991), and LNs arborizing in both glomeruli (Christensen et al., 1993), and inhibits the background firing of PNs in the MGC glomerulus activated by the other component (Christensen and Hildebrand, 1997; Lei et al., 2002). Information about the presence, and potentially the quantity, of each component is thus transmitted between glomeruli. The effect of these inhibitory inputs is not to reduce the output carried by PNs in response to a blend of both pheromone components, but rather to increase the coordination, or synchrony, of their action potentials (Lei et al., 2002). Synchrony between spikes produced by PNs arborizing in the same glomerulus, but not by PNs arborizing in neighboring glomeruli, increased in response to the blend. This result is similar to what is seen in the Drosophila AL, though in that system the effect does not rely on interglomerular inhibition (Kazama and Wilson, 2009). In contrast, the degree of synchrony between moth MGC PNs is correlated with the strength of inhibition they receive from the neighboring glomerulus (Lei et al., 2002). Synchrony provides an additional coding dimension (Singer, 1999; Biederlack et al., 2006) in the output of the MGC, such that the presence and intensity of each pheromone component can be encoded by the rate of firing of individual PNs, while the presence of both components together in a mixture is encoded in the coordination of firing of PNs. Conceptually, synchronous firing of two neurons can be thought of as a new, active, virtual neuron that is more effective in driving responses down-stream and more selective to behaviorally relevant mixtures than either of the neurons that produce it (Ghose et al., 1994). Synchrony, typically shaped by inhibitory networks, has been investigated and debated for years as a possible mechanism for “binding” the features of a complex stimulus to produce a unitary representation (Engel et al., 1992; Engel and Singer, 2001; Lestienne, 2001; Robertson, 2003; Averbeck and Lee, 2004). We propose that the inhibitory network linking MGC glomeruli provides the mechanism by which the features of an encountered pheromone mixture are compared to an internal template for the conspecific mixture, and the output of synchronous spikes encodes a blend that fits this template. Frontiers in Behavioral Neuroscience Innately attractive floral odors and synchronous codes Beyond the enlarged glomeruli of the MGC in the AL of a male moth lie a larger number of “ordinary” glomeruli, in a region called the main AL (Anton and Homberg, 1999). These glomeruli process sensory input about volatiles from flowers (sources of nectar) and foliage of host plants (on which females lay eggs) (Galizia et al., 2000). Although more is known about the pheromone-processing pathways of the MGC, emerging evidence suggests that the processing of innately attractive, complex odors in both regions of the AL share fundamental traits. While the MGC and main AL mediate different olfactory behaviors, there are many similarities between the two compartments (Christensen and Hildebrand, 2002). However, evidence from calcium-imaging (Galizia et al., 2000; Carlsson et al., 2002; Hansson et al., 2003) indicates that aggregate activity (calcium activity representing input, local circuitry, and output) in response to pheromone and plant odors is isolated to the MGC and main AL, respectively. Also, intracellular recordings (Reisenman et al., 2008) suggest that while the main AL receives significant inhibition originating from the MGC, the MGC receives no inhibition from some glomeruli, and only receives inhibition when the AL is activated by high concentrations of floral odors. Thus, the two are functionally separate compartments that may interact when the animal encounters both pheromone and floral odors. A similar arrangement is suggested by the anatomical arrangement of glomeruli in the antennal lobe of Drosophila, where classes of local neurons arborize in glomeruli across the AL, but avoid those involved in processing putative pheromone odors (Wilson and Laurent, 2005; Chou et al., 2010). While pheromonal stimuli are the gold standard for innate, olfaction-based attraction and discrimination, naive moths are also innately attracted to the scent of certain flowers in preference to others (Plepys et al., 2002; Raguso and Willis, 2002; Riffell et al., 2008). The co-evolution of flowers and their moth pollinators is most remarkable in flowers pollinated by hawk moths (Grant and Grant, 1983). Those flowers have long, slender nectaries accessible to the moth’s long proboscis (Darwin, 1862; Nilsson, 1988), large white, reflective surfaces (Raguso et al., 2003), and a strong, sweet fragrance. M. sexta moths exhibit innate attraction to the scent of the flowers of Datura wrightii, which is seasonally abundant in part of their range (Raguso and Willis, 2002; Riffell et al., 2008). Although this flower releases a mixture of volatiles consisting of more than 65 components (Raguso et al., 2003), neurons in the AL respond robustly to only nine of those compounds (Riffell et al., 2009a), and a mixture of just three of these volatiles is sufficient to attract naive moths (Riffell et al., 2009b). Thus the initially daunting complexity of a floral scent may be reduced in the olfactory processing of an animal to something more closely approximating that of a pheromone blend. A behavioral focus on a reduced subset of volatiles in a complex mixture also has been observed recently in honey bees (Reinhard et al., 2010). Owing perhaps in part to the more broadly tuned ORCs that innervate the main AL (Wang et al., 2003; Hallem and Carlson, 2006), mixtures of plant volatiles activate a significantly larger number of glomeruli (Lei et al., 2004; Skiri et al., 2004; Pinero et al., 2008) than do pheromones. Nevertheless, simultaneous recordings from neurons across the AL show that in response to an innately attractive floral scent, a pattern of firing synchrony emerges (Riffell et al., 2009b). This www.frontiersin.org September 2010 | Volume 4 | Article 159 | 3 133 Martin and Hildebrand Synchrony encodes innately attractive odors pattern is conserved in response to attractive blends with reduced numbers of components and is distinct from patterns generated by non-attractive mixtures and single components (Riffell et al., 2009b). Attractive odors also generate a distinctive pattern of firing rates across the ensemble of neurons. However, by using a shift-predictor measure of synchrony (Perkel et al., 1967), the authors ensure that the measures of synchrony and firing rate are independent. Thus both firing rate and synchrony may independently encode the presence of attractive floral scents (Riffell et al., 2009b). Synchrony, but not firing rate, is maintained across a range of concentrations that were detectable and attractive to the animal, providing a neural correlate of “concentration invariance” (Riffell et al., 2009a). An ensemble of synchronously responding neurons, innervating a larger number of glomeruli, thus is involved in encoding an innately attractive floral odor in a manner similar to that observed with sex pheromone in the MGC. A fairly superficial analysis of the output of the AL thus has suggested that pheromonal and plant-odor processing share common mechanisms, wherein innately attractive mixtures of volatiles activate innate templates in the AL network, producing synchronous output among PNs. Particular patterns of synchrony are correlated with innate olfactory behaviors, and are absent in response to stimuli that are not attractive (Riffell et al., 2009b). Definitive proof of this hypothesis will require a pharmacological or genetic manipulation that disrupts synchrony in response to a normally attractive odor, and consequently behavior. As local neurons arborize similarly among glomeruli in the main AL and in the MGC (Hoskins et al., 1986), it is likely that similar networks of reciprocal inhibition are involved in both regions of the AL. Indeed, data from studies of inhibition between glomeruli suggest a possible mechanism. For a small number of glomeruli that have been tested, interglomerular inhibition is not symmetrical (Reisenman et al., 2008). Data from calcium-imaging studies of honey bees and Drosophila melanogaster suggest that the strength of inhibitory connection is specific to each pair of glomeruli (Sachse and Galizia, 2002, 2003; Linster et al., 2005; Silbering and Galizia, 2007). A network of inhibitory connections, set according to some genetic program, could transform ORC inputs responding to a range of innately attractive odors into particular patterns of synchronized PN output, to be read at higher levels of processing. Like honey bees, moths can learn readily to associate odors with rewards by both classical conditioning (Hartlieb, 1996; Fan et al., 1997; Daly and Smith, 2000) and in more naturalistic protocols related to foraging (Cunningham et al., 2004; Riffell et al., 2008). Evidence for learning in the wild also exists, as moths are found to feed from flowers to which they are not innately attracted when the preferred, innately attractive flower is scarce (Riffell et al., 2008). Simultaneous recordings from moths learning to associate an odor with a sucrose-solution reward reveal that neurons are recruited into the ensemble encoding the rewarded odor (Daly et al., 2004). Further research will clarify whether these changes make the representation of a learned odor more similar to an innately attractive odor. Conclusions We have reviewed evidence that innate odor attraction in moths is mediated by mechanisms in the AL that recognize and respond to the configuration of a complex odor. In both the specialized, pheromone-processing MGC and the more generalized, plant- Frontiers in Behavioral Neuroscience odor-processing main AL, innately attractive odors produce patterns of synchronous output. We have presented the available evidence that synchrony is the feature of AL output that encodes the innate salience of an odor. The mechanism underlying this firing synchrony is unknown, but future investigations can benefit from comparisons between pheromonal- and plant-odor-coding networks. It is important to note that this function of the AL does not preclude other functions, such as lateral inhibition, decorrelation, and gain control (Wilson and Mainen, 2006), which may occur in tandem. Nor does it invert the tendency to attribute too little to the AL by attributing too much, as there are certainly more processes in higher olfactory centers linking stimulus and behavior. Our model of processing of innately attractive odors in the AL is depicted in Figure 1. Both sex pheromone and plant odors consist of mixtures of components present in various proportions (grayscale and colored dots in Figures 1A,E, respectively). A moth apparently requires only a subset of these volatiles to initiate innate behaviors. In the male-specific, pheromone-processing subsystem, each of multiple highly specific receptors (represented by various shades of gray in Figure 1B) responds to only one of the components of the mixture. In contrast, receptors in the plant-odor-processing subsystem are variously selective (indicated by the color of the ORN in Figure 1F) and sensitive (indicated by the saturation of the color) to one or more components of the plant odor. Plant odors thus are represented across a population of ORCs. A map of the connectivity of ORCs to their main glomerular targets in the AL is not yet available in detail. Pheromone-responsive ORCs provide synaptic input to the large glomeruli of the MGC (Figure 1C), where LNs (represented by blue arrows) mediate reciprocal inhibition between glomeruli. Although most moths have 3–4 MGC glomeruli, we currently have evidence for only the interaction of two MGC glomeruli in encoding a pheromone mixture. In response to stimulation with the pheromonal mixture, PNs in each MGC glomerulus produce more synchronous spikes (red raster lines superimposed on gray and black arrows in 1C) with other PNs in the same glomerulus. Similarly, an innately attractive odor produces a pattern of synchrony in the main AL (indicated by black lines in Figure 1G). The output of each subsystem (green arrows in Figures 1D,H) that encodes the odor is thus a pattern of synchrony between PNs in the same glomerulus for pheromone odors (Figure 1D) and across multiple, heterogeneous PNs in the main AL (Figure 1H). The particular features of this scheme, i.e. the importance of the configuration of a complex stimulus and encoding of higher dimensions of a stimulus via synchrony, parallel those uncovered in other work (Meister, 1996; Dan et al., 1998; Krahe et al., 2002; Cleland et al., 2007; Uchida and Mainen, 2007; Marsat et al., 2009; Avargues-Weber et al., 2010) and stems naturally from the observation that sensory systems are tuned, at various levels, to stimuli that are important for survival (Atick, 1992; Dusenberry, 1992). The fundamental similarities between olfactory information processing and storage in brains separated by hundreds of millions of years of evolution are becoming clearer and more numerous over time (Hildebrand and Shepherd, 1997; Davis, 2004; Ache and Young, 2005; Wilson and Mainen, 2006; Touhara and Vosshall, 2009). It seems likely that much of what is learned from moths will find parallels in other animals. www.frontiersin.org September 2010 | Volume 4 | Article 159 | 4 134 Martin and Hildebrand Synchrony encodes innately attractive odors Figure 1 | Processing of innately attractive odor mixtures in the sexpheromonal (A–D) and plant-odor (E–H) subsystems of the moth’s antennal lobe. (A) Sex pheromones of moths typically comprise a “major” component (black circles) and one or more “minor” components (gray and light gray circles). (B) Pheromonal ORCs respond specifically to one of the components of the pheromone (colors corresponding to component colors in A). (C) Pheromonal ORCs synapse in glomeruli (large ovals). Glomeruli are connected by LNs (blue arrows) that mediate reciprocal inhibition. The output of PNs in each glomerulus (gray and black lines with superimposed spike rasters) in response to the Acknowledgments The authors gratefully acknowledge the financial support of NIH grant DC-02751 (to J. G. Hildebrand) and NIH NRSA fellowship Frontiers in Behavioral Neuroscience pheromone includes a high proportion of synchronized spikes (red rasters). (D) Trains of synchronous spikes comprise the mixture-specific output of the MGC. (E) Plant odors typically include a large number of volatiles (colored circles), of which only a few may be necessary to elicit behavior. (F) Multiple ORCs respond to plant volatiles to varying degrees (colors represent specificity to correspondingly colored component from E, saturation of color represents sensitivity). (G) Stimulation with an innately attractive odor produces a pattern of synchrony (black lines) across the AL. (H) The mixture-specific output of the main AL is characterized by a pattern of synchronized firing of PNs from across the AL. DC97222 (to J. P. Martin). We also thank J.A. Riffell, H. Lei, A.M. Dacks, and A. Beyerlein for extremely helpful comments and discussion of the ideas in this review. www.frontiersin.org September 2010 | Volume 4 | Article 159 | 5 135 Martin and Hildebrand References Ache, B. W., and Young, J. M. (2005). Olfaction: diverse species, conserved principles. Neuron 48, 417–430. Angioy, A. M., Desogus, A., Barbarossa, I. T., Anderson, P., and Hansson, B. S. (2003). Extreme sensitivity in an olfactory system. Chem. Senses 28, 279–284. Anglade, P., and Stockel, J. (1984). Intraspecific sex pheromone variability in the European corn borer, Ostrinia nubilalis Hbn. (Lepidoptea, Pyralidae). Agronomie 4, 183–187. Anton, S., and Homberg, U. (1999). “Antennal lobe structure,” in Insect Olfaction, ed. B. S. Hansson (Heidelberg: Springer), 98–125. 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Citation: Martin JP and Hildebrand JG (2010) Innate recognition of pheromone and food odors in moths: a common mechanism in the antennal lobe?. Front. Behav. Neurosci. 4:159. doi: 10.3389/ fnbeh.2010.00159 www.frontiersin.org Copyright © 2010 Martin and Hildebrand. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited. September 2010 | Volume 4 | Article 159 | 8 138 APPENDIX C: DEVELOPMENT AND APPLICATION OF A METHOD TO IDENTIFY PROJECTION NEURONS AND LOCAL INTERNEURONS FROM EXTRACELLULAR SPIKE TRAINS Introduction Parallel recording of ensembles of neurons in the antennal lobe (AL) using multichannel recording has produced new insights into the processing and encoding of odor information (c.f. Dacks et al. 2008; Christensen et al. 2000; Riffell et al. 2009a, 2009b; Lei et al. 2004). However, in order to differentiate between activity related to mechanisms of internal processing, i.e. the network of local neurons (LNs) within the AL, from the activity that encodes odor information and forms the output to higher order centers, i.e. projection neurons (PNs), a reliable method of identifying these cells in multi-channel recordings is required. Here, we present a brief, technical description of the development of a method to differentiate neuron populations by characteristics of their unstimulated background activity, and an application of that method to several cells responding to a mixture of volatiles mimicking the floral odor of a major host-plant, Datura wrightii, and mixtures in which the relative proportion of one component has been altered. Several methods have been developed to address this issue in other neural systems (Sik et al., 1995; Klausberger., 2003; Henze et al., 2000; Csicsvari et al., 1998; Csicsvari et al., 1999; Bartho et al., 2004). These methods all rely on (1) independent identification of the cells recorded by the extracellular method, either by simultaneous intracellular recording and dye fills, excitatory and inhibitory interactions revealed by cross- 139 correlograms, or comparison of spike train traits to previously recorded and identified neurons, and (2) identification of characteristics in the extracellular waveforms or background spiking patterns that separate the types of cells into clusters in a multidimensional space. For this method, identification of cells as PNs or LNs relies on two factors. First is the observation that PNs tend to respond more selectively to odors than LNs, as they receive innervation from only one type of ORC while LNs arborize in many glomeruli and can thus be driven by input from multiple ORCs (Wilson and Laurent 2005; Chou et al. 2010; Reisenman et al. 2011). Second, the background activity of PNs is bursty, while LNs tend to fire more regularly (Lei et al. 2011). We therefore recorded the response of AL neurons to a panel of odorants, and characterized them as high, medium, low selectivity cells (Figure 1A. Using characteristics of the interspike interval histogram (peak location, and width at half height) that are related to the regularity of firing, we find two well-separated clusters, one of which contains all but one of the highly-selective cells. We provisionally label this cluster the PN cluster, and the cluster with majority low-selective cells the LN cluster (Figure 1B). Applying this method to AL responses to D. wrightii floral odor, we find PNs that respond selectively, with a higher firing rate (Figure 2A) or greater synchrony (Figure 2B), to mixtures in which the proportion of components mimics the proportion found in the natural odor. Methods Multi-channel recordings Animals were prepared as described for juxtacellular recording See Section 2.3.2), with one exception: because the multi-electrode probes are not as delicate as the 140 glass electrodes, and to maintain stability of the preparation, the antennal lobe was not desheathed. A 16-site silicon microelectrode (NeuroNexus, MI, USA) was inserted into the AL parallel with the antennal nerve. Ensemble activity was acquired using a Pentusa amplifier and OpenEx software (Tucker-Davis Technologies, FL, USA), digitized at 25 KHz, and high-pass filtered at 100 Hz. Action potential waveforms crossing a threshold were captured across all four sites on a shank using the tetrode recording technique (Gray et al., 1995). Spikes were sorted using Offline Sorter (Plexon, TX, USA) by a clustering algorithm that utilized waveform parameters including principle components, peak-valley ratio, linear and non-linear energy, and half-peak spike width. Units that were statistically separable (multivariate ANOVA; p<0.05) across 3 of the above parameters were used for further analysis. Spike timestamps were exported to Neuroexplorer (Nex Technologies, NC, USA) for further analysis. For each cell, 5 min of background, unstimulated firing was recorded along with responses to stimuli. 10 µl of individual floral volatiles or mixtures of volatiles diluted in mineral oil were added to a 1 cm by 1 cm square of filter paper in a 5 ml glass syringe. During stimulation, a solenoid switches a 0.1 ml/min stream of filtered air from a clean syringe to the stimulus syringe, delivering odor-laden air into a 1 l/min constant stream of clean air directed at the antenna ipsilateral to the AL from which recordings are made. Selectivity analysis For 6 animals, we recorded the response of a total of 30 cells to a set of floral volatiles. Ten monomolecular odorants were chosen: benzaldehyde (BEA); benzyl alcohol (BOL); farnesene (FAR); geraniol (GER); linalool (LIN); methyl salicylate 141 (MES); myrcene (MYR); nerol (NER); phenylacetaldehyde (PAA); cis-3-hexenyl propionate (ZHP). These volatiles belonging to three different odor classes: monoterpenoids (LIN, NER, MYR, FAR and GER), aromatics (BEA, BOL, MES and PAA) and aliphatic aldehydes (ZHP). Seven of the volatiles (BEA, BOL, FAR, GER, LIN, MES, MYR and NER) are components of the floral odor of a major host plant, Datura wrightii (Raguso et al. 2003, Riffell et al. 2009a). Floral volatiles were diluted to a concentration of 5µg/µl of mineral oil. For each cell, a peri-stimulus time histogram (Palm et al. 1988) was produced using NeuroExplorer. A response was scored as “excitatory” if, during the first second after odor onset, the firing rate in more than one consecutive 10ms bin exceeded the mean background firing rate plus 1.95 standard deviations. The mean firing rate over time was calculated as the mean of all bins during the response window that exceeded the threshold. From this response data, we calculate a selectivity index the lifetime sparseness (Sel): where ri is the response to stimulus i, and n is the number of stimuli in the series (Vinje and Gallant, 2000). This nonparametric statistic takes values between 0 (nonselective) and 1 (highly selective). Several parameters were calculated from the background firing of each recorded cell. Of these, characteristics of the ISI histogram were used for cluster analysis of the cells: the median ISI value, and the width at half-height of the ISI histogram. For both, histograms were smoothed by estimating the probability density function of the ISI 142 distribution (Matlab, The MathWorks, CA, US). Fifty-eight additional cells recorded for other experiments were included in this analysis. The quality of the resulting clusters was assessed using a multivariate ANOVA (p<0.05). GC analysis of floral odors and preparation of a synthetic odor. Floral volatiles were collected from D. wrightii flowers using dynamic headspace sorption. 103 flowers were collected from the field, and placed in vials of water in an incubator for overnight odor collection. Flowers were enclosed in transparent vinyl oven bags (Reynolds), closed below the flower with plastic ties, with a total enclosed volume of 500 mL. Charcoal-filtered air was pumped into the bag at 3 l/min, and odor-laden air was drawn by a vacuum through a sorbent cartridge trap at 1 l/min. The traps were composed of a borosilicate glass tube (7 mm) packed with 100 mg of Super Q adsorbent (mesh size 80-100) and plugged with i.d. #4 silanized glass wool. Odors were collected from near sunset, right before anthesis, and continued overnight for 12 hours. Odor analysis Odor traps were washed with 400 µl of HPLC grade hexane to elute trapped volatiles. Samples were stored in 2mL borosilicate glass vials with Teflon-lined caps at 80ºC. From each sample, 1 µl was injected (splitless, 30 s) into a Shimadzu model 14A GC (MD, USA.) equipped with a flame ionization detector. Separation of volatiles was achieved through use of a DB-1 column (J & W Scientific, CA, USA.), with an initial oven temperature of 50 ºC for 5 min, a constant rate of increase of 6 ºC per min to 230 ºC, and holding at 230 ºC for an additional 6 min. Data was recorded and analyzed using EZChrom Elitesoftware (Scientific Software, Inc., CA, USA.). Volatiles were identified 143 and quantified by comparison to a concentration series of purified synthetic standards injected under identical conditions. Validation of synthetic mixtures A synthetic mixture was developed through an iterative process. The mixture was prepared by adding each of seven components previously identified as producing significant responses in the AL (BEA, BOL, FAR, GER, LIN, MES, MYR and NER, Riffell et al. 2009a) in concentrations reflecting the proportion of that component in the odor emissions of D. wrightii flowers as determined above, and mixing with mineral oil to a final volume of 1 ml. 10 µl of this mixture was placed onto a 1 cm square of filter paper in each of 10 5ml glass syringes. The syringes were allowed to equilibrate for 4 hours, and then 4 ml of the odor-laden air was injected into traps identical to those used in quantifying the D. wrightii floral odor. Traps were eluted, and the samples analyzed as above. The significantly smaller peaks obtained were quantified, and components at higher or lower concentrations were adjusted for the next iteration of this procedure. A solution (Table 2) was obtained with component ratios matching the average component ratios found in the D. wrightii floral odor. For the BOL, LIN, MES, and NER volatiles, two series of mixtures were produced with concentrations of these odors at 0.01, 0.1, 1, 10, and 100 times the concentration used in the full synthetic floral mixture, either alone or with all other components at the natural proportions. 144 Component Concentration Purity (%) Benzaldehyde (BEA) 2.08 ng/µl ≥99.5 [Fluka] β-Myrcene (MYR) 9.69 ng/µl ≥95.0 [Fluka] Benzyl alcohol (BOL) 520.95 ng/µl ≥99.8 [Sigma] Linalool (LIN) 19.19 ng/µl ≥97.0 [Aldrich] Methyl Salicylate (MES) 284.42 ng/µl ≥99.5 [Fluka] Nerol (NER) 192.76 ng/µl ≥97.0 [Aldrich] 153 µg/µl ≥99.0 [Fluka] Geraniol (GER) Table 1. Volatiles used in the synthetic mixture, with the concentration in mineral oil that yields an odor in air with the natural proportions of the floral odor. 145 Figure 1. Identification of PNs and LNs from extracellular spike trains. 146 Figure 1. Identification of PNs and LNs from extracellular spike trains. A. Histogram of the selectivity index for all cells tested. The responses of recorded neurons to a panel of 10 odorants were used to calculate a selectivity index for each cell. Three groups were observed, and categorized as high (red bars), medium (green bars) or low (blue bars) selectivity. An additional, purely non-selective cell was categorized as a low selectivity cell (leftmost blue bar). B. Clustering via characteristics of the ISI histogram. The peak location and the width at half-height of a probability density function fitted to the ISI histogram of each cell, projected in a 2-dimensional space, reveals one distinct, tightly grouped cluster, and one diffuse, large cluster, with significant separation between them (multivariate ANOVA, p<0.05). The smaller, tighter cluster contains a majority of low-selectivity cells, including the single non-selective cell, and is categorized as the LN cluster. The larger cluster contains all but one of the highly selective cells, and all of the medium-selectivity cells, and is categorized as the PN cluster. The existence of single, high-selectivity cell is likely attributable to the relatively small number of odors used in this experiment, and is comparable to a high-selectivity cell previously recorded and identified through dye filling as a LN (Reisenman et al. 2011). 147 Figure 2. Responses of identified PNs to floral odor mixtures of various proportions. 148 Figure 2. Responses of identified PNs to floral odor mixtures of various proportions. A. Firing-rate response of a PN identified through the method in this appendix. This neuron responds with a concentration-dependent increase in mean instantaneous firing rate to a series of concentrations of methyl salicylate alone (red bars). 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