ENCODING THE CONFIGURATION OF A CONSPECIFIC PHEROMONE IN THE MANDUCA SEXTA by

ENCODING THE CONFIGURATION OF A CONSPECIFIC PHEROMONE IN THE MANDUCA SEXTA by
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). This
research, like much of neuroethology, is motivated by the principle best expressed by
August Krogh (1929): “For a large number of problems there will be some animal of
choice, or a few such animals, on which it can be most conveniently studied.” The
excellent track record of work inspired by this principle gives hope that the study of the
encoding of the configuration of a particular, innately attractive odor in the AL of a moth
will find broader applicability to studies of the role of synchrony and encoding of
complex sensory stimuli in other systems.
85
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APPENDIX A: THE NEUROBIOLOGY OF INSECT OLFACTION: SENSORY
PROCESSING IN A COMPARATIVE CONTEXT.
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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
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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.
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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
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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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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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
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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).
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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.
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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
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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. Finally, we have attempted to illustrate the broad,
interdisciplinary nature of work in this field. Insect olfaction is
especially fertile ground for interplay among studies in ecology,
evolution, and behavior that set the parameters for the problems
insect olfactory systems must solve.
Acknowledgments
We thank Tom Christensen and Nick Strausfeld for helpful
comments in preparing this manuscript, and Charles Hedgcock for
assisting in creating the figures. JPM was supported by NIH
fellowship F31DC009722 during the preparation of this review.
Our current research in this area is supported by NIH grant R01DC-02751 (to JGH).
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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.
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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
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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
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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.
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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.
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Synchrony encodes innately attractive odors
Conflict of Interest Statement: The
authors declare that the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential conflict of interest.
Received: 24 March 2010; paper pending published: 13 July 2010; accepted:
9 August 2010; published online: 24
September 2010.
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.
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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-
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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
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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). When the same
concentration series is combined with the other odors in the Datura mixture at constant
concentrations, the response (blue bars) is maximal to the mixture with the natural
proportion of methyl salicylate. B. Synchrony between two PNs recorded simultaneously.
Shift-predictor subtracted cross-correlation histograms of two PNs in response to a series
of synthetic Datura floral odor mixtures with varying concentrations of benzaldehyde.
Synchrony is maximal in response to the mixture with the natural proportion of
benzaldehyde.
This material will be published in the Frontiers in Neuroscience..
149
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