submitted to the
Combined Faculties for the Natural Sciences and for Mathematics
of the Ruperto-Carola University of Heidelberg, Germany
for the degree of
Doctor of Natural Sciences
Presented by: Attila Rácz, MD
Born in: Debrecen, Hungary
Molecular determinants of hippocampal oscillatory activity
Prof. Dr. Peter Seeburg
Prof. Dr. Hannah Monyer
In memory of Dr. Ervin Szegedi (1956-2006),
my physics teacher at secondary school
In vitro electrophysiological studies in genetically modified mice with a deletion of the
GluR-A subunit in parvalbumin-positive GABAergic interneurons (PV-GluR-A KO mice) provided
evidence for the involvement of this cell-population in the generation of hippocampal network
synchrony. Besides, these mice displayed several alterations in hippocampus-dependent cognitive
tasks (Fuchs et al., 2007). To study the characteristics of hippocampal network synchrony
thoroughly, we applied in vivo electrophysiological measurements in freely moving animals.
We used tetrode and silicon probe hippocampal recordings from mutant and wildtype (WT)
animals and compared cellular activity obtained from pyramidal cells and interneurons as well as
network activity. The results can be summarized as follows:
1. PV-GluR-A KO mice exhibited increased ripple-power compared to WT mice. The
underlying mechanism cannot be accounted for by an augmented cellular activity during ripples but
by an increased phase-modulation of both pyramidal cells and interneurons as indicated by the
unitary analysis.
2. The decreased gamma-power in the PV-GluR-A KO mice revealed by in vitro
measurements could not be corroborated by the in vivo study. However, a reduction in gammafrequency could be identified during REM-sleep of the PV-GluR-A KO mice. The phase-preference
of pyramidal cells during gamma-oscillations was not different between genotypes. However, there
was a delay of the phase-preference of interneurons in PV-GluR-A KO compared with WT mice.
3. The firing rate of pyramidal cells during theta-oscillations was decreased in PV-GluR-A
KO mice whereas that of interneurons did not change significantly. We propose that the pyramidal
cells’ underperformance is due to the altered function of interneurons.
4. Pyramidal cells were more “bursty” in PV-GluR-A mutants. The increased “burstiness”
occurred during theta-, gamma- and ripple-oscillations. We think that the suboptimal work of
interneurons makes pyramidal cell firing less “predictable” and maybe temporary fluctuations in the
excitatory and inhibitory network state can disturb the optimal modes of pyramidal cell-discharge.
In summary, this in vivo study provides direct evidence that PV-positive GABAergic
interneurons play a crucial role in the generation of synchronous network activity in the
Elektrophysiologische Untersuchungen in vitro an genetisch modifizierten Mäusen, in denen
die GluR-A Untereinheit in Parvalbumin-positiven GABAergen Interneuronen ausgeschaltet wurde
(PV-GluR-A KO Mäuse), ergaben, dass diese Zellpopulation an der Entstehung synchroner
Netzwerkaktivität massgeblich beteiligt ist. Des weiteren wiesen Verhaltenstests darauf hin, dass die
genetische Modifikation zu Defiziten von Hippocampus-abhängigen Leistungen führte (Fuchs et al.,
2007). Um synchrone Netzwerkaktivität im Hippocampus besser charakterisieren zu können, führten
wir elektrophysiologische Ableitungen in vivo an sich frei bewegenden Mäusen durch.
Wir benutzten Tetroden und Silicon-Proben und verglichen bei modifizierten und WildtypMäusen (WT) Einzelzellaktivität von Pyramidenzellen und GABAergen Interneuronen sowie
oszillatorische Netzwerkaktivität. Die Ergebnisse können wie folgt zusammengefasst werden:
1. PV-GluR-A KO Mäuse zeigten erhöhte “Ripple”-Aktivität im Vergleich zu WT-Mäusen.
Wie die zelluläre Analyse zeigt, scheint der zugrunde liegende Mechanismus nicht die erhöhte
zelluläre Aktivität in “Ripple”-Oszillationen zu sein, sondern eine erhöhte Phasenmodulation sowohl
von Pyramidenzellen als auch von Interneuronen.
2. Die verminderte Gamma-Leistung in PV-GluR-A KO Mäusen, die sich aus in vitro
Messungen ergab, konnte in vivo nicht verifiziert werden. Wir fanden jedoch eine Verminderung der
Frequenz von Gamma-Oszillationen während REM-Schlaf in den PV-GluR-A KO Mäusen. Es gab
keinen Unterschied in der Phasen-Preferenz von Pyramidenzellen während Gamma-Oszillationen.
Interneurone von PV-GluR-A KO Mäusen jedoch waren verzögert im Vergleich mit WT-Mäusen.
3. Die Feuerfrequenz von Pyramidenzellen während Theta-Oszillationen war verringert in
PV-GluR-A KO Mäusen, während die von Interneuronen sich nicht signifikant änderte. Die
reduzierte Aktivität von Pyramidenzellen ist vermutlich eine Konsequenz der veränderten
4. Im Vergleich zu WT Mäusen, waren Pyramidenzellen in PV-GluR-A KO Mäusen mehr
“bursty” sowohl während Theta- als auch Gamma- und Ripple-Oszillationen. Die suboptimale
Funktion der Interneurone ist wahrscheinlich der Grund dafür, warum das Feuern von
Pyramidenzellen weniger “vorhersehbar” ist. Eventuell können Fluktuationen von Erregung und
Hemmung im Netzwerk das optimale Muster des Feuerns von Pyramidenzellen stören.
Zusammengefasst weisen diese in vivo Untersuchungen darauf hin, dass PV-positive
GABAerge Interneurone bei der Entstehung synchroner Netzwerkaktivität des Hippocampus eine
wichtige Rolle spielen.
CA1, 2, 3
GluR-A, GluR-B, GluR-D
NR1, NR2
Adenosine DeAminase acting on RNAs
α-Amino-3-hydroxyl-5-Methyl-4-isoxazole Propionic Acid
Blood Oxygen Level Dependent
Cornu Ammonis 1, 2, 3
Calcium/CalModulin-dependent protein Kinase II
CannaBinoid (receptor) 1
Current-Source Density (analysis)
Dentate Gyrus
Depolarization-induced Suppression of Inhibition
Excitatory Amino Acid Transporter
Excitatory PostSynaptic Potential
Fast Fourier Transform
Functional Magnetic Resonance Imaging
Gamma-Amino-Butyric Acid
GlutAmate Decarboxylase
Giant Depolarizing Potential
Glutamate Receptor A, B, D
High-Frequency Stimulation
5-HydroxyTryptamine, serotonin
Inhibitory PostSynaptic Potential
Current-Voltage (curve)
Kalium-Chloride Cotransporter 2
Local Field Potential
Large-Amplitude Irregular Activity
Long-Term Potentiation
Multi-Electrode Array
Natrium-Kalium-Chloride Cotransporter 1
N-Methyl-D-Aspartic acid
Nitric Oxide, Nitric Oxide Synthase
NeuroPeptid Y
NMDA-Receptor 1, 2
N-ethylmaleimide-Sensitive Fusion protein
Oriens-Lacunosum-Moleculare (cell)
Protein Interacting with C Kinase 1
PostSynaptic Density
Rapid Eye Movement (sleep)
Synapse-Associated Protein
Standard Deviation
SharP Wave
Slow-Wave Sleep
Transmembrane AMPA-receptor Regulating Protein
Theta-Burst Stimulation
Theta-modulated Place-by-Direction (cells)
General anatomy of the hippocampus
The hippocampal formation consists of several subregions, including the dentate gyrus (DG)
and hippocampus proper (Figure 1.). The hippocampus itself can be divided into subregions named
after “Cornu Ammonis” (CA3, CA2 and CA1). Axons from the CA1 form the major output of the
hippocampus, the subiculum on one hand and the fornix on the other. The former one feeds
information back into the entorhinal cortex (mainly layer 5), which in turn also innervates CA1,
CA2 and CA3 via the perforant pathway. The fornix arches towards the mamillary nucleus, from
where two main tracts are formed, the fasciculus mamillothalamicus (Vicq d’Azyr) and the
fasciculus mamillotegmentalis (named after Gudden). The first one gives input to an anterior
portion of thalamic nuclei, which project to the cingulum. The cingulum also volutes back to the
parahippocampal structures, thereby closing one of the limbic circles. The hippocampus is a real
centre of anatomical connections, not only does it receive inputs from the dentate gyrus and
entorhinal cortex, but it is also reciprocally connected to the septal nuclei, which also provide
cholinergic and GABAergic (gamma-amino-butyric acid) input to the hippocampus. The dentate
gyrus receives noradrenergic input from the locus coeruleus, serotonergic innervation from the
raphe nuclei whereas the CA1 receives dopaminergic input from the mesolimbic system, especially
the ventral tegmental area. As we shall later see, these modulatory systems may exert a strong
influence on learning functions, both in terms of stress-related (Reymann & Frey, 2007) and
reward-related learning (Foster & Wilson, 2006). The amygdala comprises a complex of nuclei
reciprocally connected with the CA areas and is functionally related to fear-conditioning.
Even though we refer to the hippocampal formation as archicortex, the basic circuitry of the
hippocampus shows remarkable differences to other cortical formations. A striking hippocampal
feature is its three-layered structure, with cell bodies arranged in the middle, dendritic trees on one
side and axons on the other. This is in sharp contrast with the six-layered neocortical
microarchitecture. The lamination offers an excellent opportunity for understanding the anatomy of
the basic circuitry and for studying its principal physiological functions. The five-layered
parahippocampal structures (e.g. the subiculum or entorhinal cortex) are also referred to as
periarchicortex because they show a transition between the archi- and neocortical organizing
principles (for a more comprehensive treatise see Amaral & Witter, 1995).
Similar to other brain structures, the two major neuronal cell types in the hippocampal
formation are the principal cells and interneurons. The archicortex is populated by distinct neuronal
subgroups. The principal cells of the dentate gyrus are called granule cells, those of the
hippocampus proper are called pyramidal cells. An interesting glutamatergic cell type in the DG is
Figure 1.: The basic anatomy of the hippocampus, as depicted by Santiago Ramon y Cajal at the
beginning of the 20th century. One can clearly identify distinct subregions of the hippocampal
formation and basic in- and output routes and internal connections of it: the perforant path coming
from the entorhinal cortex, the Schaffer-collaterals and fornix originating from CA3 and CA1.
the mossy cell. It is innervated by granule cells and projects back innervating the dendritic tree of
granule cells in the neighbourhood of the cell they receive excitation from. The interneurons of DG
are located mainly in the molecular and polymorphic layers, but some, mainly of the basket cell
type, can also be found in the granule cell layer. The axons of the granule cells are called mossy
fibers, they lack myelin-sheath and form unique synaptic structures onto CA3-cells. CA3-pyramidal
neurons receive their input mainly from dentate gyrus mossy fibers, from the perforant path
(entorhinal input), from the contralateral CA3 (commissural connections) and via recurrent
collaterals from CA3-cells themselves. The latter pathway is called Schaffer-collateral-system, and
due to its anatomical organization remarkable auto-associative features are attributed to it that also
has behavioural consequences (Nakazawa et al., 2002). CA3-pyramidal cells are quite big (30-35
µm), in comparison to CA1-pyramids (10-15 µm). CA3-cells project both to CA1 pyramidal cells
and to CA1 interneurons, thereby ensuring proper feed-forward excitation and feed-forward
inhibition. The transition zone between the CA3 and CA1 is called CA2. Here cells are still big, but
they do not receive mossy fibers from the DG. The CA1 receives input from the CA3-Schaffercollateral system mainly via the basal dendrites and apical dendrites located in the stratum radiatum.
In addition, the apical dendrites of CA1 pyramidal cells are also innervated by the perforant path,
which runs in the stratum lacunosum-moleculare. Apparently the recurrent loop, connecting CA3pyramids is absent in CA1. CA1-pyramids also drive interneurons that provide feed-back inhibition
onto the pyramidal cells. It is important to note that whereas connections between DG granule cells
and CA3-pyramids are rather convergent (a given granule cell innervates 20-30 CA3-pyramids, but
a given CA3 cell receives input from roughly 50 granule cells), connections between CA3 and CA1
are more divergent, also due to the Schaffer-collateral system (Amaral & Witter, 1995).
The hippocampus is a special brain-area where the genesis of granule cells continues after
birth*. *Other structures possessing this potential are the olfactory bulb (for interneurons) and in distinct species some
neocortical areas, such as auditory cortex in some birds, in migrating birds hippocampus as well. After birth the lateral
and medial ganglionic eminence and the subventricular zone serve as a place for interneuron precursors generator which
then follow certain routes (like the rostral, ventral and dorsal migratory streams) to reach their targets: the basal ganglia
or the olfactory bulb. These neuroblasts form the granular and periglomerular cells of the olfactory bulb (for a more
complete overview see the reviews of Marin & Rubinstein, 2004 and Wonders & Anderson, 2006).
A number of theories and experiments imply that adult neurogenesis in the dentate gyrus
might be associated with hippocampal plasticity, possibly by guaranteeing a continuous rewiring of
the system. It is indeed suggested that explorative behaviour promotes neurogenesis in the dentate
gyrus (animals living under environmentally enriched conditions show this phenomenon, Segovia et
al., 2006). It is also known that cabdrivers have a bigger hippocampus, due to the regular navigation
tasks they perform during their everyday routine (Maguire et al., 2000). Interestingly, the right
hippocampus is the one which shows this correlation, and the size of the right hippocampus
correlates well with the time spent as a taxi-driver as well as with the navigation skills of the given
cabdriver (actually this observation also corroborates the old view that the human right hemisphere
would be responsible for the integration of spatial information and the left one for the temporal
information, Szirmai et al., 2000). On the other hand, early-childhood brain-irradiation (e.g.
performed as oncological therapy) can substantially reduce the number of available neurogenic
precursors and can lead to learning deficits in later stages of life. In any case, it seems to be
important to have ongoing neurogenesis in a brain structure involved in learning and plasticity.
Histology of hippocampal interneurons
There is a wide spectrum of interneurons taking part in the histological architecture of CA3
and CA1. Interneurons can be grouped in distinct classes according to a number of arbitrary
features, which in some cases hint to their specific roles in certain neurophysiological processes.
Interneurons can be classified based on their morphological properties, their location, the cellular
domains that they innervate, their electrophysiological properties, the expression of specific
histological markers and proteins, or their participation in distinct oscillatory states (such as thetaoff and theta-on cells, Mizumori et al., 1990). Since there are certain correlations between the
above-named criteria, current attempts to classify interneurons aim at taking into consideration as
many characteristics as possible (molecular, anatomical, functional).
A large amount of data in this research field was generated in the laboratory of Peter
Somogyi and co-workers. These researchers implant sharp electrodes in the hippocampus of
anaesthetized rats, adjust the location of the electrode according to the local field potentials and
record the activity of individual cells by moving the electrode very close to them. In this way they
can follow the behaviour of individual cells during different network-states. The juxtacellular
labeling method can be used to fill the recorded cell with a dye (byocitin or neurobiotin) for further
anatomical reconstruction. The principle of this method is that during the membrane potentialfluctuations of theta-oscillations the neurons can take up the charged dye-molecules (Pinault, 1996).
This way the Somogyi group could establish many correlates between morphology, biochemical
markers and electrophysiological properties of certain interneuron subtypes. However, since they
examine oscillation-related activity in animals under anaesthesia, it is difficult to extrapolate the
results of these findings to a likely scenario as it might occur in vivo.
To illustrate interneuron diversity, just a few examples will be discussed. O-LM cells
(oriens-lacunosum-moleculare cells) for instance innervate dendrites in the stratum lacunosummoleculare whereas they sit and expand their dendritic tree in the stratum oriens, to receive input
from axons of pyramidal cells. Bistratified cells already innervate stratum radiatum dendrites
(Klausberger et al., 2004) whereas basket cells inhibit the perykarion of neurons with basket-like
structures. It is estimated that a basket cell can form inhibitory connections on as many as 18002000 pyramidal cell somata (Amaral & Witter, 1995; Freund, 2003). Axo-axonic or chandelier cells
form synapses on the axon initial segment and since according to the classical view this is the
action-potential generation site*, their function can be very important in the output control of
pyramidal cells (Klausberger et al., 2003; Szabadics et al., 2005). The neurogliaform cells (Price et
al., 2005) are mainly found in the stratum lacunosum-moleculare controlling the perforant path
input from the entorhinal cortex. These cells are quite heterogeneous in their electrophysiological
properties (Zsiros & Maccaferri, 2005).
*Some studies point out that action potentials may be generated in the dendrites as well (Kamondi et al., 1998),
based on the observation that the frequency of dendritic Ca2+-spikes can also exceed that of the somatic action
A recently identified novel interneuron-type in the hippocampus is the Ivy-cell. These cells
are quite numerous, they are located mainly in the pyramidal layer, show very dense axonal fields
innervating preferentially the basal dendritic domains and they co-express neuropeptide Y (NPY)
and nitric oxide synthase (NOS). It is speculated that they control the input coming via the Schaffer13
collaterals in a rather domain-specific way via the release of NO (Fuentealba et al., 2008). In
addition, NO may also regulate blood circulation in the brain, in this manner Ivy-cells might adjust
blood and nutrient supply to local needs.
Of interest are also interneurons that innervate (and thus inhibit) other interneurons. The
activity of these interneurons can lead to desinhibition of given pyramidal cells (Fonyó, 1995).
Certain immunochemical markers are also used to distinguish between interneuronsubtypes. In many cases they are calcium-binding proteins, such as parvalbumin (PV), calbindin
(CB) and calretinin (CR), in other cases neuropeptides, such as cholecystokinin (CCK),
somatostatin (SOM) or NPY. According to the current view, the expression of these markers is
correlated with the anatomical and physiological subclass to which a given interneuron belongs.
Bistratified cells, axo-axonic cells and a great percentage of basket cells are PV-positive, O-LM
cells are mainly SOM-positive (Klausberger et al., 2003).
Principles of excitatory neurotransmission
Pyramidal cells constitute the output neurons of the hippocampus. However, pyramidal cells
also innervate other pyramidal cells and interneurons in the hippocampus, so excitation is a primary
action there. The excitatory drive onto principal cells and interneurons is transmitted mainly via
glutamatergic synapses. The receptors, residing on the postsynaptic membranes are ligand-gated
ionotropic receptors. According to their pharmacological properties, glutamate receptors can be
classified in three major classes: AMPA-, kainate- and NMDA-receptors (AMPA stands for αamino-3-hydroxyl-5-methyl-4-isoxazole propionic acid, NMDA for N-methyl-D-aspartic acid,
reviewed by Mayer, 2005). Each class comprises several subunits. Thus, for instance there are four
main subtypes of AMPA-receptor forming subunits (referred to as GluR-1-4 or GluR-A-D) that are
differentially expressed with respect to the brain region and cell type. Pyramidal cells preferentially
express GluR-A and GluR-B (Geiger et al., 1995). The latter subunits renders heteromeric AMPAreceptors with any of the other three subunits Ca2+-impermeable. The required Ca2+ influx via
glutamate receptors involved in potentiation of synapses on pyramidal cells has been attributed
exclusively to NMDA-receptors. However, the situation is different in interneurons since they
express GluR-A and GluR-D but relatively low levels of GluR-B (Geiger et al., 1995) and are thus
equipped mainly with Ca2+-permeable AMPA-receptors. Whether AMPA-receptors are involved in
synaptic plasticity in interneurons is not clear so far. Overall, synaptic plasticity in pyramidal cells
has been much studied over the last two decades and numerous in vitro and in vivo paradigms have
been employed. Synaptic plasticity in interneurons has hardly been studied and in fact was
considered to be absent in interneurons. However, recent studies provide evidence that synaptic
strength in interneurons is modifiable (Lamsa et al., 2007).
The functional interplay between AMPA- and NMDA-receptors resides in their different
kinetics and functional regulation. Both receptors comprise four transmembrane domains, an
extracellular N-terminal and an intracellular C-terminal domain. NMDA-receptors have a higher
affinity for glutamate than AMPA-receptors. This means that without any other regulation NMDAreceptors would be activated first upon the presence of environmental glutamate and this would
result in a massive Ca2+-influx into the cell even under low external glutamate-concentrations.
However, NMDA-receptors at resting membrane potential are “blocked” by Mg2+-ions. The Mg2+block can only be relieved by strong membrane-depolarization. This mechanism reverses the order
so that AMPA-receptors, despite their lower glutamate-affinity gate first and their mediated Na+current can depolarize the cell to the “threshold” of NMDA-receptor activation (Kandel et al.,
2000). Thereby it is ensured that NMDA-receptors can only conduct their massive and relatively
long-lasting Ca2+-current, if it is really needed. On one hand this offers specificity for long-term
potentiation (LTP) on principal cells, on the other hand it also circumvents possible harmful effects
of excessive intracellular Ca2+-concentrations*.
*High intracellular Ca2+-concentrations can lead to the activation of Ca2+-activated proteases, such as calpains,
which degrade important molecules, even though calpains are also involved in LTP-induction (see the review of Bliss &
Collingridge, 1993). The co-expression of AMPA- and NMDA-receptors on the postsynaptic density further ensures
that ectopic or extrasynaptically located receptors alone cannot interfere much with synaptic function. It is speculated
that extrasynaptic glutamate-receptors constitute a pool for newly synthesized proteins, which are incorporated later into
the synaptic membrane by lateral membrane-diffusion (Ashby et al., 2006).
Glutamate-receptors are formed of tetrameric assemblies. For instance NMDA-receptors of
the hippocampus are composed of two NR1 and two NR2 (NMDA-receptor 1 and 2) subunits
(Furukawa et al., 2005), the latter being either NR2A or NR2B. NR2 subunits differ with respect to
their developmental regulation and kinetic properties. NR2B-containing receptors, which are
preferentially expressed at earlier developmental stages, have slower gating kinetics, thereby
allowing for longer-lasting Ca2+-currents. The glutamate-binding site is located on the NR2
subunits. For NMDA-receptor activation, binding of the co-modulator glycine is also required and
its binding site is on the NR1 subunit.
The C-terminal domain of the NMDA-receptor subunit contains binding sites for
Ca2+/calmodulin-dependent protein kinase II (CaMKII) and for proteins possessing PDZ-domains,
such as the PSD-93, PSD-95 (postsynaptic density proteins 93 and 95), SAP-97 and SAP-102
(synapse-associated proteins 97 and 102)(reviewed by Kim & Sheng, 2004). In case of the AMPAreceptors the so-called TARPs (transmembrane AMPA-receptor regulating proteins) control the
channel conductivity by bridging the AMPA-receptors and PDZ-domain proteins. The stargazin
(TARP γ2) is a classical example of these molecules (Chen et al., 2000). The stargazin receives its
name after a mouse mutant with cerebellar deficits (a “stargazer” phenotype), due to the markedly
reduced expression of stargazin on cerebellar granule cells of the mutant mouse (Hashimoto et al.,
An immense diversity of glutamate receptor subunits is provided by alternative splicing and
RNA-editing (Higuchi et al., 1993). The editing sites in the RNA are located in the region of the 2nd
intramembrane helix, corresponding to the so-called pore-loop (translated to protein-structure) of
the AMPA-receptors. They have very important functional implications. The Q/R-editing site on the
GluR-B is processed by one of the ADARs (adenosine deaminases acting on RNAs). If the editing
is compromised with GluR-B-mutations, glutamin will be incorporated into the protein instead of
arginin, which renders it Ca2+-permeable. The increased Ca2+-conductance on pyramidal cells leads
to epileptic seizures and early animal death in a mouse model (Feldmeyer et al., 1999).
Figure 2.: Schematic view on the domainstructure of AMPA-receptor subunits. The
N-terminal domain lies in the extracellular
space while the C-terminal domain is
intracellular. The second intra-membrane
α-helix bends back towards the cytoplasm.
The RNA-editing site also lies in that
The C-terminal domain comprises binding
sites for the PDZ-domain protein PICK1
(Protein Interacting with C Kinase 1,
involved in receptor-clustering of the
AMPA-receptors, Xia et al., 1999) and NSF
(N-ethylmaleimide-sensitive fusion protein,
involved in membrane fusion events and
also in disassembling the AMPA-receptors
from PICK1, Hanley et al., 2002). The
structure of NMDA-receptor subunits is
basically similar to this principle. The
picture is from www.bris.ac.uk, the official
website of Bristol University.
I have not commented on the role of metabotropic glutamate-receptors, they are 7TMproteins and activate G-proteins upon ligand binding. They are often found on the perisynaptic
regions as well, and are consequently activated by higher concentration glutamate-spillover
(Somogyi et al., 1998).
Principles of inhibitory neurotransmission
In addition to excitatory synapses there are also inhibitory ones in the nervous system. The
former are also referred to as asymmetric, the latter symmetric. These differences rely on
electronmicroscopical studies of Gray and the classification of Colonniel, who described a more
electrondense material on the postsynaptic side of excitatory synapses whereas on inhibitory
synapses this density is more or less symmetrical (Kandel et al., 2000). In addition, granules of
excitatory transmitters are mainly big and of round shape while those of inhibitory synapses are
smaller and rather ovoid (Uchizono, 1965). Whereas excitatory synapses are formed mainly on
dendrites and dendritic spines, inhibitory synapses are usually found on dendritic shafts and on
perisomatic regions, such as the soma itself or the axon-hillock. The strategical importance of this
arrangement lies in the electrotonic attenuation of postsynaptic potentials and their spatiotemporal
summation on distinct parts of the cell (Fonyó, 1997; Kandel et al., 2000).
The two main inhibitory neurotransmitters of the central nervous system are GABA
(gamma-amino-butyric acid) and glycine. However, the latter is confined mostly to the brainstem
and the spinal chord. Both the ionotropic glycine- and GABA-receptors belong to the acetylcholinereceptor family, a major hallmark of which is the pentameric structure composed of different
subunits. The ionotropic GABA-receptors (or GABAA-receptors) are anion-channels, they transport
Cl- and HCO3- anions. From the well-known anxiolytics the benzodiazepines and the zolpidem act
on GABAA-receptors* (Fürst, 1998). However, different brain regions and different cell types
express different receptor-subunits and different cellular domains can also show a different
expression-pattern (Mody & Pearce, 2004). This can explain why many of these compounds
influence the involved target-mechanisms to a different extent, such as anxiolytic, sedatohypnotic or
muscle-relaxant effects and side effects can be of different strength. Nevertheless, these drugs also
affect learning.
*Barbiturates also act on GABAA-receptors, but they have a different binding site. Whereas benzodiazepins
increase the frequency of channel-opening upon their binding, the barbiturates keep the channels open for a longer time.
The most commonly used GABAA-receptor antagonists in electrophysiology are the gabazine, bicuculline and
GABA as well as glutamate also acts on metabotropic receptors, which are coupled to the
function of G-proteins. These receptors are 7TM-receptors. The GABAB–receptors are found
mainly presynaptically (Somogyi et al., 1998), very often on the periphery of the synapses. Their
effect evolves slower, because of the delay of the intracellular cascades that are activated by the
7TM-receptors. In many cases, the GABAB–receptors can decrease the transmitter-release from the
presynaptic active zones (reviewed by Freund & Katona, 2007).
GABA is produced from glutamate via the action of GAD (glutamate decarboxylase)
enzymes. Cells take up glutamate with excitatory amino acid transporter (EAAT) molecules, which
are also expressed by glial cells. The secreted GABA is bound to the GABA-receptors and after
dissociating from the receptor GABA is removed from the synaptic cleft by transporter mechanisms
(Kandel et al., 2000). The kinetics of GABAA-receptors is also faster than that of most glutamate
receptors. The fast GABA-removal via its re-uptake mechanism and the channel-kinetics in the
synaptic cleft are responsible for the phasic component of GABAergic inhibition whereas the tonic
inhibition is accounted for extrasynaptic GABAA-receptors, which also have a different subunitcomposition (Mody & Pearce, 2004).
Physiology of interneurons
From a general point of view interneurons fulfill a role in the activity-control of local neural
circuits. This view is reflected by the name “local circuit neuron”. The effect they exert on the
innervated cells is mainly inhibitory. This stands in contrast with the principal cells, which are
mainly excitatory and are often called projection neurons, for projecting to different or distant brain
structures or circuits. However, there are exceptions on both sides. In certain cases GABAergic
interneurons can also provide distant projections, such as in case of the interneurons of the septal
nuclei (Amaral & Witter, 1995) or Purkinje-cells, the former projecting to the hippocampus, the
latter to the cerebellar nuclei. Long-range inhibitory connections constitute an important organizing
principle between distinct parts of the basal ganglia as well. Another example is given by the
reticular thalamic nuclei, which provide GABAergic innervation for other thalamic nuclei.
Excitatory cells can also be local-circuit neurons. The so-called “mossy cells” of dentate gyrus are
glutamatergic, but they innervate neighbouring granule cells that they receive input from (Amaral &
Witter, 1995).
Inhibition in the cortex is mainly provided by GABA. However, the effect of GABA can
also be excitatory. The excitatory action of GABA is very prominent in development when it can
also contribute to the plastic formation of microcircuits and can shape early network-patterns
(reviewed by Ben-Ari et al., 2007). The effects of GABA are mediated by ligand-gated ionchannels, which are permeable to Cl-- and HCO3--anions. The direction of the current is determined
by the membrane potential and the equilibrium potential of the given ions. In certain cases cells
have higher internal Cl--concentrations and according to the Nernst-equation it leads to a smaller
(less negative) Cl--equilibrium potential. When this equilibrium potential exceeds the actual
membrane potential, the GABA-current can change its direction and instead of being an inward
negative current, it represents an outward negative current. This can lead to depolarization of the
cells (Cohen et al., 2002; Stein & Nicoll, 2003), in certain cases in the form of shunting-inhibition
(Vida et al., 2005), in other cases might also induce action-potentials (Szabadics et al., 2005). The
cellular Cl--concentration is regulated by two transporters: the KCC2 (K+-Cl-cotransporter 2) and
NKCC1 (Na+-K+-Cl- cotransporter 1) channels. The latter imports Na+, K+ and 2 Cl- ions into the
cell whereas the former extrudes Cl- anions coupled to K+-export (Delpire, 2000). The expression of
these molecules is developmentally regulated and this can account for the higher internal Cl-concentrations during development and early postnatal periods when in many cases GABA is
excitatory. Rodent infants are especially prone to seizures, which may reflect the immature
inhibitory system in their brain at that age. Another phenomenon, which is generally associated with
the switch of Cl--gradient is the giant depolarizing potential (GDP, reviewed by Ben-Ari et al.,
2007). It occurs in cortical areas of newborn or infant mammals but disappears later. However,
according to Imre Vida and Marlene Bartos, a large proportion of hippocampal pyramidal cells
might also exhibit the same properties as neurons early in development (Vida et al., 2005). In that
situation GABA has a shunting-inhibitory effect. In other words it depolarizes the cell to some
extent but then it fixes the membrane potential close to the Cl--equilibrium potential, thereby also
inhibiting action potential generation. These effects may have important implications in the
generation of oscillations where they might contribute to the precise timing of cell-discharge in
large cell-populations.
Interneurons in general fire at a higher rate than principal cells. In case of basket
interneurons or axo-axonic cells, the innervation they provide is also more efficient than that of
provided by principal neurons that generally innervate distal dendritic domains. The higher
discharge-frequency of basket cells is brought about by several mechanisms of which the
expression of fast repolarizing potassium-channels such as Kv3.1 or Kv3.4 is of utmost importance
(Baranauskas et al., 2003). By their fast action, the depolarization-block of Na+-channels is relieved
very rapidly on the interneurons.
Putative roles of interneurons
As we have seen, interneurons not only can inhibit other cells but in some instances can also
excite them. Inhibition is a powerful tool to overcome deleterious effects of spreading excitation (in
case of epileptic seizures for example). The principles of “synfire-chains” proposed by Abeles and
Aertsen (Gewaltig et al., 2001) describe network-activities lacking a substantial inhibition. In this
case, which resembles epileptic seizures, the excitation can propagate and activate an ever-growing
cell-population and can only be stopped by the “exhausted” or “pseudo-refractory” state of the
system, determined primarily by neurotransmitter-release and properties of ion-channels (e.g.
desensitization kinetics). Interneurons can circumvent these problems purely by their inhibitory
action. Interneurons can also have a contrasting effect (collateral inhibition) that makes the output
of principal neurons more specific to certain stimuli and thus also minimizes the energy-cost of
information-processing. Phasic inhibition imposed on principal-cell-bodies enhances synchrony
between distinct cells. In certain ways interneurons can have a role in the generation of gamma- and
ripple-oscillations by their rhythmic inhibitory effect that also relieves the depolarization block
from the Na+-channels (Klausberger et al., 2004). As a consequence, a large number of principal
cells (and also interneurons) can be available for excitation and fire together. This happens for
instance in the hippocampal CA1-region in slow-wave sleep (SWS) when ripples (oscillations in the
frequency range between 130 and 250 Hz) occur.
Plasticity at synaptic and network level
Plastic changes belong to the most important ones in our brain-operations and without them
we could hardly imagine our daily life. Electrophysiologists often relate certain in vitro and in vivo
paradigms with plasticity. In general, we call synaptic plasticity any change in the efficacy of
synaptic transmission. These synaptic changes can affect the release-probability of the presynaptic
terminal, the number of released transmitter-molecules, the currents evoked on the postsynaptic side
and also the structure of the synapses (Kandel et al., 2000). Plasticity can occur on shorter and
longer time-scales at excitatory and inhibitory synapses leading either to an augmentation
(potentiation) or a depression of synaptic transmission. The terms “paired-pulse facilitation” or
“paired-pulse depression” denote short-term plasticity. Very often the kinetics of vesicular release
explain this kind of plasticity. Upon an action potential synaptic vesicles are mobilized and get
close to the release sites. When a new action-potential is generated, more or fewer vesicles are
available for release, depending on the distance between the release site and the vesicles. Another
aspect of short-term plasticity is the polyamine-dependent facilitation that is a postsynaptic
mechanism. Certain polyamine molecules, such as spermin, spermidin or putrescin block the
internal side of certain AMPA-receptors lacking GluR-B. Upon prolonged depolarization the
polyamines exit the channels, thereby allowing a larger pool of channels to conduct ions. This effect
is often accompanied by changes of the I-V (current-voltage) curve of the given channels, being
inwardly-rectifying in the beginning, and double-rectifying after this form of plasticity takes place
(Rozov et al., 1998). Not only interneurons can have this form of plastic change, but also pyramidal
cells, especially when they do not express GluR-B in a sufficient amount (Burnashev, 2003) and
this effect can contribute to epileptogenesis.
Long-term potentiation (LTP, originally described by Bliss & Lømo, 1973) requires
alterations on either the pre- and/or postsynaptic side of the synapses. Postsynaptic LTP involves
the incorporation of different sets of ion-channels on the synaptic membrane for instance or the
synthesis of new proteins as well as induction and repression of genes. These different steps are also
reflected by the staging of LTP: LTP1 is linked to posttranslational protein-modifications, LTP2 is
reflected in changes in protein synthesis without changes at mRNA-levels whereas LTP3 depends
on gene-expression alterations (reviewed by Bliss & Collingridge, 1993; Reymann & Frey, 2007).
The Ca2+-current of NMDA-receptors is thought to be a key mediator in LTP-induction, however,
the activation of Ca2+-permeable AMPA-receptors can also lead to LTP under physiological (Lamsa
et al., 2007) as well as pathological conditions (Feldmeyer et al., 1999).
Protocols, most commonly used in slice-preparations to evoke LTP are pairing, tetanic
stimulation (Bliss & Lømo, 1973) or high-frequency stimulation (HFS) and theta-burst stimulation
(TBS, Larson et al., 1986). As we will see later, the pairing protocol “associates” the activation of
presynaptic inputs with the postsynaptic activity of the cell, so it relies on the concept of spike-timedependent plasticity*.
*When action potentials are generated in neurons, the potential changes propagate back into their dendritic
tree. As a consequence, Ca2+-channels open and increase Ca2+-levels in distant dendritic domains. Thus, a well-timed
input on a given synapse can gain efficiency and be strengthened (Markram et al., 1997).
HFS also resembles physiological network-processes, such as ripples when cells can fire
with a high frequency for a short time-period while TBS consists of a high-frequency input wrapped
in 5 Hz clusters (a good example is described by Raymond & Redman, 2006). Since pyramidal cells
of the hippocampus tend to discharge during explorative behaviour (ongoing theta-rhythm in the
hippocampus in the frequency-range of 4-8 Hz) and place cells often fire in bursts, we can see this
protocol as a real counterpart of an in vivo situation which potentiates synaptic in- and outputs of
place cells or other types of output neurons. Experiments show that the longer a given pyramidal
cell is silent, the higher the probability that it will respond with burst firing to a sufficient excitatory
input (Harris et al., 2001). The underlying mechanism is probably related to the de-inactivation
kinetics of Na+-channels responsible for action potentials. Interestingly, pyramidal neurons are the
most “bursty” not exactly in their place field-centre, but in places where the average spiking-orbursting-frequency is around 6-7 Hz, a frequency like that of theta-rhythm. In certain cases, this
characteristic can make place fields sharper. Overall, it is proposed that single spikes can also gate
the efficiency of burst-spikes on dendritic domains, thereby contributing to the dimensions of
plasticity (Harris et al., 2001). Remarkably, LTP can also be induced in vivo by certain learning
paradigms, like inhibitory avoidance (Whitlock et al., 2006). The resulting increase in field
potentials showed resistence to further high-frequency stimulation (LTP-occlusion), suggesting that
the electrophysiological basis of learning correlates with changes induced with LTP-paradigms. The
hippocampus has an extensive monoaminergic innervation and it is indeed thought that these
monoamines play a role in learning. The performance of animals in learning paradigms can be
enhanced by a modest stress, such as swim-stress (reviewed by Reymann & Frey, 2007). This
observation is also underlined by the fact that ripples show alterations after administration of certain
serotonergic (5-HT1A and 5-HT3 receptors) and histaminergic (H1 and H2 receptors) antagonists
(Ponomarenko et al., 2003a). Low-frequency stimulation usually leads to depression of synapses.
These processes are extremely important with respect to brain oscillations since rhythmic inand output during oscillations can underlie synaptic modifications and in turn, these synaptic
changes can then alter local or global features of oscillations. Cells firing together within a short
time-window determined by an oscillatory cycle can potentiate their connections easier as reflected
by the concept of “cell-assemblies”. Another eloquent example is constituted by the “spindle
bursts”, high-amplitude synchronous events of 10-11 Hz, recorded from somatosensory cortices of
newborn rat pups. These network patterns are brought about by limb-movements and are supposed
to shape the sensory map of their body at that age (Khazipov et al., 2004).
Brain oscillations in general
Network synchrony has been a focus of research in neuroscience for almost hundred years
now. Even though basic principles of synchrony and the underlying circuitry have been described,
there are still many debates regarding mechanisms and physiological roles of oscillations. The main
oscillation-types that can be recorded from the skull surface are usually in the slower frequencyranges of delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). This is because the
slower an oscillation is, the bigger the entrained cell-population and thus one can record these
massive effects even from the skull where the signals are heavily attenuated. In other words, the
relation between oscillation-frequency and oscillatory power can be described by a power-law
(logarithmic relation, Buzsáki & Draguhn, 2004). For the long-range synchrony pacemakers and
long-range connections between distant cortical domains are also required in addition to the
numerous short-range connections. Theoretical estimations and anatomical measurements suggest,
however, that even a small percentage (0.5 %) of connections when long-range can fulfill this
function (Buzsáki, 2006). However, they need to be at critical places. For example the thalamic
inputs coming from nonspecific thalamic nuclei (such as the reticular thalamus) provide a major
contribution to the generations of delta- and alpha-rhythms, and sleep-spindles as well (both
thalamocortical and corticothalamic projections are needed in this process, reviewed by Steriade,
1999). These nuclei are also responsible for generation of cortical UP- and DOWN-states that
constitute an ongoing switch between a depolarized and hyperpolarized state of the cellular
membrane potential, inflicted by the thalamic pacemakers. One might speculate that for faster
rhythms short-range synchrony can be of bigger importance. That is indeed the case but not in an
exclusive sense. CA1-ripples for instance, even though being very fast emerge synchronously in the
two hemispheres even if they do not show cycle-by cycle coherence (Chrobak et al., 1996). In this
case commissural projections between the two hippocampi take part in their synchronization.
Oscillations of the hippocampus
The hippocampus shows a wide array of oscillatory patterns in the characteristic frequency
bands of theta- (4-12 Hz), gamma- (30-85 Hz) and ripple-range (130-250 Hz). These oscillations
are spatiotemporal summations of electric currents of orchestrated cell-populations. The laminar
organization of the hippocampus is quite suitable for recording purposes because of the high celldensity of the pyramidal-layer resulting in a high density of transmembrane currents. We have to
keep in mind that cellular synchrony determines the extracellular rhythms, and what we simply call
electroencephalogram (EEG) or local field potential (LFP) is generally a shadow of intracellular
events and action potentials.
Ripple-oscillations (figure 3.) are characteristic for the CA1-region and even though in vitro
also CA3 can generate them, CA3-ripples cannot be accounted for as traditional ripples, their
frequency in vivo also being slightly smaller than those of CA1 (Csicsvári et al., 1999a). Distinct
oscillations are characteristic of certain behavioural states, ripples are brought about during slowwave sleep (SWS), waking immobility, consummatory and grooming behaviour whereas thetarhythm nested with gamma (figure 3.) usually occurs during exploratory behaviour and REM-sleep
(rapid eye movement sleep or paradoxical sleep, described by Jouvet; Buzsáki, 2006). The structure
of the theta-related EEG also shows species-specific differences. For example theta of the human
REM-sleep is rather “fragmented” (Buzsáki, 2006) and also reflects evolutionarily conserved
navigation strategies, such as that of bats, in which theta is “packed” synchronously with
ultrasound-emission used for echolocation (Ulanovsky & Moss, 2007).
Regarding the theta-rhythm we can claim that its generation is not specific and restricted to
the hippocampus and that probably many oscillators interact while bringing it about, involving also
the septal nuclei with its cholinergic and GABAergic projections to the hippocampus. Also
GABAergic back-projections from the hippocampus to the septum play a role. Based on
pharmacological and behavioural correlates, theta activity is often dissected into type I and type II
theta. Both types occur during locomotion, but only type II that is atropin-sensitive can be recorded
under urethane-anaesthesia when theta is usually evoked by sensory-stimulations, such as a tailpinch (Yoder & Pang, 2005). As determined form partial coherence analysis, even the hippocampal
formation contains two theta-generators, one of them mediated by the entorhinal inputs via the
perforant path terminating in the stratum lacunosum-moleculare, the other by the CA3-Schaffercollaterals, innervating dendritic domains of the stratum oriens and radiatum (Kocsis et al., 1999).
Figure 3.: Examples of hippocampal oscillations. On the left side a ripple is shown (lower trace is
raw signal, upper trace after filtering between 130 and 250 Hz). On the right side a very short
segment recorded during REM-sleep is shown, with characteristic theta- and gamma-oscillations
(lower trace is the raw signal, the upper one is the filtered signal between 30 and 85 Hz). The time
bar for ripples is 0.2 s, for gamma 0.5 second. Note that the amplitude of these events in the mouse
hippocampus is in the mV-range.
The gamma-rhythm is not specific for the hippocampus but can also occur in distinct
neocortical domains. The most well known examples are the visual and the auditory cortices where
the incoming input promotes network oscillations (Gray & Singer, 1989). In their classical
experiment these scientists recorded from the visual cortex of anaesthetised cats with extracellular
electrodes while they projected moving bars onto the retina of the animals. They found that at
certain recording locations the power of gamma showed a tuning-curve depending on the direction
of the bar or arrow they moved. Interestingly, but not surprisingly they also found that the cells
showed very similar tuning curves on that location and those cells which had the same directional
preference showed a quite remarkable phase-locking to the ongoing gamma-rhythm. Gammasynchrony in the visual cortex is not only promoted by sensori stimuli but is also influenced by
stimulus-selection* (Fries et al., 2002), implying that perceptual differences can adjust the level of
“synchrony” and can also gate sensori inputs.
*Stimulus-selection means a “decision” on which one of the stimuli projected onto the eyes separately would
be the dominant one if there are contrast- or illumination-differences between them or if they are projected onto the
retina of the dominant or subdominant eye.
The hippocampal gamma-rhythm in classical situations, such as locomotor behaviour and
REM-sleep, is nested within theta-rhythm. In addition, given parameters, such as amplitude and
frequency of the gamma are modulated by characteristics of theta, such as the phase of theta but
also amplitude of the underlying slower rhythm (Bragin et al., 1995).
It is not entirely correct to speak about a given gamma-rhythm in the hippocampus. It seems
that this structure has two gamma-generators, one of them being the DG (driven by the entorhinal
cortex), the other the CA3-CA1 circuitry, and these two systems interact with each other (Csicsvári
et al., 2003b). It is generally accepted that the UP and DOWN states of the neo- and paleocortical
areas influence these generators. UP-states can drive a specific set of dentate granule cells, which
can oscillate at gamma-frequency and can activate a selective set of pyramidal cells in the CA3
whereas most of the pyramidal cells are inhibited by interneurons. In this scenario the CA3-CA1
gamma- and ripple-generator would be suppressed whereas the generator in DG would be active.
During a DOWN-state, however, the DG would not exert suppression on the CA3-CA1 axis,
allowing the generation of CA3-CA1 gamma and ripples (Isomura et al., 2006). The suppressive
effect of entorhinal inputs is corroborated by the fact that by dissecting entorhinal inputs, the
gamma-power in the CA3-CA1 system shows a strong increase (Bragin et al., 1995). The function
of CA3-pyramidal cells is of utmost importance in the gamma-synchrony of CA1 since by
removing CA3-inputs to CA1, one can extinguish oscillations in the latter (Fisahn et al., 1998). It
seems that both the recurrent excitatory loops within CA3, as well as the monosynaptic drive of
CA3-pyramidal cells onto CA1-pyramids and CA1-interneurons contribute to synchronous network
activities of CA1 (Csicsvári et al., 2003b). Since pyramidal cells show the tightest coupling to the
gamma-rhythm recorded on the same electrode, it has been speculated that the local gamma-power
is the reflection of EPSPs (excitatory postsynaptic potentials) and IPSPs (inhibitory postsynaptic
potentials) in the neighbourhood of the recording site. That way, an analogy with the visual cortex
can be drawn since the local gamma-power would be determined by the actual spatial input and the
receptive field of the given cells of a particular location (Gray & Singer, 1989).
Ultrafast (above 100 Hz) synchrony resembling ripples can occur in the neocortex also
normally but more often under pathological conditions. Epileptic activity is an extreme grade of
ultrafast synchrony. However, ripples represent a physiological mechanism, thought to be important
for memory-consolidation processes. It is estimated that roughly 10-15 % of all hippocampal
neurons can be active during a given ripple, hence during a short time-window spanning no longer
than 100 ms. This extreme temporal synchrony is supposed to bring together cells in the form of
“cell-assemblies” that might be the primary storage-place for memory-traces. Ripples are not
specific for the hippocampus but also occur in the amygdala (Ponomarenko et al., 2003b) and in
certain output routes of the hippocampal formation, such as the subiculum and entorhinal cortex
(Chrobak & Buzsáki, 1996) and unitary analysis confirms that ripples are indeed generated there.
Ripples are thought to be dependent on the CA3 subregion and the Schaffer-collateral inputs and in
slice-preparations spontaneously occurring ripples travel from CA3 towards CA1 (Maier et al.,
2003). Interestingly, one can also induce ripples with LTP-protocols in slices, in which previously
no ripples occurred spontaneously (Behrens et al., 2005). This observation would imply that ripplegeneration relies on cell-assembly formation in the CA3-network. The CA3 region cannot be
considered homogeneous regarding either its histology or its involvement in the generation of ripple
oscillatory epochs. The CA3a and CA3b domains are characterized by extensive recurrent
collaterals that can recruit subsequently more and more cells in the beginning of sharp waves
whereas CA3c would rather be an “output-connector” towards CA1. Interestingly, at least 10 % of
CA3 pyramids need to be recruited in a 100 ms window to have a considerable effect on CA1synchrony in forms of ripples (Csicsvári et al., 2000). Remarkably, lower grade synchrony is
associated with a smaller increase in firing rates of CA3-pyramids than of CA1-pyramids, but this
relation is completely the opposite for higher-grade synchrony (synchrony meaning the number or
percentage of cells firing in a short time-window), which probably also reflects the activity of
recurrent loops. On the other hand, József Csicsvári and his colleagues made very interesting
observations and distinctions based on the actual ripple-amplitude generated in CA1. They
classified ripples into big (>7SD above threshold) and medium size (7SD>size>4SD above
threshold) categories and they saw that high-amplitude ripples are more coherent across different
recording sites of CA1 whereas smaller ripples are not so much. Besides, they found that the
activity of given CA3 neurons could be predictive of the character of the upcoming CA1-ripple.
This leads to the idea that ripples might reflect the coherent and synchronized activity of CA1- (and
also CA3-) microdomains, CA1-neurons being strongly correlated with certain CA3-pyramids on
one hand, on the other hand it suggests that ripples of different amplitude or morphology represent
different activated cell-assemblies. Similarly to gamma, pyramidal cells show the strongest
coherence with “their local” ripples and show much weaker with distant ones whereas interneurons
show a high coherence with distant locations as well, suggesting a more general role for them in the
generation of gamma- and ripple-oscillations.
To nail it down to an anatomical scheme, the story begins in the CA3 where the recurrent
loops gather more and more CA3-cells firing and bringing about a sharp-wave, which is equivalent
to the excitatory input via the Schaffer-collaterals. In CA1 this input excites pyramidal cells and
interneurons in parallel (feed-forward excitation and feed-forward inhibition). The inhibitory effect
of the interneurons on the pyramidal cells then will be delayed by one synaptic delay with respect to
the maximal excitation of pyramids. The CA1-pyramids also activate CA1-interneurons, which
exert inhibition on them (feed-back inhibition). The interneurons hyperpolarize the cells, which
ensures the relief from the Na+-channel block and therefore pyramidal cells can fire synchronously
again, activating again the interneurons that will inhibit them. The “energetic drive” is the CA3 and
the ripple lasts until the underlying sharp wave lasts.
We know very little about the subcellular and molecular details of the generation of ripples
and in many respects of gamma-rhythm. Besides chemical neurotransmission recent studies also
suggest the involvement of gap junctions in this process (Draguhn et al., 1998; Traub & Bibbig,
2000; Whittington & Traub, 2003), even though the importance of “electrical synapses” in higherorder brain functions of phylogenetically more developed species has been underestimated for a
long time. Gap junctions are molecular complexes that allow for an intercellular transport of
molecules smaller than 2 kDa. The gap junctions are usually formed from two hemichannels,
expressed on the neighbouring cells, these are called connexons, and each connexon is built up from
six connexin molecules (Kandel et al., 2000). Since there are many types of connexins expressed in
the brain, the subunit-composition of each connexon can vary to a great extent, this also leads to a
great versatility of the conducted currents. There are connexins, for instance connexin36, which are
expressed exclusively on neurons whereas others, for instance connexin43, are expressed both on
neurons and glial cells. Pannexins are also supposed to form gap junctions (Bruzzone et al., 2003).
As far as we know the mostly known pannexins: pannexin1 and pannexin2 are specific neither for
pyramidal cells nor for interneurons, but can be expressed in both neuronal populations (Vogt et al.,
2005). Connexin36, however, seems to be more specific for interneurons (Hormuzdi et al., 2001).
Certain connexins, such as connexin36 are located mainly on dendrites whereas pannexins are
supposed to be mostly located on axons. It seems that both axonal (Schmitz et al., 1996) and
dendritic gap junctions can facilitate the rhythmogenesis in the gamma- and ripple-frequency range
since action potentials or postsynaptic potentials can propagate between cells practically without
any synaptic delay. Slices from the hippocampus of connexin36 knockout mice displayed reduced
gamma-oscillations in vitro (Hormuzdi et al., 2001) and certain alterations in ripples as well (Maier
et al., 2002). The connexin36-deficient mutant mouse also showed diminished gamma-power
during exploratory behaviour (Buhl et al., 2003).
Based on the findings of Dietmar Schmitz it was speculated that the average number of gap
junctions that an axon forms with other axons is actually very low, 1 or 2. However, as also
modeling studies (Traub & Bibbig, 2000) show, even that number can make ripple-oscillations very
robust since the excitatory output of the network would not depend so much on the individual
excitation of pyramidal cells. Roger Traub and his colleagues developed an interesting
multicompartmental model composed of both principal cells and interneurons in which in addition
to chemical synapses axo-axonal gap junctions are also present between principal cell axons. Based
on their computation, axons can “fire” with a very high frequency, even if the cell somata cannot
follow this high frequency. The major requirements for this are a critical density of axo-axonal
coupling (the before mentioned 1-2, on average 1.6 per axon), a sufficient conductance of the gap
junction channels (so that the action potential can jump quickly from one axon to the other or at
least can induce spikelets) and some ongoing firing of certain pyramidal cells, which becomes more
robust upon dendritic depolarization occurring in the form of sharp waves. Their prediction is that
the frequency of the evolving “rippling” is higher when either the conductance of a single gap
junction is increased or the density of the gap junctions on an axon is increased. However, by
increasing the strength of inhibition, the oscillation-frequency approaches the gamma-range, so it
slows down. The weaker inhibition also favours the antidromic propagation of action potentials
whereas stronger inhibition and lower frequency is favourable for orthodromic spike-propagation.
Even though the principle of axo-axonal coupling is thoroughly discussed nowadays (however, with
a lot of debate), it raises certain problems regarding the specificity of output processing since
principal cells are considered the output neurons of our brain.
Another remarkable feature of the gap junctions is that they connect almost exclusively
neurons of the same type, either principal cells or different subclasses of interneurons (Blatow et al.,
2003). This might indicate a developmental role for gap junctions as well, expressed already at
earlier stages of the ontogenesis in precursors of specific neuronal subclasses.
Even though gap junctions might have a role in the generation of gamma- and ripplerhythms, inhibitory neurotransmission is indeed involved in oscillogenesis. One can record very
small amplitude intracellular membrane potential-fluctuations in synchrony with ripples and the
polarity of these oscillations reverses around the Cl--equilibrium potential, indicating that
GABAergic transmission is most probably involved in their generation (Ylinen et al., 1995).
Besides, ripples show changes after the application of different GABAergic agonists and
antagonists, such as benzodiazepines, zolpidem and flumazenil (Ponomarenko et al., 2004). These
and other experiments (Penttonen et al., 1998) also suggest that IPSPs and also EPSPs on both
pyramidal neurons and interneurons contribute to the extracellular LFP-oscillations, be they ripples
or gamma-oscillations.
Network synchrony in vitro
Many of the mentioned rhythms also exist in brain slices, suggesting that the mechanisms
bringing them about rely on smaller circuitries. Spontaneously occurring ripples can easily be
recorded from CA3 and CA1 in slice-preparations (Draguhn et al., 1998), but they can also be
induced by electrical stimulation. The application of the GABAergic blocker gabazin actually
facilitates the ultrafast oscillations, even if not in the form of sharp wave-ripples (SPW-ripples,
Maier et al., 2003) which often leads to the hypothesis that GABAergic transmission is not
necessary for the generation of high-frequency oscillations. Besides, since these oscillations are
sensitive to the gap-junction-blockers octanol and carbenoxolon, it has been hypothesized that axoaxonal gap junctions may contribute to their generation (Draguhn et al., 1998; Maier et al., 2003).
On the other hand, the slicing procedure can destroy longer axons which might also be coupled via
gap junctions. One might thus underestimate their importance in network synchrony or depending
on the orientation of slices one might get different results.
Gamma-oscillations can also be induced by tetanic stimulation or pharmacologically in
hippocampal (Fisahn et al., 1998; Bartos et al., 2007), entorhinal (Cunningham et al., 2003) and
auditory cortical slices (Cunningham et al., 2004; Traub et al., 2005). The most commonly used
drugs in these protocols are kainate, domoate and the cholinergic agonist carbachol. However, one
must be aware that the mechanisms underlying the rhythms in these in vitro models do not
necessarily match each other or either the real in vivo situation. Carbachol for instance activates
metabotropic cholinergic-receptors on pyramidal cells first and interneurons are activated
secondarily via the principal cells. This type of oscillation is both sensitive to the AMPA-receptor
blocker NBQX and the GABAA-blocker bicuculline (Fisahn et al., 1998). However, when the
network is activated via kainate-receptors, pyramidal cells and interneurons are activated
simultaneously and since kainate can provide sufficient excitation, it is not sensitive to NBQX
(Bartos et al., 2007). Furthermore, mechanisms can be different in hippocampal subregions. Last,
but not least, gamma-oscillations in slice-preparations occur in absence of theta-rhythm, which is
normally not the case in vivo.
Interestingly, interneurons can also synchronize when glutamatergic neurotransmission is
blocked in the network, indicating that via GABAergic inhibition they can pace each other (Traub et
al., 1998). However, since GABA might also have a shunting inhibitory effect and gap junctions
may also contribute to synchrony, we cannot clearly attribute a major role to phasic inhibition in
this process.
Slower oscillations can also be induced pharmacologically, in neocortical preparations for
instance where the application of carbachol evokes synchronized intracellular membrane potentialoscillations in mutually coupled interneurons (multipolar bursting cells) in the theta-frequencyrange (Blatow et al., 2003).
However, not only electrophysiological paradigms can be helpful in understanding the
principles of network synchrony. A newly developed method, the two-photon microscopy offers an
excellent opportunity to investigate synchrony in smaller local circuits using optic principles. The
application of Ca2+-sensitive dyes allows for pursuing the activity-state of a given neuron while the
use of a shorter wavelength irradiating laser source enhances the penetration depth of the light
beam, thus expanding the histological volume one can examine (Denk & Svoboda, 1997). One can
then correlate the state of neighbouring neurons with a relatively good time-resolution, in the range
of tens of milliseconds.
Multi-electrode arrays (MEAs) can also be used to record from several sites of an in vitro
preparation (Mann et al., 2005). The application of voltage-sensitive dyes can also inform us about
the coherent activity of bigger cell-populations, their depolarized and hyperpolarized states.
However, their use is limited due to the toxicity of the dyes.
Brain synchrony in vivo
Neuronal synchrony in vivo can be measured with a wide array of methods. The EEG can be
recorded either on the skull or from the brain in situ using implantable electrodes or grid-electrodes
which are usually applied to the cortical surface. In certain pathological conditions, such as
pharmacologically untreatable epilepsy, intracerebral recordings can help localize the seizure-center
and can lead to a full recovery of the patient after the operation. We have to be aware that with
skull-EEG one obtains only attenuated signals that are furthermore restricted to a lower frequencyrange due to the bone capacitance. Besides, signals are usually summations of a larger brain surface,
which can lead to imprecision. The magnetoencephalography (MEG) uses the principle that
alternating currents (such as our neural postsynaptic and action potentials) generate magnetic fields
which can be recorded with loop-like devices. Since for the recording an optimal orientation of the
magnetic field is required, the MEG is usually more straightforward in following the activity of
brain sulci and fissures than that of planar cortical regions (Buzsáki, 2006).
Implantable electrodes are of wide use in neurophysiology of animals, especially in rodents
but also in monkeys. In general, one uses extracellular electrodes, such as single-wires or
stereotrodes, the latter being better in unitary recordings since they enhance the efficiency of unitseparation. The most commonly used is the tetrode. Tetrodes consist of four very thin metal wires
that are twisted around each other. The thickness of one such wire is precisely in the range of
cellular size. Thus, the distinct channels will give an excellent spatial resolution since action
potentials recorded on different channels will be of different amplitude due to the very steep decay
of the recorded action potential with distance (McNaughton et al., 1983; Buzsáki, 2004). Indeed,
tetrodes were first applied to reconstruct spatial arrangement of closely spaced cells. The
“extracellular” action potential is not simply a mirror image of the intracellular one but can be
approximated with the first derivative of it in time. With the help of tetrodes one can relate LFPoscillations with single cell data. Silicon probes also show the stereotrode principle, however, their
major aim is different. Their big advantage is that one can record from many histological layers
simultaneously (Csicsvári et al., 2003) and thereby reconstruct current-source density (CSD)
profiles to reveal what kind of currents underlie the rhythms and to determine in which layers they
are generated (Mitzdorf, 1985). This computation can be complicated since the relation between
intracellular currents and their extracellular “reflections” are quite complex. In case of an action
potential the cell gets temporarily positive relative to its resting potential, due to the cation-influx.
Since these charges stem from the extracellular space, their close environment gets temporarily
negative, that is why we normally see action potentials as negative deflections on extracellular
recordings. However, LFP-oscillations are not only a summation of action potentials of the
neighbouring neurons, but postsynaptic potentials of large neuronal populations most probably have
an even larger contribution. When a cellular domain gets depolarized, the extracellular space around
shows negativity. However, on other parts of the cell positive charge will be released to compensate
for the depolarization, that is why the extracellular space surrounding those cellular domains will
become slightly positive. If we record from different histological layers simultaneously, we
normally find these sink-source pairs. In this case there will be an active sink and a passive source
representing a so-called return current. This can also be the opposite, in case of inhibitory
neurotransmission. To find out, which is the active and passive current of the sink-source pair, one
usually needs unitary analysis as well.
The optical methods used in in vivo functional imaging have a much lower spatial and
temporal resolution than electrophysiological methods. Nevertheless, they can provide useful
information on the function of distant brain regions in different cognitive tasks. Functional magnetic
resonance imaging (fMRI) and the resultant blood oxygen level dependent (BOLD) signal relies on
the principle that the blood supply of active brain regions increases (Kandel et al., 2000; Buzsáki,
2006). Due to the delay of intracellular cascades involved in blood-vessel regulation, the temporal
resolution of this method is in the range of seconds.
Interneurons in oscillations and plasticity
Certain interneurons are active at distinct behavioural states and distinct phases of
oscillations (Table 1.). One of the most synchronous oscillatory events of the brain is the previously
described “SPW-ripple” complex. Axo-axonic cells are active usually in the very beginning of the
ripples and they seem to be silent afterwards (Klausberger et al., 2003). Both PV-positive basket
cells and bistratified cells increase their firing during ripples (Klausberger et al., 2004), but CCKpositive interneurons (CCK-cells) do not change their activity (Klausberger et al., 2005) whereas OLM cells are silent during these events. Interneurons are also active at distinct phases of the thetacycle (Table 1.), thereby leading to the assumption that CCK-cells might be important in the phaseprecession of pyramidal cells for instance and that O-LM cells might be important in thetageneration. The methodology used in the Somogyi-lab allows for a good post hoc identification of
the recorded cell-type but urethane-anaesthesia used in their experiments is a certain limitation.
Tetrode-recordings from freely behaving animals allows for the distinction between pyramidal cells
and interneurons, but the identification of interneuron-subtypes can be more problematic. Studies
from the Buzsáki-lab confirm that interneurons also tend to fire on the ascending phase of ripplewaves (whereas pyramidal cells in the troughs) and generally on the descending slopes of thetawaves (Buzsáki et al., 2003 for mice; Csicsvári et al., 1999b for rats), as we see for PV-positive
basket cells in anaesthetized rats.
Cell type
Basket cell
Descending (PV),
Increases, ascending phase
or ascending (CCK)
(PV), does not change (CCK)
Chandelier (axo-axonic) cell Peak
In the beginning
Bistratified cell
Increases, ascending phase
O-LM cell
Table 1.: Interneurons functioning in distinct ways during different oscillations. Table is based
mainly on data obtained in the Somogyi-lab.
Very important distinctions between interneurons can be made according to biochemical
markers that they express. The PV-positive basket cells also express certain K+-channels (Kv3.1
and Kv3.4) that ensure their fast repolarization after action potential generation (Baranauskas et al.,
2003). These K+-channels also ensure that they can fire with a high frequency, in slice-preparations
up to 100 Hz. CCK-cells on the other hand can follow a frequency only up to about 40 Hz
(reviewed by Freund, 2003). Glutamate receptor expression on interneurons is also different: PVpositive basket cells express kainate- and AMPA-receptors at a great density, but only very small
amount of NMDA-receptors whereas CCK-cells have a much higher density of NMDA-receptors
on their surface. Therefore, PV-cells might be more static actors whilst CCK-cells more plastic.
CCK-cells also express CB1 (cannabinoid) receptors, to whom an important role in DSI
(depolarization-induced suppression of inhibition) has been attributed. When a pyramidal cell is
active and emits “bursts” (let us imagine a place cell at a given location during active exploratory
behaviour), cannabinoids are synthesized in the pyramidal cell and diffuse to the inhibiting basket
cells. Since CCK-cells express cannabinoid receptors, they can sense the endocannabinoid. This
leads to a decreased GABA-release from their synaptic terminal (Klausberger et al., 2004).
Therefore the given pyramid is not inhibited so much and it can emit spikes even with a smaller
degree of excitation. So it can slide downwards on the slope of the theta-wave, and thus accounts
for phase-precession. Not surprisingly, the consumption of marihuana leads to learning- and
memory-deficits. Application of cannabinoid agonists in vivo resulted in decreased LFP-power
especially in the ripple-frequency range, which was not associated with any change in the
discharge-rates of neurons. However, the coordination in cell-assemblies and the spiking-regularity
was influenced by the drugs explaining the oscillatory-findings and also the learning-deficits
basket cell populations
may perform distinct
and PV-cells express
different receptors on
their synaptic terminals
electrophysiological properties
are different as well.
Picture comes from
Freund & Katona, 2007,
Neuron, artwork was
done by Gábor Nyíri.
on the synapses formed by the diverse basket cell-population: PV-cells having mainly α1, CCKcells α2 on their postsynaptic targets. It is important to note that α2 subtypes of GABAA-receptors
are responsible for the anxiolytic effect of benzodiazepins (reviewed by Freund, 2003).
I mentioned the absence of plasticity in interneurons that do not or only weakly express
NMDA-receptors. Certain LTP paradigms almost exclusively point to the importance of NMDAreceptors in the induction of Hebbian LTP (which means that pairing stimulation of a given
pathway with the depolarization of the postsynaptic cell leads to strengthening of the “targeted”
synapses). This can be more or less true for CCK-cells since they express NMDA-receptors in a
good quantity which conduct Ca2+-ions at a relatively positive membrane potential. However, in
case of oriens or alveus-associated interneurons that only have Ca2+-permeable AMPA-receptors
activated at a more negative voltage, LTP is anti-Hebbian, meaning that pairing of
hyperpolarization with the stimulation of a given pathway leads to synaptic strengthening (Lamsa et
al., 2007). This holds true for PV-cells as well, due to their low-level NMDA-receptor expression.
There are also other differences between PV- and CCK-cells that suggest a more plastic role
of the latter type in network phenomena. Ca2+-channels on the presynaptic terminals of PV-cells are
of the P/Q-family, with a strong coupling between these channels and the Ca2+-sensor required for
vesicular GABA-release. This kind of mechanism usually excludes short-term facilitating effects on
these synapses. In contrast, CCK-cells express N-type Ca2+-channels on their presynaptic boutons.
These channels are located further away from the sensor, thereby allowing facilitation to occur on
those GABAergic synapses that are formed by CCK-cells (reviewed by Freund & Katona, 2007).
CCK-cells are also modulated by a number of modulatory afferents, such as serotonin (5HT).
Besides, GABAB-receptors expressed on their axon-terminals extrasynaptically can inhibit GABArelease in an autocrine manner. These observations attribute a more rigid role to PV-cells in
network synchrony whereas the CCK-cells would have a more delicate, tuning role, which requires
certain plastic capabilities of course.
Proposed functions of the hippocampus
We have seen the anatomical architecture and histological constitution of the hippocampus
and have hints, how these can serve electrophysiological phenomena, such as oscillations or
plasticity. Now we have to see, what cognitive tasks this structure performs and if there is any
correlation between network synchrony and cognitive behaviour. The hippocampus has been
implicated in many cognitive functions and depending on the animal species the hippocampus
might perform different sets of “intellectual” tasks. The importance of the hippocampus in learning
was first supposed in the mid of the 20th century when clinical and neurosurgical cases proved that
without an intact temporal lobe, learning of new information is impossible. A patient often referred
to (H.M.) suffered a complete loss of the capability of acquiring new information after a surgical
bilateral lesion of his temporal lobes, even though his memories from his life before the operation
were more or less preserved (Scoville & Milner, 1957)*. Another important cause for hippocampal
destruction can be a herpes-encephalitis, which destroys the temporal structures with a great
propensity, or the Wernicke-Korsakow-syndrome, a dietary B1-vitamin deficiency. The latter,
however, is not specific for the hippocampus but rather to its fornical and mammillary projections.
The recurrent wiring of the hippocampus (especially that of CA3) via its activity-related energy
consumption also makes it quite vulnerable to hypoxia. Probably it is also related to the
hippocampal wiring that the seizure threshold for this brain area is low and epilepsy eventually
leads to neurodegeneration. The pathological changes of Alzheimer Disease in its classical form are
first seen in the temporal lobe and also hippocampus (for a complete view on the pathology of
hippocampus see Szirmai, 2005). All the named diseases perturb learning-mechanisms and
memory. An interesting aspect of H.M.’s postoperative deficits reflects a contextual retrieval
problem. The patient remembered persons he had just met before, as long as these persons were in
the same context, for instance the same room. When they moved to a new room he could not
recognize the same persons any more.
*However, other symptoms caused by the loss of the temporal lobes might not be directly related to the
hippocampus proper. The Klüver-Bucy-syndrome for instance, which is characterized in monkeys, is due to the loss of
the corpus amygdaloideum. Since the amygdala is involved in fear conditioning, the animals usually exhibit a loss of
fear even towards noxious or dangerous factors, such as snakes. In addition, they become hypersexual and develop oral
The classical idea is that the hippocampus would be a temporary storage place of newly
acquired information and thereby the residing place of memory traces shortly after their acquisition.
According to this view, the information would be “written out” from the hippocampus to
neocortical areas during SWS (Buzsáki, 1989; Buzsáki, et al., 1994). This memory-consolidation
process would require the strengthening of synapses between hippocampal output neurons and their
targets. Ripples represent a highly synchronized hippocampal activity and it is supposed that they
have an important role in the consolidation process. Besides, pyramidal cells often fire with a high
frequency during ripples, which is quite suitable for modifying neocortical synapses (just as an in
vivo LTP-protocol). It was shown that performance in certain cognitive tasks increases after sleep,
and the performance correlates well with the time spent in SWS (Buzsáki, 2006). However, ripples
can also occur in awake immobile periods and sometimes also during exploratory behaviour
(O’Neill et al., 2006). The role of these ripples (with theta-rhythm interspersed SPW-ripple
complexes) would be to provide a better update of the recently learned environment and to cope
with the huge place-related information coming in during exploration. During REM-sleep, the
period when normally dreams occur, most of the potentiated cells also show a reactivation (it can be
considered a replay-mechanism). Since inhibition is somewhat looser during sleep than during
wakefulness, some previously not perfectly potentiated output neurons can also be activated. This
effect might be responsible for the somewhat confounding and very often quite creative character of
our dreams*.
*As we shall later see, memories are often thought to be stored in the coherent activity of cell-assemblies, via
their well-correlated activity. As Freud points out in many of his clinical cases (“Dora: An analysis of a case of
hysteria” or “Little Hans”), subjects or locations in dreams are mainly transferred to other subjects or places (which
could coalesce with a slightly perturbed assembly-coordination). On the other hand he implies that in many cases the
logical relations between subjects are preserved, sometimes are turned to opposite and mimic that of the original
persons and objects. Thus, it might be that more stable connections between cell-assemblies would be representing
logical operations.
According to evolutionary psychiatrists, dreams could also have an adaptive role during
phylogenesis by providing subjects with “novel ideas” during their everyday struggle for potential
resources (Stevens & Price, 1996). An alternative proposal relies on the analysis of the most
frequent dream contents. The fulfillment of basic instincts in many dreams (sexual contents), the
quite frequent archetypal contents (such as proposed by Jung) and behavioural patterns often
performed when arousal happens in the middle of a dream (“flight and fight” for instance) imply
that genetic determinants can be involved in the programming of dream contents. These programs
could have a role in “pretraining” subjects in infancy or childhood for dangers lurking around them
in prehistoric times (actually a similar thing happens in the well-known movie “Matrix”). However,
evolutionary studies also suggest that REM-sleep has something to do with the economy of brain in
terms of memory storage. Certain egg-laying mammals, representing a very early stage in the
mammalian development do not have REM-sleep at all, on the other hand they have a huge
neocortex compared to their overall body-size. It may well be that sleeping also has a role in
removing unnecessary memory-traces, and keeping only the really necessary ones, thereby
minimizing the spatial requirements for memory storage (for more details see Stevens & Price,
However, certain theories imply that the hippocampus is also involved in memory retrieval.
According to these ideas the two hippocampi would not be equal but one of them rather specialized
for memory acquisition and the other to memory retrieval. In this case the latter would function as a
“librarian”, who, according to the “memory-indexing hypothesis” would provide access to the
information stored in the neocortex (Lytton & Lipton, 1999).
An interesting theory proposes that the information can be stored in the form of cellassemblies, like small engrams and with time these engrams would be transported along the
temporoseptal axis of the hippocampus. According to this model the oldest information would be
stored around the temporal pole of hippocampus whereas newer engrams would be located closer to
the septal pole (temporoseptal engram shift model, Lytton & Lipton, 1999). However, a theory like
this might be difficult to reconcile with the stability of place fields observed in rodents and with the
memory-indexing hypotheses since it would require a day-to-day update and continuous change in
the indexing synapses’ strength. However, certain imaging studies seem to support this idea in
human subjects. Since the rodent and human hippocampus is of different size and the proposed size
of engram-modules is roughly the same, the information would need more time to travel along the
human hippocampus than it would need in rodents.
In rodents, however, the hippocampus performs navigational tasks as well. As an animal
explores and learns a new environment, it uses certain navigation strategies. The intellectually most
demanding is called taxon navigation that uses allocentric (meaning external) cues, usually distal
landmarks for its spatial navigation (for example trees, hills, the sun, rivers, etc.). A major hallmark
of this strategy is a multimodal perceptual integration of all incoming environmental information.
Indeed, the hippocampal formation receives extensive innervation from brain regions involved in
visual and olfactory processing. As an animal is getting acquainted to an environment, it develops
alternative strategies to navigate in it. First, it might memorize turning or intersection points of the
maze, just as we learn to find our goals using streets of a town. This form of orientation is called
praxic navigation and is usually related to the striatum. In the next step of abstraction the animal
plans its trajectory based on earlier experience just by knowing its initial location, the goal location,
its speed and the direction of its own movement. This type of navigation, also referred to as path
integration, does not require external cues, but egocentric (or idiothetic) ones. Different brain
regions might be involved in this form of orientation, supposedly mainly the entorhinal cortex. In
the next part we shall discuss the different cell types involved in the navigation (for a more
complete view an spatial navigation see Redish, 1999; on path integration see McNaughton et al.,
2006). A navigation strategy for a given environment does not exclude others, the animal might
switch back to the hippocampal one when its striatum is compromised in its function, as was shown
by experiments in which lidocain-injections were administered (reviewed by Redish, 1999).
Distinct subregions of the hippocampus might perform different tasks in orientation and
spatial navigation. Nakazawa et al. (2002) showed that a CA3 region-specific NMDA-receptor
knockout mouse is impaired in a pattern completion task (after an already learned environment is
represented with certain external cues, some of them are removed and then an experimental subject
has to recognize the same environment based on the remaining cues). According to their hypothesis,
this would be accomplished by the autoassociator CA3-loops, which can “fill in” the missing gaps
with the help of their recurrent connections (reviewed by Nakazawa et al., 2004). The CA3 also
seems to be important for the acquisition of one-time experience (Nakazawa et al., 2003). Another
phenomenon, the pattern separation (discrimination between superficially similar but basically
different environmental arrangements) would be performed by the dentate gyrus (McHugh et al.,
Cells specialized for navigation
In the seventies an interesting observation was made by O’Keefe and Dostrovsky. They
found that the activity of a great percentage of hippocampal pyramidal cells is bound to the location
of a rat in a given recording environment (O’Keefe & Dostrovsky, 1971). They christened them
place cells since they were only active when the rat was at a given location and remained silent in
other places (figure 5.). In parallel, other cells were described in certain thalamic nuclei, in the
mammillary nucleus and in the retrosplenial cortices that fired only when the animal’s head faced a
certain direction. These cells were named head-direction cells (Ranck, 1984). Both the animal’s
position and the head-direction that usually indicates the direction of motion contain indispensable
information for path integration.
Figure 5.: Place cells are usually represented by firing rate maps. A
typical CA1 place cell (picture is from Kentros et al., 2004, Neuron) has
a single receptive field where its firing is maximal (peak frequency,
indicated with dark blue) while its activity sharply decreases with
increasing distance from the centre. Different species have different
place field sizes.
Place cells can be found in the CA3 and CA1 areas as well. However, their characteristics
are somewhat different. One should note that place fields are context-dependent. They display a
given field in a specific environment but they can fire in a different place in a contextually different
one. Besides, the animal’s near past and future can also influence their activity. This is called
retrospective and prospective coding (Wood et al., 2000). Place fields might be bidirectional or
unidirectional (normally it is only visible on tracks where the animal can explore a given location
only from two directions and cannot make “junction crossings” such as in open fields or more
realistic environments). What makes place cells interesting with respect to oscillations is their
phase-precession. This means that while the animal approaches and traverses the receptive field of a
given cell, the cell emits spikes on earlier and earlier phases of subsequent theta-cycles. This
phenomenon is often explained by the interaction of two oscillators, a somatic and a dendritic one,
the former representing perisomatic inhibition and the latter dendritic excitation upon
environmental exploration. The dendritic excitation depends on the animal’s spatial position. Since
the extracellular local field would be generally composed of the interference of the “two thetaoscillations” but the action potential emission is rather determined by the excitation strength which
invades the soma upon dendritic depolarization, the spikes come earlier and earlier, as the drive is
getting stronger (Harris et al., 2002). As the animal traverses many place fields while moving in the
environment, several place cells are activated in a sequence. As the animal reaches the center of one
place field, cells which predict the animal’s future location are already activated but the neurons
representing the present and near past locations are still active, although on different phases of the
theta-rhythm. Given the principle of phase-precession, there would be many cells firing in one
theta-cycle, in different phases of the ongoing theta-rhythm. Thus, both the animal’s past, present
and future is represented this way in a compressed time-scale and to pack this spatial information in
cell ensembles the theta-clockwork seems to be a prerequisite (Buzsáki, 2006).
There are several models describing the coordination of neurons in this cell-assembly. The
pacemaker-model suggests that the cellular activity would solely be determined by an external
pacemaker representing environmental input. However, the cell-assembly model proposes that there
is also an internal coordination between members of the cell-assembly in the hippocampus itself
(Dragoi et al., 2006). According to the cell-assembly model the precision of information processing
would be enhanced, since the firing-order of pyramidal cells would also be determined by the
synaptic interactions between assembly members. Taken together, it appears that spacerepresentation involves distinct computational strategies within the same system: spatial location is
represented by the phase of the theta-cycle when a given place cell discharges (temporal- or phasecode), by the spike frequency generated by a cell (frequency-code) as the animal gets closer to the
center of the place field and by the combination of active cells at a given time thus denoting a cellassembly (population code).
Place fields are not rock-solid entities, they can change in a dynamic way. The stability of
place fields is strongly influenced by the attention level of the animal when it learns its
environment. For example, the performance of an intellectually demanding task in a maze leads to
more stable place fields (Kentros et al., 2004). In an interesting experiment Dragoi and his
colleagues used LTP-protocols on the Schaffer-collateral inputs to modulate place cell activity of an
already learned environment. This way they found certain changes and rearrangements of place
fields, which in many cases turned out to be reversible (Dragoi et al., 2003). As also these
electrophysiological insights suggest, NMDA-receptors play a role in place encoding. Indeed, mice
with a CA1-specific NR1-deletion display smeared hippocampal place fields (McHugh et al., 1996).
As mentioned earlier, animals may recognize the same environment even after slight changes. This
is generally reflected by the behaviour of place cells that fire in similar locations although small
alterations are carried out in the environment. However, the firing frequency of pyramidal cells
might change, this is called partial or rate remapping. When the environment is changed to a
different one, cells do not preserve their original place field but fire on distinct locations and with
distinct characteristics. In this case also the spatial interrelation between distinct place fields is lost
compared to the original environment. This is called global remapping, which implies a contextual
shift regarding the animal’s place-representation. These phenomena can be investigated with socalled “morphed environments”, which involve gradual alterations on the maze the animal learns
(Leutgeb et al., 2005).
Place cells are not only active during exploration but in sleep they show “reactivation”. As
we have seen for the cell-assemblies during theta-rhythm, it has been proposed that the sequential
order of reactivated cells firing inside ripples during SWS reflects the order of activation during the
exploration (Buzsáki, 1994). However, when reactivation occurs in ripples of waking immobile
periods, the reactivation order is just the opposite of the “place-acquisition-order” (Foster and
Wilson, 2006). It is thought that this “reverse replay” would enhance the efficiency of rewardrelated learning. Since normally an animal has to reach its target-location first to get a reward, the
reverse-order would ensure the activation of cells related to reward (maybe somehow connected to
dopamine-release) already in the beginning of an oscillatory epoch and thus it would “mark” (so to
say enhance the potentiation on) the whole sequence of the activated place cells.
Sometimes one can find cells with more complex properties in the parahippocampal
structures, such as the pre- and parasubicular cortices. These include the TPD-cells (thetamodulated place-by-direction cells, Cacucci et al., 2004). The activity of these neurons is not just
simply a function of the animal’s location but also shows a dependence on their head-direction.
Interestingly, in remapping experiments the two modalities were independent of each other since
place-related activity showed global remapping while the directional preference of these cells
remained constantly the same.
A recent discovery suggests that place-related activity is not unique for pyramidal cells but
can be found for interneurons as well (Ego-Stengel & Wilson, 2006; Maurer et al., 2007). It has
been proposed that those interneurons can inherit activity-patterns from the innervating pyramidal
cells but the function of this phenomenon is still unknown. In many cases interneurons show phaseadvancement, meaning that they are activated at later and later phases of subsequent theta-cycles.
This phenomenon is explained by the differential kinetics of excitatory and inhibitory drive that
interneurons receive, which can be different from that of pyramidal cells.
In 2005 an interesting cell type was found in the entorhinal cortex, a structure that provides
a substantial input to the hippocampus. These cells discharge when the animal is located in the
vertices of a hexagonal spatial matrix and are called grid cells (Hafting et al., 2005). The geometry
of this system can be described with three major parameters; the grid spacing (the distance of
vertices from each other), the phase of the grid (describing the orientation of the matrix or the axes
of the lattice) and the gridness score (describing how well a given cell can be fitted with grid-like
properties). Interestingly, grid cells recorded from the same location show very similar features
(figure 6.) and there is a correlation between anatomical position in the dorsomedial entorhinal
cortex and the grid spacing for instance. In one report the grid spacing was also correlated with
intrinsic cellular properties, such as resonance-frequency in the theta-range (Giocomo et al., 2007),
implying a relation with oscillations. The formation of grid-structure is dependent on distal cues and
landmarks but it persists without them (for example in darkness). By rotating cue-cards in the
environment the grids move together with them, suggesting a remapping-phenomenon.
Figure 6.: Grid cells from the
entorhinal cortex, picture from
Hafting et al., 2005, Nature.
On the upper part one can see
firing rate-maps and grid centers of
three grid cells. In the lower row
spatial cross-correlograms for
pairs of three distinct grid cells
recorded on the same tetrode are
seen. The orientation and spacing
of the grids are quite similar to
each other, as shown by the crosscorrelograms and grid centres can
be superimposed on each other by a
simple translocation.
The entorhinal cortex also possesses head-direction cells and cells that comprise both gridlike- and head-direction-properties. These cells are the so-called conjunctive cells. Interestingly,
when researchers rotate the cue-configuration in the animal’s environment, grid cells and headdirection cells rotate their tuning-curves to the same degree, suggesting that they are involved in the
same sort of place-representation (this behaviour is different from that of the TPD-cells where after
remapping directional tuning remains the same even with a different place-preference). The activity
of the conjunctive cells is modulated by the speed of the animal. Altogether these facts suggest that
the entorhinal cortex could be sufficient for the generation of path integration (Sargolini et al.,
2006). Thus, the entorhinal cortex would provide a spatial metric system for navigation while the
role of the hippocampus would be to integrate multimodal information from the environment.
Which mechanism is the primary one, needs to be verified.
The importance of PV-positive interneurons and their involvement in learning
In the previous parts I summarized current knowledge on hippocampal oscillations and
explained with a few examples how these oscillations can be related to plasticity and to the roles the
hippocampus performs in behaviour. But is there anything known regarding the link between
hippocampal interneurons specifically and behaviour?
PV-positive interneurons (PV-cells) comprise roughly 25 % of the hippocampal interneuron
population, this number and their diversity (both axo-axonic, bistratified and a great percentage of
basket cells belong to them, and many O-LM cells also express PV weakly, Klausberger et al.,
2003) also suggest an important role for hippocampal physiology. Indeed, malfunction of PV-cells
may be involved in the pathogenesis of human psychiatric disorders, such as schizophrenia (Zhang
& Reynolds, 2002). Basket cells are responsible for perisomatic inhibition and together with the
axo-axonic cells their basic presumed function would be the output-control of principal cells.
However, axo-axonic cells might not exclusively be inhibitory, but in many instances they are
excitatory. PV-cells have an exquisitely high density of excitatory terminals on their dendritic tree
compared to other interneuron types (Gulyás et al., 1999, see also figure 7.), which suggests a very
reliable coupling to synchronous excitatory events.
We have seen that PV-cells are coupled to ongoing oscillatory rhythms, both to theta and to
ripples, but different PV-positive subpopulations might be involved in different ways in these
processes. Axo-axonic cells are active just in the beginning of ripples and stay silent afterwards
whilst PV-positive basket cells increase their activity throughout ripple activity, just as bistratified
cells do. PV-cells express NMDA-receptors at low levels, therefore they are considered less plastic
than CCK-cells (Freund, 2003).
In our laboratory Elke Fuchs generated transgenic mice that express Cre-recombinase in PVcells. By crossing these mice with floxed GluR-A mice (Zamanillo et al., 1999) she obtained
offsprings in which PV-cells lose their GluR-A-expression (PV-GluR-A KO mice). She also
generated and analyzed GluR-D knockout (GluR-D KO) mice, with the advantage that normally
GluR-D-expression is almost restricted to PV-cells. In vitro electrophysiological insight into these
Figure 7.: PV-positive cells receive a robust excitatory innervation on their dendrites compared to
CB- and CR-positive cells (electronmicroscopic studies by Gulyás et al., 1999).
mice revealed that they have smaller AMPA-currents on their PV-cells than normal mice have and
pharmacologically induced gamma-oscillations were decreased in their power in both mouse
mutants (Fuchs et al., 2007). She also analyzed the animals in learning-paradigms, in spatial
working memory (T-maze), reference-memory (Y-maze and hidden platform Morris water-maze)
tests and novel object recognition*. Interestingly, the animals underperformed in spatial working
memory (figure 8.B) and novel object exploration but were practically normal in reference memory
(figure 8.A), indicating a defect in the early phases of memory-acquistion.
*In the T-maze an animal initially finds a food pellet in a given arm of the maze. In the next run the food pellet
is placed in the other arm and the animal has to remember for a short time that it already consumed the food in the first
arm. Therefore in an optimal case he will turn to the actual, food-storing arm. In the Y-maze the animal always finds the
food pellet in a given location but its initial location is kept flexible. So this test is designed for spatial navigation and
long-term memory functions. In the novel object exploration test the animal explores distinct objects placed in a cage
for a certain time, and the time it spends with each object is measured in a session. Subsequently one object is displaced
by a new one and the time spent with the two objects is measured again. In a normal situation the animals prefer
exploring the new object.
These data underline the possible involvement of PV-cells in learning processes and their
involvement to alterations in network synchrony.
Figure 8.: Both the general
GluR-D KO and the PV-cell
specific GluR-A KO mice
display normal spatial reference
memory (A), but have deficits in
spatial working memory (B). In
the reference memory-test one
can see the gradual increase in
performance from 50 %
(chance-level) towards higher
values. Thus, with time even
these mutant animals can learn
environment. The test for
working memory shows that the
mutants perform significantly
worse than WT mice (B). The
figure is from Fuchs et al.,
2007, Neuron.
Brain oscillations are supposed to provide a temporal frame for information processing and
many cognitive functions, as suggested by a wide literature (Gray & Singer, 1989; Buzsáki et al.,
1994; Buzsáki, 2006). However, so far this assumption turned out to be difficult to prove
experimentally due to the lack of appropriate model systems.
In vitro electrophysiological and behavioural studies (Fuchs et al., 2007) carried out in our
lab showed that the mice in which GluR-A has been knocked out specifically in PV-positive
interneurons (later on referred to as PV-GluR-A KO mice) exhibit a complex phenotype, involving
perturbed network synchrony and deficits in hippocampus-specific behavioural and learning
paradigms (see the respective part of the Introduction). Based on these findings and the literature,
we wanted to investigate, whether certain aspects of synchronous network activities and various
forms of cognitive behaviour can be correlated with each other.
In my experiments the main goals and questions were the following:
I. What kind of changes if any do different hippocampal oscillations show in the PV-GluRA KO mice in vivo? Given the proposed roles of PV-positive cells in gamma- and ripple-rhythms,
we hypothesized that gamma- and ripple-oscillations show alterations in vivo. In other words, what
is the function of PV-positive interneurons (mainly PV-positive basket cells) in the generation of
hippocampal gamma- and ripple-oscillations?
II. What features can we extract from unitary analysis in the mutant animals? How do these
alterations on a cellular level (pyramidal cells and interneurons) translate into modifications on a
multicellular network level? To answer this, we analyzed unitary activity in wildtype (WT) and PVGluR-A KO mice and tried to correlate unitary results with oscillations.
III. Can we draw any correlation between network synchrony (in the form of oscillations
and unitary activity) and the behavioural deficits in these animals?
To address these questions we applied in vivo electrophysiological measurements in freely
moving and behaving mice using three types of electrodes: single tungsten wire-arrays, tetrodes and
silicon probes.
In this study we used fourteen mice, seven WT (mostly floxed GluR-A mice), and seven KO
(floxed GluR-A animals with Cre-expression under the PV-promoter). The PV-GluR-A KO mice
were generated by Elke Fuchs based on the floxed GluR-A mice (Zamanillo et al., 1999), which
were crossed with the PV-Cre mice (Fuchs et al., 2007). The floxed GluR-A, PV-Cre or complete
WT littermates served as negative controls in our experiments (Table 2).
Wildtype mice
PV-GluR-A KO mice
M60 (floxed GluR-A)
M210 (3 tetrodes)(WT)
M221 (floxed GluR-A)
M1096 (PV-Cre)
M486 (7 tetrodes)
M1097 (floxed GluR-A)
M671 (floxed GluR-A)
M701 (WT)
Table 2.: The animals used in this study, numbers mean identification numbers in the Central
Animal Facility of the Heidelberg University. In case of the pairs 209-210, 220-221, 1086-1097
(together with 1096), 671-672 and 700-701 WT controls were littermates of the KO mice, in case of
the remaining animals this was not so. Animals implanted with silicon probes are marked with bold
signals and gray shading.
Eight mice were implanted with single tungsten wire-array electrodes (tungsten wires of 45
µm, obtained from California Fine Wires Company), two with tetrodes (a WT with 3 and a KO with
7 tetrodes) and four with silicon probes (Table 2). The age of the animals on the day of the surgery
was between three and four months on average (101±33 days for WT and 94±27 days for KO,
means and standard deviations). The average weight of the mice was in the range of 25-30 gramms,
they were provided with food and water ad libitum.
The electrode drives were prepared manually from prefabricated elements. They consisted
of movable pieces (their number ranging from 3 to 7 for tetrodes or just 1 in case of wire-arrays or
silicon probes), which could be advanced or retracted with a screwdriver. The connection board and
the PIN-connector were fixed with a small and thin metal framework on the drives. The electrodes
were guided by silica-tubes with 75 µm internal and 150 µm of external diameter (from Polymicro
LLC, www.polymicro.com), they were fixed in the tubes with glue and protruded from them 3-4
millimeters. Tetrodes were prepared from polyimide-coated platinum-iridium wires 12 µm in
diameter, obtained from the Kantal Palm Coast company. Since the impedance of the tetrodes was
above 1 MΩ, we had to decrease the capacitance of their tip by using a gold-chloride (AuCl3)
solution (SIGMA, 200 mg/dl). After plating their impedance was around 250-400 kΩ. The
connection between the tetrode-channels and the connection-board was established with a
silverprint (Auromal 38, AMI DODUCO GmbH). The respective channels of the board and the
PIN-connector were soldered together with an aluminium-based soldering pen.
Figure 9.: Two special electrode-types used in this study. On the left side a tetrode-drive is shown.
One can see the seven microdrives that can be moved independently from each other and the very
thin tetrodes protruding from the silica-tubes. The small “legs” at the very right of the picture are
used for the fixation on the animal’s skull. On the right side a characteristic design of silicon
probes is shown (picture from Csicsvári et al., 2003a, Journal of Neurophysiology). A: global view
of a probe, B: the connector side, B1-B2: shanks magnified with the arrangement of recording sites,
C-D: connection schemes of preamplifier modules used with silicon probes.
Silicon probes have been purchased from a company (ACREO). In these experiments 8shank, 64-site probes were used, comprising 8 recording sites on each shank, their vertical spacing
being 50 µm. The distance of the shanks from each other was 60 µm, thereby creating an 8 times 8
rectangular matrix spanning more than 400 µm of the hippocampus in lateral direction, and
allowing for recording from both the stratum oriens, stratum pyramidale and stratum radiatum
simultaneously. To protect the drives and to provide a good electromagnetic “shielding” we applied
an aluminium foil-based coverage to the drives, which we hardened with epoxy-glue (R&G GmbH,
www.r-g.de). The shielding was also grounded. The recording sites of the probes were connected
with two flexible circuits comprising 32 channels to a small board, which ensured a proper contact
between the probe itself and the preamplifiers. We used 32-channel preamplifiers with an inputresistance of 10 MΩ to minimize the signal amplitude-decrease resulting from the use of highimpedance electrodes, such as silicon probes. These preamplifiers were ordered from the Brain
Technology Team (www.braintelemeter.atw.hu, Pécs, Hungary).
Animal handling, anaesthesia and surgery were carried out in accordance with the German
Laws for Animal Care and Animal Wellfare. For operation narcosis we used volatile isoflurane
anaesthetic. The mice were preanaesthetised in a chamber in a way that they were dizzy enough to
endure the fixation on the stereotaxic frame meanwhile they also inhalated the gas (1 liter of
atmospheric pressure air/minute supplied with required amount of isoflurane, around 4-5 % for
anaesthesia-induction and later on around 1 % for the maintenance of the narcosis), which provided
a smooth continuum for the deeper anaesthesia stages. The very fast equilibrium between brain
tissue, blood and inhalation mixture allowed for an excellent control over the depth of narcosis. The
depth of anaesthesia during the operation was controlled frequently by the hindpaw-reflex and
cornea-reflex. No incision took place until these reflexes disappeared. During the operation the eyes
were covered with a polycarbohydrate-based gel (Vidisin Optic from Dr. Mann Pharma) and the
thermal stability of the mice was ensured by a heating pad. The very top of the mouse head was
shaven, the skin above the skull after desinfection was incised and the skull surface was disclosed.
For the later fixation of the electrode head-set and for grounding and referenceing purposes stainless
steel screws were also implanted, two above the prefrontal cortical region and two above the two
cerebellar hemispheres respectively. We also implanted wire-electrodes into their neck-musculature
for electromyography (EMG) recording purposes. Subsequently a small hole (1-1.5 mm in
diameter) was drilled into the skull above the parietal cortical areas. The centre of the hole was
located 2 mm posterior and 1 mm lateral to the bregma (the implantation coordinates were based on
George Paxinos & Keith B. Franklin: The mouse brain in stereotaxic coordinates). The dura mater
was removed using a small pincette and electrodes were implanted in the neocortical layers or the
corpus callosum of that region. Silicon probes were implanted in a “coronal” plain to provide a
“coronal electrophysiological section” of the respected hippocampi. After the implantation the
electrode-parts lying free and the brain surface in between the borders of the hole were covered with
wax, and the “legs” of the drive were fixed on the skull and onto the screws with dental acrylic
After the recovery of the animals, the electrodes were slowly advanced to the hippocampal
pyramidal cell layer where recordings were obtained using an MCP Plus system (and AlphaMap
software, from Alpha Omega) in the frequency range from 1 to 10000 Hz. For recordings, animals
were placed in a circular arena of 48 cm in diameter with plastic walls of 50 cm height. The floor
and walls of the arena were cleaned with ethanol after every recording session. The animal’s
headset was connected with the preamplifier and the preamplifier was connected with a cabel to the
amplifier channels of the recording setup. Signals were digitized with an analog-digital conversion
board allowing for a sampling rate of 20 kHz. In addition to recordings in CA1 stratum pyramidale,
we also acquired recordings from the stratum oriens and stratum radiatum and in some cases from
CA3 and DG. After completion of the experiments the mice were deeply anaesthetized with
ketamin (Ketavet, Pharmacia GmbH) and an electrolytic lesion was induced via a selected electrode
located in the stratum pyramidale. For the lesion a square-wave pulse of 150 µA and 2 sec duration
was applied with a Digitimer Ltd. stimulator. Subsequently mice were perfused transcardially with
20 ml physiological saline solution followed by the same amount of 4 % PFA (para-formaldehyde).
Their brain was removed, sliced on a vibratome and stained with cresylviolet. In all cases the
successful positioning of the electrodes in CA1 was verified histologically.
Analysis of the data
Analysis of oscillations
Recordings were analyzed off-line with the Spike2 software and MatLab-scripts (see
Matlab-website at www.mathworks.com), and occasionally also with GraphPad Prism
(www.graphpad.com). For LFP- and oscillation-analysis preselected recordings were split based on
a 700 Hz low-pass and high-pass filter into EEG-signal and spike-related potentials (Spk-files)
(mprocess-algorithm, designed by József Csicsvári). The EEG-files had a sampling rate of 1250 Hz.
We selected recordings from stratum pyramidale where ripple amplitudes were maximal from a
given animal and where sharp waves preferentially showed a biphasic profile (according to the
pyramidal layer). The recordings (roughly 1.5-2 hours in length) were scored according to the
behavioural state of the animals with a 10 sec bin-size. The behavioural staging was performed
using the prefrontal cortical EEG-signal, the EMG-signal and in certain cases the hippocampal EEG
itself. Awake exploration was characterized by so-called “desynchronized” signal on the prefrontal
and theta-oscillations on the hippocampal channels combined with phasic muscle-activity. To fulfill
criteria for slow-wave sleep (SWS) the prefrontal and hippocampal EEG had to show large
amplitude slow-waves (so-called “synchronized” patterns) corresponding to delta waves and sleepspindles and the EMG-channels had to display a lack of considerable muscle-related activity. REMsleep criteria were desynchronized cortical activity and hippocampal theta-rhythm with muscle
atonia. Intermediate stages were allocated to segments, which did not fit to either of these groups
but they were not included in the analysis later.
Ripples (selected from SWS) and gamma-oscillatory events (selected from REM-sleep and
awake exploration) were first analyzed as events with a threshold-based peak-detection algorithm
(Ponomarenko et al., 2004). For ripple-analysis, recordings were filtered in the 130-250 Hz range
with a 15 Hz transition band, for gamma a 30-85 Hz range was used with a 5 Hz transition. The
filtered signals were also rectified and smoothed with 0.005 s windows for ripples and 0.015 s for
gamma-analysis. Detection thresholds were computed based on the smallest variance 3 s long
recording segment from the rectified and smoothed signal. The mean (M) and standard deviation
(SD) of this baseline segment served later for computing thresholds for ripple-detection (M+7SD)
and gamma-envelopes (M+2SD). As additional criteria, these signal-segments had to be at least 15
ms long in case of ripples and 25 ms long in case of gamma-envelopes. The final length of these
events was determined based on the points where they fall below M+0.5SD and M+1SD (ripples
and gamma respectively) on the rectified signal. The amplitude of the detected events was
calculated from the filtered (but not rectified) signals and was normalized later to the SD and
threshold values of the baseline: (amplitude-threshold)/SD. For frequency-analysis a waveletfunction (Morlet) was applied in the region of the ripple- and gamma-peaks on the filtered signal.
This analysis was followed by a power spectral analysis (multi-taper method, modified by Partha
Mitra). This algorithm used the peak-amplitude points of the ripples and computed the frequencycomposition in the time-segment located 64 ms around the amplitude-peak using a 2048-point Fast
Fourier Transform (FFT) function. The power spectrum was computed as a second power of the
Fourier Transform. We also analyzed ripples of different amplitude-ranges, most prominently
ripples bigger than the median of amplitude distributions but smaller than those belonging to the
upper 5 percentile. This way our estimates were more robust against threshold-differences and
outliers. Ripples whose prominent frequency was higher than 140 Hz in their power spectrum were
also compared between the animals and animal groups.
To gain insight into the theta- and gamma-bands, a similar multi-taper method was used,
with the exception that we selected REM-sleep and awake exploratory episodes from representative
recordings based on hypnograms and these segments were used in total for spectral decomposition.
The spectrum of SWS was analyzed in a similar way. We also analyzed amplitude- and frequencymodulation of gamma-rhythm by theta. First we computed the Hilbert-transform of the selected
signals in the theta-band to get the most precise approximation of the theta-phase at each sampled
point. This way the precise and corrected theta-phase could be allocated to the gamma-peaks. A
complete and Hilbert-transformed theta-wave was split into 24 equal phase-bins (15 degrees or
actually π/12 radians) and the mean gamma-wave amplitude (from filtered gamma) in each
particular bin was computed by averaging the gamma-waves peaking in that particular phase-bin. In
a similar way the frequency-modulation was computed using the interpeak-intervals of gammacycles covering the theta-wave and the values were arranged according to the timing of the gammawaves they stem from. In these cases gamma was treated as a continuous oscillation. These
measures, however, were also analyzed separately for gamma-cycles of detected gamma-events. For
ripple-waveform analysis, ripples extracted from the EEG were aligned to each other with their
positive peaks. For coherence-studies cross-spectral coherence and interpeak-intervals were
calculated. To achieve that, time-lags between peaks of successive oscillatory cycles were
computed on different positions (actually different channels) and their difference served as a base
for frequency comparison with IPI (the difference between the interpeak-intervals on the compared
channels). The cross-spectral coherence of different channel-combinations was computed from the
multi-taper Time Frequency Cross-Spectrum of the respective channels (originally developed by
Partha Mitra). The cross-spectrum is practically the FFT-based spectrum of the reference channel
multiplied with the spectrum of the respective channel. To make this measure insensitive to the
signal-amplitudes, the second power of the cross-spectrum was divided by the multiplied product of
the cross-spectra of the individual channels (the cross-spectrum of an individual channel is
NormCrossSpectralCoh12 = (CrossSpect12) /(CrossSpect11*CrossSpect22). To describe phasecoherence, phase-shifts between the peaks of oscillatory cycles on the compared channels (any
given channel relative to a reference channel) were computed separately for gamma- and rippleenvelopes.
We also applied current-source density (CSD) analysis to uncover the underlying currents of
ripple-oscillations. To do so, different channels (on the same shank) of silicon probe recordings
were used. Average waveforms were computed on the different channels triggered by the ripplepeaks on a given pyramidal-layer channel. From the average waveforms spanning 240 ms centered
at the ripple peak, the second spatial derivative was derived for each sampled point. For the
derivation we used a spatial difference of 2 channels (practically 100 microns). Finally, to make the
results more comprehensive, a linear interpolation was used and the results were plotted with a
colour-code in the two-dimensional time-depth matrix.
Unitary analysis
For spike-sorting the above-mentioned Spk-files were used for feature-extraction which was
based on Principal Component Analysis. These features (altogether 17) were used later for a 17dimensional hierarchical clustering (KlustaKwik, written by Ken Harris, available at
http://sourceforge.net/projects/klustakwik). The resultant clusters were verified and merged
manually by using the Klusters software (designed by Lynn Hasan).
For the identification of units we used distinct criteria for pyramidal cells (less than 3 Hz
average firing rate, a characteristic autocorrelation function with “bursty” sidepeaks and more than
0.35 ms spike-width) and interneurons (more than 7 Hz average firing rate, distinct autocorrelation,
lack of “burstiness”, spike-width shorter than 0.35 ms, figure 10.). The autocorrelation of a given
cell describes the relation of its spikes to each other in time, and thus is very similar in its meaning
to the interspike-interval histogram. The spike-width was computed between the points of 25 % of
the maximal spike-amplitude (Csicsvári et al., 1999b). The units characterized by firing between 3
and 7 Hz were classified as “intermediate type cells”, they might include very fast pyramids or
special, slower interneuron-types.
Units were considered “clean”, if they did not contain any spikes in their autocorrelogram in
the first 2 ms bins. For the analysis of interneurons, also some units containing a lower number of
spikes in the respective bins of their autocorrelogram were used as multiunits. In this case their
“contamination” was estimated by relating the firing rate in the refractory period (refractory rate) to
the “asymptotic firing rate” reflecting the average firing rate. A refractory rate of 100 would mean
that in the given multiunit every second action potential stems from a contaminating unit. However,
we included only much “cleaner” multiunits in the analysis. The isolation-quality was described
with the isolation distance or Mahalanobis-distance, reflecting the overlap between the clusterclouds (Harris et al., 2000). An isolation distance above 30 indicated a good isolation quality and
above 40 an excellent separation (Harris et al., 2000). To minimize the inclusion of cells which
were recorded more than once (and therefore could have biased the distributions), we applied an
electrode-advancement protocol and also a redundancy-screen that compared features of the units
from subsequent recordings. In this way one cell was usually represented by one recording session
in our database.
Recordings were also analyzed in distinct EEG-states based on an automatic theta- and
ripple-detection routine. The automatic ripple- and gamma-detection was performed as described
previously. For automatic theta-detection a 2048-point FFT-analysis was performed on the selected
Figure 10.: The Klusters software (Hazan et al., 2006) was used to analyze multiunit recordings.
The platform shows four windows illustrating cluster-clouds of three selected units, the waveforms
of these three cells on the four tetrode-channels, the cluster-similarity matrix and the interspikeinterval histograms (or so-called autocorrelations). The purple cell is a putative basket cell, the
other two are pyramidal cells. Cross-correlations (relation between the firing of distinct neurons)
are indicated in white.
EEG-file with a 2.5 s bin-window size and 1.25 s overlap between them. Segments were selected as
theta, if the ratio of the spectral power between 6-12 Hz (theta) and 2-5 Hz (delta) exceeded 6 in a
given bin. For oscillatory phase-computations the peaks and troughs of oscillatory waves (theta,
gamma and ripples) were determined and these time points were set as π (or 180 degrees which is
equal to - π or -180 degrees due to the circular nature of oscillations) and 0 respectively, and the
phase values between these points were interpolated. Since theta-waves show a physiological
asymmetry (their descending slope lasting longer than the ascending) and cannot be described
perfectly with harmonic oscillator models (Siapas et al., 2005), a correction of non-uniformity
distribution was also applied for their analysis. This way, unitary activity could be investigated
during distinct oscillatory types (theta, gamma and ripples). In addition to average firing rates,
faster spike-trains (more than 50 Hz, corresponding to spikes omitted in a time-window shorter than
20 ms) were described with the instantaneous or “bursting” frequency. The phase-locking of
isolated units was analyzed in distinct ways. The number of spikes of a given cell was computed in
20 bins covering a complete wave (from - π to π), and the spike-phase histograms were averaged
after normalization to the overall number of spikes emitted by a given unit. In a complementary
way the phase-preference of units was estimated with the Rayleigh test. We also estimated the
modulation-strength with the so-called “modulation depth”, the relative difference in the spike
count between the phase-bins with the maximal and minimal counts.
Statistical analysis
The examined parameters from WT and KO animal groups were compared using the
Wilcoxon rank sum test, and in case of normal distributions with the t-test. Regression slopes were
compared with covariance analysis. We used the statistical toolboxes of the Matlab and GraphPad
Prism packages. We applied the Watson-Wheeler test to compare circular distributions. If not
otherwise indicated, distributions are represented by their mean values and standard errors.
Hippocampal oscillations in PV-GluR-A KO mice
Earlier in vitro experiments on the PV-GluR-A KO mice were performed by Aleksandar
Zivkovic, Andrey Rozov and Marc Cunningham. Their most important finding was that AMPAcurrents on PV-cells were decreased. In addition, pharmacologically induced gamma-oscillations
were reduced in the KO mice. These effects were even more pronounced in a similar mutant, the
complete GluR-D KO (GluR-D is expressed almost specifically in PV-cells). In the GluR-D KO
mice, the precision of spike timing of CA3-interneurons was reduced in terms of gamma-phaselocking as well (Fuchs et al., 2007). Since PV-positive basket cells are supposed to play an
important role in the generation of fast oscillations and in the PV-GluR-A KO mice PV-positive
cells receive much less excitatory drive, our prediction was that both gamma-synchrony and ripples
would be perturbed in these mutants in vivo. To our surprise gamma-oscillations and ripples were
preserved in the hippocampus of the PV-GluR-A KO mice, although there were certain alterations.
To analyze these events we have to extract them from a composite “material”, namely the
hippocampal EEG. By looking at EEG-recordings from this brain-structure, one has the feeling of
traveling on a fantastic highland (like Tibet), involving big mountains (delta-waves and sharp
waves), sometimes these mountains have refined riffs on their top (ripples on the sharp waves),
sometimes there are deep canyons (radiatum sharp waves), and often there are flat plains situated
between the mountains. This pattern, occurring usually during SWS, is called large-amplitude
irregular activity (LIA), sometimes comprising slow oscillations as well (Wolansky et al., 2006).
Another time one can find a regular pattern of mountains with faster riffs located on them (theta
waves nested with gamma), this is what we see during active exploration and REM-sleep. To
navigate this landscape we have to subdivide it into smaller pieces based on the bigger and slower
components, which also reflect behavioural stages (hypnogram-scoring), and then look at its
components in different frequency-ranges.
After sorting according to behavioural stages we analyzed the power of gamma- and rippleoscillations (frequency-bands are 30-85 Hz for gamma and 130-250 Hz for ripples) using two
approaches. We applied a threshold-based event-detection algorithm which one can run on the
recording-signals already filtered in the appropriate frequency bands. This gives us information on
the amplitude, duration and leading-frequency of a given event. However, since the recorded signalamplitude also depends on the electrode-impedances, we could not use the voltage values directly,
but only after normalizing them to the detection threshold and standard deviation of the baseline
(see Materials and Methods). After computing the event- (ripple- and gamma-) arrays of individual
animals, we also pooled the data for WT and PV-GluR-A KO mice. It turned out that neither
gamma-oscillations, nor ripples show statistically significant alterations in the mutant mice in either
of the examined parameters (figure 11.).
Normalized amplitude
SD above threshold
Time (ms)
Length of events
Frequency (Hz)
Figure 11.: Ripples and REM-gamma as events based on a peak-detection algorithm. Columns on
the left refer to ripples (from SWS), those on the right to gamma-epochs from REM-sleep. The mean
of medians of the individual animals from the WT and KO group are plotted indicating standard
deviations as well.
Interestingly, when we grouped the animals according to the implanted electrode-types
(wire-electrodes, including tetrodes versus silicon probes), we found different results regarding the
amplitude of ripple-oscillations: ripples were of higher amplitude in wire-implanted KO mice and
smaller in probe-implanted mutants than in WT mice. Wire-electrodes have a low impedance while
tetrodes have intermediate resistance but uncomparably smaller than silicon probes. The high
impedance of the latter devices can account for a bigger signal-amplitude loss in our amplification
system (see later, figure 17.). In addition to this, silicon probes implanted in KO mice had a higher
impedance than those implanted in WT animals and this unfortunate circumstance can explain, why
we did not find bigger ripples in the KO mice implanted with silicon probes. We also compared the
ripple-oscillations in yet another way to obtain the power spectrum of the ripples. For this analysis
we detected the ripple-peaks in the recordings and computed the power spectrum in the EEGsegment 64 ms around the ripple-peak (figure 12.). We also selected those ripples whose peakfrequency was above 140 Hz and plotted their power spectrum separately.
Figure 12.: Power spectrum of ripples (A) and “real ripples” (B, peak-frequency above 140 Hz)
from 5 WT and 5 KO mice implanted with wires (blue indicates WT, red KO mice). The power
spectra are based on EEG-segments comprising 64 ms around the ripple-peaks. In panels C and D
the mean power spectra plus standard errors are shown for the wire-implanted mice.
The power spectral analysis takes into account that the power a given oscillation conveys is
proportional to the second power of the voltage (just as sound intensity is proportional to the second
power of the sound pressure). With this method we see an increased ripple-power in the individual
as well as in the pooled power spectra of PV-GluR-A KO mice compared to that of WT mice.
However, as before, the increased ripple-power holds for the wire-implanted mice whereas it is just
the opposite in case of silicon probe-implantations. An explanation for that can be that the rippledetection thresholds were very similar among wire-implanted mice but unfortunately were different
for probe-implanted WT and PV-GluR-A KO mice. Thus, the considerably lower detectionthreshold (which also reflects the mentioned impedance-differences between silicon probes) in KO
animals can account for the lower ripple-power among probe-implanted KO mice. As can also be
seen later on figure 17, the power-decrease is practically homogeneously affecting the complete
frequency-spectrum of the SWS- and REM-sleep of the mutants, which is also an indication that the
impedance of silicon probes implanted in the KO animals was unfortunately higher compared to
those used for WT mice.
Interestingly, ripples from the PV-GluR-A KO mice very often looked “strange”, meaning
that many of them showed huge variability in their cycle-to-cycle wave-amplitude and morphology.
To approach this problem more precisely, we computed average ripple-waveforms by aligning
ripples to each other by their peaks but surprisingly the average waveforms looked normal in the
KO mice (figure 13.). The mean ripple-frequency (as determined by Gaussian fittings of the ripplepower spectra) did not show a significant difference between the groups (138.23±2.25 Hz in WT
Voltage (mV)
Voltage (mV)
and 136.70±2.17 Hz in KO, means and standard errors, p=0.63, t-test).
Time (ms)
Time (ms)
Figure 13.: Average ripple-waveforms from 5 WT and 5 KO mice implanted with wire-electrodes
(A). Ripples from the raw signal were aligned with their positive peaks. A 64 ms time-window is
shown with mean waveforms and standard errors. B: average ripple-waveforms with the inclusion
of probe-implanted animals (altogether 7 WT and 7 KO mice). KO mice display bigger amplitudes.
Thus, this method could not reveal these slight disturbances, however, one might try to
subgroup ripples according to distinct features and to cluster them in distinct groups. Another
characteristic feature of ripples is the “intraripple frequency accommodation”, meaning that the
frequency of the ripple is maximal in its beginning and slightly decays towards the end of the event
due to the spike-frequency-accommodation of the participating cells. Since ripples change this
“deceleration-profile” upon application of various GABAergic agonists (Ponomarenko et al., 2004),
it is thought to be a sensitive measure of inhibitiory network state during ripples. We used a
wavelet-based method to determine the ripple-frequency every 5 ms after its beginning and
analyzed it between 15 and 50 ms, since in the very beginning of these ultrafast events there is a
frequency-peak that we cannot fit with linear models. Interestingly, we did not find any significant
alteration in the intraripple frequency accommodation in the PV-GluR-A mutant mice (figure 14.).
Intraripple frequency
Frequency (Hz)
Time (ms)
Figure 14.: Intraripple frequency
accommodation is a sensitive measure of
network mechanisms underlying ripples.
WT We did not find any significant change in
KO the deceleration profile in KO mice
compared to WT animals. Linear
regression analysis indicates that the
deceleration rate for WT: 0.087 Hz/ms
and for KO: 0.105 Hz/ms (p=0.37,
analysis of covariance). The mean of the
individual animals in the two groups is
plotted as a function of time. 95 %
confidence intervals are also indicated.
To analyze the overall spectral composition of the hippocampal EEG in distinct behavioural
states, we performed a Fourier Transform based power spectral analysis on SWS and REM-sleep
EEG-segments in WT and KO mice. As figure 15. shows, the power-increase in the ripple-band in
KO animals is also quite conspicuous in this analysis. There is also a slight increase in the gammaband but to a lesser degree.
Figure 15.: Power spectrum of
SWS-periods from WT (blue)
and KO (red) animals. In this
analysis all 7 WT and 7 KO
mice (implanted with wires and
silicon probes) were included.
The respective EEG-segments
selected based on
hypnograms. Even though we
find a slight increase in thetaand gamma-power as well, the
ripple-power increase is much
more pronounced. Plotted were
the logarithms of the mean
power values with standard
errors on a decibel scale.
The power-increase in the ripple-band in the PV-GluR-A KO mice, however, may not only
be an indication of increased ripple-size but can also result from an increase in the occurrencefrequency or duration of ripples (this tendency, even if not significant is present; ripple-length in
WT: 104.690±3.455 ms, in KO: 116.800±4.615 ms, means and standard errors of the median values
from 7 WT and 7 KO animals, p=0.16, t-test). We also looked at the ripple-occurrence frequency
and found that in KO mice they are generated a bit more frequently, however, this effect was not
significant (1.43±0.128 Hz in WT and 1.77±0.159 Hz in KO, p=0.12, t-test). Thus, it seems that we
have a complex phenotype in which ripple-generation is affected at multiple levels.
Figure 16.: Theta- and gamma-power in REM-sleep episodes (A) and during awake exploration
(B), WT is indicated in blue, KO in red. There is a modest power-increase in the KO mice in both
the theta- and gamma-band, which is, however, much smaller than that in the ripple-band. Power
spectra of 5 WT and 5 KO animals were averaged for REM-sleep and for awake exploration
(animals with silicon probes were omitted due to impedance-differences and the high noise-levels
that silicon probes show during awake exploration). Logarithms of the mean powers with standard
errors are plotted using a decibel scale.
The gamma-power was slightly increased in REM-sleep and during exploration, which was
paralleled by a modest increase in the theta-power in these states. However, these alterations were
much lighter than those of the ripples and were practically due to one outlier animal in the KO
group. One can also see on the power spectra that the maximal amplitude-peak of REM-gamma is
shifted to lower values in the KO mice (figure 16.), a result which is corroborated by the analysis of
theta-gamma comodulation (figure 18.). Silicon probe-implanted mice were treated separately
(figure 17.B) due to the electrode-impedance problems.
We also looked at the relation between theta- and gamma-oscillations. It is known that thetaoscillations modulate certain features of gamma-oscillations (Bragin et al., 1995). Theta-oscillations
are in phase in the stratum oriens and pyramidale but in the stratum radiatum their phase changes
and gradually turns to a phase-reversal. To minimize the variability resulting from different
locations, the modulations were always computed from pyramidal layer recordings. There are
distinct features of oscillations that can modulate other oscillations. The phase of theta modulates
both the amplitude and frequency of gamma-oscillations. The maximal gamma-frequency we
normally find around the peak of theta-waves whereas on the slopes of the theta-waves the gammarhythm is somewhat slower. The maximal gamma-amplitude we find on the peak or on the
descending slope, slightly after the peak of the theta-waves. Sometimes these gamma-waves can be
as huge in amplitude as the underlying theta, thus we can call them “gamma-spikes” (Buzsáki et al.,
2003). There is also a positive correlation between the theta-amplitude and gamma-power and
frequency. Interestingly, there is a weak negative correlation between the amplitude and frequency
of theta, meaning the bigger a theta-wave the slower it is (data not shown). In case of ripples, this
relation is usually positive (Csicsvári et al., 1999a). As most of the features of the two oscillations
are somehow modulated by each other, it is very difficult to say in the end, which feature modulates
exactly what, so the term “co-modulation” may be more correct to describe this phenomenon. We
also looked at how the gamma is modulated by theta in the PV-GluR-A KO mice. To our big
surprise we find that gamma is nicely modulated by the phase of theta both in amplitude and
frequency in the PV-GluR-A KO animals (figure 18.). This effect can be seen both during REMsleep and awake exploration. We find a slower gamma-rhythm in the REM-sleep of the mutant
mice, this difference is 2.13 Hz if we account for the complete theta-wave and 2.87 Hz when we
look at the region around its peak. These differences are significant (p<0.0001, paired t-test
comparing respective phase-bins of the theta-cycle). Theta-power seems to be very slightly
increased in the PV-GluR-A KO mice both in REM-sleep and explorative behaviour (again due to
an outlier animal, figure 16.), but frequency-composition in the theta-range seems to be unaltered.
Figure 17.: Power spectra of SWS (A) and REM-sleep (B) from silicon probe recordings. The blue
colour indicates WT and the red KO mice. One can see an overall decrease in the powers of KO
mice, which is, unfortunately, due to the impedance-difference of these high-resistance electrode
types, which affected more seriously the KO group. Logarithms of the mean powers with standard
errors are plotted on a decibel scale.
Voltage (mV)
Voltage (mV)
Theta-phase (rad)
Frequency (Hz)
Frequency (Hz)
Theta-phase (rad)
Theta-phase (rad)
Density of gamma-peaks
Density of gamma-peaks
Theta-phase (rad)
Theta-phase (rad)
Frequency inside
envelopes (Hz)
Frequency inside
envelopes (Hz)
Theta-phase (rad)
Theta-phase (rad)
Theta-phase (rad)
Figure 18.: Co-modulation of theta- and gamma-rhythms is preserved in the PV-GluR-A KO mice
both during REM-sleep and awake exploration. This holds true also for the amplitude of gammapeaks (first row) and the frequency of the gamma-rhythm (second row) all over the selected thetaranges and also for the number of gamma-cycles and gamma-frequency in detected gammaenvelopes (third and fourth rows). One can see that there is a slight decrease in REM-gammafrequency in mutants, which is, however, not seen during explorative behaviour. Nevertheless, this
can also be the consequence of the overall slightly slower gamma-rhythm in exploration where the
capacity for gamma-frequency increase is not exploited maximally.
Hippocampal oscillations measured in defined layers
EEG-oscillations are brought about by the spatiotemporal summation of extracellular
currents propagating in three dimensions. In the hippocampus these spatial dimensions are
represented by different histological layers and the precise topological arrangement of the
histological constituents underlies the remarkably different and very characteristic EEG-profiles.
Basic oscillatory features as described in the previous section were analyzed in the pyramidal cell
layer of CA1. However, most of the investigated patterns are also visible in other layers. Thus,
gamma-oscillations can also be recorded in the stratum oriens and stratum radiatum. This applies to
ripples as well, with the difference that ripples vane in the stratum radiatum whereas gammaoscillations become stronger there. However, this is due to the fact that ripple-generation is unique
for the stratum pyramidale whereas gamma-oscillations are also generated in the DG. Thus, the
gamma-oscillations we record in the deep stratum radiatum and in the stratum lacunosummoleculare are actually mixtures of gamma-oscillations passively volume-conducted from the hilus
and from the pyramidal cell layer. Gamma-oscillations show phase-reversal in the stratum radiatum,
suggesting that dendritic excitation from the Schaffer-collaterals is involved in their generation.
This idea was also proven in vitro with the use of voltage-sensitive dyes and current-source density
analysis (Mann et al., 2005). Theta-oscillations reverse their phase below the stratum pyramidale,
therefore also the phase-relation between theta and gamma changes there, the maximal-amplitude
gamma-waves sliding to the theta-troughs. This feature and the constantly increasing gammaamplitude towards the DG is responsible for the fact that in the stratum lacunosum-moleculare
gamma looks even more prominent than the theta-rhythm. Coming to SWS, ripples look the most
robust in the pyramidal cell layer, however, their underlying driving force, the sharp wave is not
always visible there but is more intense at the border of the stratum oriens and stratum pyramidale
(in the form of a positive deflection) or is dominant in stratum radiatum in the form of a big
negative deflection (positive and negative refer to the appearance but following convention the
radiatum sharp wave is called “positive”). The bigger sharp waves usually represent a bigger drive
of the Schaffer-collaterals, therefore the ripples associated with them also tend to be of higher
amplitude. Silicon probes are extremely suitable for examining oscillations in many histological
laminations simultaneously (Csicsvári et al., 2003a). In figure 19. two short segments from a proberecording are visible, the first one exemplifying SWS with ripples and the second a fragment of
REM-sleep. These recordings make it possible that by a computational procedure one can determine
which layers take part in the generation of different rhythms, whether they participate actively or
passively and whether they constitute a current-source or current-sink.
To gain insight into the histological profile of gamma- and ripple-oscillations we analyzed
silicon probe recordings. The depth-profiles look very similar in the WT and PV-GluR-A KO mice
for gamma- and ripple-rhythms and also sharp waves.
We also performed current-source density (CSD) analysis to determine the generation site of
ripple-oscillations (figure 20.).
Figure 19.: Silicon probe
recordings from a WT
mouse. The 8 channels
shown are spaced 50 µm
apart from each other.
The upper trace is from
the stratum oriens, the
following four represent
stratum pyramidale and
the lower traces were
A: A segment from SWS
with ripples and sharp
waves which are very
prominent in the stratum
B: Theta-rhythm nested
with gamma-oscillations
from REM-sleep. One
can see the gradual
phase-shift of theta
beneath the pyramidal
cell layer and the phasereversal of gammawaves in the stratum
Figure 20.: CSD-analysis of
ripples from a WT (A) and a PVGluR-A KO (B) mouse. Ripples
were detected on a silicon probechannel located in the pyramidal
cell layer. Ripple-peaks were used
as a trigger for the computation
of average waveforms on distinct
channels and the waveforms
served for computing the CSDmaps. The plots show a timesegment of 120 ms around the
ripple-peak, 0 depth indicates the
pyramidal layer, positive values
denote a direction towards
stratum oriens, negative values
mark a direction towards stratum
radiatum. Around the ripple-peak
one can see a sink in the stratum
radiatum, resulting from the
Schaffer-collateral input during
sharp waves. It is combined with
a current-source in the pyramidal
layer which most probably results
from perisomatic inhibition. We
also find a current-source in the
stratum radiatum and a sink in
the pyramidal layer following
sharp waves. The CSD-analysis
indicates that there is no
profound difference in the
generation of sharp wave-ripples
between WT and PV-GluR-A KO
The CSD-analysis provides evidence that the generation of ripples is confined to the
pyramidal cell layer in the PV-GluR-A KO mice, and it is associated with dendritic depolarization
in the stratum radiatum, indicating that the anatomical substrates of ripple-oscillations did not
change in the mutant hippocampus.
Due to the precise arrangement of recording sites on silicon probes these recordings are
suitable for coherence-analysis. Oscillations may look different in distinct recording sites of CA1
because projections may reach the targets with short time-delays that can cause measurable phaseoffsets during gamma- and ripple-oscillations. This effect can be described by the phase-shift of
oscillatory peaks. However, spectral parameters at a given recording site can also reflect
inhomogeneities in the recorded cell-population. Thus, since fast excitatory transmission is reduced
in PV-cells, we may speculate that individual interneurons would function rather independently
from each other and therefore the inhibition they inflict on pyramidal cells would be
inhomogeneous. This could lead to a jitter in the timing of postsynaptic and action potentials of
pyramidal cells, which would be visible on the spectral difference on distinct locations. To describe
these phenomena we computed the cross-spectral coherence and interpeak-interval (difference in
the interpeak-interval between oscillatory cycles of given site-pairs, IPI). It is of immense value that
these measures are independent of signal-amplitudes and thus provide highly reliable results (see
Materials and Methods).
coherence measures from WT
(blue) and PV-GluR-A KO
(red) mice implanted with
silicon probes. On the
electrode-shanks at the left
and right side of the probe a
given channel (located in the
stratum pyramidale) was
selected as a reference site.
Coherence measures for
distinct shanks with respect to
the reference channel were
calculated as an average of
channels on the given shanks.
The plotted results are
averages of two animals (2
WT and 2 KO). One can see
that neither the spectral
parameters (A: cross-spectral
coherence and B: IPI) nor the
phase-coherence (C) differs
significantly between the
animal groups. Due to the
huge standard deviations only
the mean values are plotted.
We recorded four mice with silicon probes, two KO and two WT animals, the KO and WT
controls pairwise coming from the same litter. We found that in one animal pair the examined
parameters did not differ significantly, however, in the other pair we found a slight decrease in
cross-spectral and phase-coherence both with respect to gamma- and ripple-oscillations. Overall,
when pooling the results we do not find these alterations significant, implying that synchrony in the
gamma- and ripple-frequency bands is not significantly perturbed in the spatial dimension upon
reducing excitatory input in PV-cells (figure 21.).
Unitary analysis in the PV-GluR-A KO animals
To understand, how unitary activity underlies oscillatory phenomena, we isolated pyramidal
cells and interneurons from tetrode- and silicon probe-recordings. Tetrodes are the most powerful
tools in recording multiple single units from freely moving and behaving animals* whereas silicon
probes, depending on the spacing of the recording sites can also be useful but not to that extent (at
least with intersite-spacings more than 50 µm they are less powerful).
*To understand the tetrode-principle, one should imagine an experimentator walking in a dark forest on a
sunny afternoon. Several birds, belonging to distinct species are singing in the trees but due to the leaves and low lightpenetration the scientist would not see them. However, he hears their songs coming from different directions. The birdsongs reach the two ears of the investigator with some time-delay, and also with a power-difference due to the different
distances the sound waves travel to reach the ears and due to the shadowing effect of the head on the ear which is
located more distant to the bird. Therefore he can estimate the direction and the distance of a given bird based on these
principles. If he is an ornithologist, he will also be able to distinguish between birds based on the peculiarities of their
song (like pitch-frequency, intonation etc.), even if they come from similar directions. If there are two people walking
there, the spatial arrangement of their four ears will increase the spatial resolution and species-recognition to an even
higher degree. Due to the different action potential waveforms that pyramidal cells and interneurons emit and due to the
amplitude-distributions on the four tetrode-channels they can also be sorted quite efficiently.
Pyramidal cells and interneurons can be easily distinguished in our recordings by distinct
features, such as spike width, firing rate and autocorrelations (see Materials and Methods). The
main advantage of our recordings compared to those from anaesthetized preparations relies in the
more physiological circumstances a given cell is examined in since animals are not affected by the
narcotics. Besides, one can record the same cell for several days, and also microcircuits can be
examined given the relatively big number of cells isolated from a stereotrode. However, since we
cannot fill the cells, the histological verification is more problematic. We cannot clearly distinguish
between many of the interneuron-subtypes. Nevertheless, in many cases the location of the cell
and/or firing patterns are predictive (figure 22., Csicsvári et al., 1999b). We can also correlate our
data with those obtained from anaesthetized animals since in those experiments many interneurons
show distinct features regarding oscillations.
At a microcircuit level we can also relate the activity of cells relative to each other. The
cross-correlation of cells can be indicative of whether there is excitatory or inhibitory transmission
between the examined cells. In figure 10. (Materials and Methods) one can see two pyramidal units
and an interneuron recorded from CA3. The cross-correlations indicate that there is a relatively high
Figure 22.: Interneurons from specific locations of our recordings. A: a putative CA1-basket cell,
with characteristic autocorrelation. B: a putative O-LM interneuron (however, it can also be a
basket cell) suggested by its location (CA1 stratum oriens). C: a putative basket cell from the DG.
D: an “intermediate cell”, the autocorrelation resembles that of interneurons but the firing rate is
only ~3 Hz. A, B and C were recorded from a KO animal with tetrodes, D is from a WT mouse
implanted with a silicon probe. For visual inspection the first 30 ms range of the autocorrelations is
probability that both pyramidal cells innervate the interneuron (shown by the positive peaks close to
the zero ms bin) whereas the interneuron probably inhibits one of the pyramidal units (shown by the
negative deflection next to the positive peak). In vitro experiments on neocortical slices showed that
special interneurons can also excite pyramidal cells (Szabadics et al., 2006), therefore it would be
interesting to show this phenomenon in hippocampal in vivo recordings as well, based on the
analysis of cross-correlation functions. A monosynaptic excitatory coupling from an interneuron to
a principal neuron would be an in vivo proof for that. Cross-correlations of cell-combinations of
different tetrodes can also reveal synaptic connections between distant cells, an advantage, which
due to the thin slice-preparations, cannot be achieved in vitro.
From the huge number of cells recorded from the PV-GluR-A KO mice and their WT
littermates, we selected 42 pyramidal cells and 24 interneurons from the CA1 of WT mice, and 134
pyramidal units and 99 interneurons from the CA1 of PV-GluR-A KO mice. The selected cells had
an isolation distance of at least 30 (see Materials and Methods). Besides, we had recordings from
24 CA3-pyramidal cells and 11 CA3-interneurons from a KO animal, and we also recorded 1 DGinterneuron that satisfied the named criteria in both a WT and in a KO animal. These recordings
were mainly from two mice implanted with several tetrodes but silicon probes also contributed
substantially. From this pool we selected 34 pyramidal cells from the WT and 114 from the KO
group, and 17 WT and 62 KO interneurons based on more stringent criteria. These cells were
chosen based on a redundancy-screen that excluded those neurons that could be recorded more than
once and had a higher probability of being the same. All the selected units were recorded in CA1.
Isolation distances for the
selected units
Figure 23.: The isolation distances of
the selected pyramidal cells (Pyr,
single units) and interneurons (Int,
mainly multiunits). The isolation
WT distances were not significantly
KO different between pyramidal cells of
WT (50.139±4.697, means and
standard errors from 34 cells) and
KO mice (39.461±0.815, 114 cells)
and between interneurons of WT
(120.990±20.784, 17 cells) and KO
animals (91.423±8.215, 62 cells;
p=0.156, Wilcoxon rank sum test).
However, interneurons generally
have a higher isolation distance than
pyramidal cells.
We used only single units from pyramidal cells but also multiunits from interneurons. The
contamination percentage of a given interneuron was approximated using its autocorrelation
function. The area in the first 2 ms of this function (refractory period) was compared to the
asymptotic rate of the neuron. In this way we got a rough estimation of the percentage of spikes in a
given cluster that originated from a different, “contaminating” unit (figure 24.). The isolation
distance did not differ significantly between WT and KO interneurons (figure 23.), nor did it for
pyramidal cells. We did not find a significant difference between the contamination percentage of
WT and KO interneurons either (figure 24.).
Contamination degree of
Refractory rate
Figure 24.: The contamination degree of WT
and KO interneurons, expressed as a percentage
of the refractory and asymptotic firing rate. There
is no significant difference between the groups,
(19.053±3.303 for WT and 21.105±1.789 for KO;
p=0.59, t-test). Mean values with standard errors
are plotted.
I mentioned that in extracellular recordings units are usually indicated by negative
deflections. However, sometimes we find small positive potentials at the beginning of spikes. These
could represent hyperpolarization just before spikes are generated and the recorded spikes may be
“rebound spikes”. By measuring the time between the positive and negative peaks of the spikesignals we can have a rough estimate of the duration of action potentials. In our practice, however,
we measured the spike-width between the points with 25 % amplitude of the maximal negative
peak. As mentioned above, interneurons express specific sets of ion-channels, which contribute to
the short spike-width whereas pyramidal cells have a slower action potential and usually have
longer-lasting after-hyperpolarizations. Neither pyramidal cells nor interneurons showed significant
difference in their spike-width when we compared WT with PV-GluR-A KO mice (figure 25.,
p=0.11 and 0.68 for pyramidal cells and interneurons, Wilcoxon rank sum tests). However, it is also
not surprising since AMPA-currents usually do not contribute much to the currents of action
potentials but rather to excitatory postsynaptic currents, and are activated at more negative
membrane potentials whereas sodium channels open only at more positive values.
Spike-width of the recorded cells
Time (ms)
Figure 25.: The spike-width of cells.
Pyramidal cells (Pyr) have a longer
action potential and consequently a
bigger spike-width (0.436±0.015 ms for
WT pyramidal cells of WT and 0.430±0.009
KO ms for those of KO) than interneurons
(Int, WT: 0.256±0.009 ms and KO:
0.251±0.005 ms) where the expression
of certain K+-channels ensures a faster
action potential (see Introduction).
However, the spike-width of the same
cell-type did not differ between
genotypes (p=0.11 for pyramidal cells,
and 0.68 for interneurons, Wilcoxon
rank sum test).
Unitary firing rates in distinct behavioural states
First we determined the average firing rates of CA1 pyramidal cells and interneurons in WT
and PV-GluR-A KO mice respectively. Interestingly, there was no significant difference either for
pyramidal neurons (0.370±0.044 Hz for 34 WT units and 0.491±0.045 Hz for 114 KO units,
p=0.72, Wilcoxon rank sum test) or local circuit neurons (15.904±1.367 Hz for 17 WT and
15.570±0.908 Hz for 62 KO interneurons, p=0.40, Wilcoxon rank sum test). There was only a very
slight tendency towards an increased firing rate in pyramidal cells but it did not reach statistical
significance (figure 26.). An explanation for this phenomenon could rely in the diversity of
interneurons we recorded. Since we cannot really discriminate between PV-cells and other
interneuron-subtypes that in fact comprise the majority of the hippocampal interneuron-family, our
results do not exclude the presence of silent or slowly-firing PV-cells in the CA1 area of the
Average firing rates
Average firing rate
pyramidal cells
Firing rate (Hz)
Firing rate (Hz)
Figure 26.: Average firing rates of pyramidal cells and interneurons in WT and PV-GluR-A KO
animals. Mean values and standard errors of the distributions are plotted.
The characteristic firing mode differs between the two cell types. Pyramidal cells tend to fire
in bursts, meaning that within bursts very short time-periods elapse between successive action
potentials but after emitting a burst the cell can stay silent for a longer time-interval, even seconds.
This effect overall leads to a lower average firing rate of principal cell-activity. Interneurons
discharge with a much higher frequency, however, usually less likely in bursts, so their activity is
less clustered in time. To gain insight into these kinetical parameters we also looked at the bursting
firing frequencies of the well-isolated units (see Materials and Methods and figure 27.).
Pyramidal cells in KO mice displayed an overall higher bursting frequency (157.630±7.401
Hz and 198.280±3.693 Hz for 34 cells from WT and 114 pyramidal cells from KO animals,
respectively, p<0.0001, Wilcoxon rank sum test). Interneurons, however, did not differ in this
respect (104.400±7.041 Hz for 17 WT and 94.416±3.793 Hz for 62 KO interneurons, p=0.21,
Wilcoxon rank sum test). This observation implies that even though PV-cells have a diminished
excitatory drive, interneurons overall can be efficiently recruited during faster discharges. Thus,
additional mechanisms may be involved in their “acceleration”.
Pyramidal cells
Ratio of spikes in bursts
Bursting frequency (Hz)
Bursting frequency (Hz)
Burst-spike index
Figure 27.: The instantaneous (or bursting)
frequency of pyramidal cells (A) and
interneurons (B). Pyramidal cells in KO mice
can fire bursts at higher frequency. Pyramidal
cells in the PV-GluR-A KO mice are more
prone to fire in bursts (C). Means and standard
errors are plotted.
We were also interested in the propensity of pyramidal cells to fire in bursts. Therefore we
determined the “burst-spike index” for pyramidal cells (figure 27.C), by counting the spikes that
were emitted during bursts and relating it to the overall number of action potentials that a given cell
emitted. Whereas in WT mice this ratio is 0.434±0.024, in PV-GluR-A KO mice it is 0.601±0.016,
indicating that on average 43 % of the spikes of WT pyramidal cells is clustered in bursts whereas
in PV-GluR-A KO mice roughly 60 % of the spikes belongs to burst firing. This difference is also
highly significant (p<0.0001, Wilcoxon rank sum test).
The mechanisms underlying cellular behaviour are most likely dissimilar during different
behavioural and network states, therefore we analyzed unitary activity during different oscillations:
theta-, gamma- and ripple-activity respectively. The most interesting finding was that during thetaoscillations pyramidal cells fired with a significantly lower average rate in PV-GluR-A KO mice
than in WT mice (2.631±0.449 Hz for 32 WT and 1.981±0.189 Hz for 113 KO pyramidal cells,
p=0.012, Wilcoxon rank sum test, figure 28.). However, even though there is a slight tendency
towards a decreased firing rate of interneurons in KO animals during theta-rhythm (29.801±2.379
Hz for WT and 24.845±1.636 Hz for KO, 17 and 62 cells respectively, p=0.09, Wilcoxon rank sum
test), this effect is not statistically significant. We do not see any difference in pyramidal cell or
local circuit neuron firing during ripples ( fig. 29.).
Firing rate (Hz)
Firing rate (Hz)
Pyramidal cells
Figure 28.: Unitary firing rates during theta-oscillations. Pyramidal cells of the KO group fire
significantly less in these states.
Pyramidal cells
Firing rate (Hz)
Firing rate (Hz)
Figure 29.: Firing rates during ripple-oscillations. Neither pyramidal cells (2.321±0.473 Hz and
2.409±0.391 Hz for 31 WT and 111 KO pyramidal cells, p=0.140, Wilcoxon rank sum test), nor
interneurons (46.853±5.596 Hz and 45.228±3.607 Hz for 17 WT and 59 KO interneurons
respectively, p=0.825, t-test) show any change during ripple-oscillations.
The increased “burstiness” of pyramidal cells and their decreased average firing rate during
theta-oscillations in KO mice tempted us to analyze bursting characteristics in distinct oscillatory
forms. Interestingly, when we looked at instantaneous (or bursting) frequency of pyramidal cells in
theta-related states, we found a significant increase (87.831±8.614 Hz for 30 WT and 139.020
±5.407 Hz for 106 KO pyramidal neurons, p<0.0001, t-test, figure 30.A). Thus, even though
pyramidal cells in the PV-GluR-A KO mice fire on average less during theta-oscillations, when
bursts occur, they can accelerate to a higher rate than pyramidal cells in WT mice. It is of note that
even though the gene-deletion is PV-cell specific, we find the main effect on pyramidal cells. This
observation suggests that network mechanisms are brought about by an interplay between
pyramidal cells and interneurons in the mouse hippocampus.
Bursting frequencies in different oscillations
Pyramidal cells
Bursting frequency (Hz)
Bursting frequency (Hz)
Bursting frequency (Hz)
Bursting frequency (Hz)
Bursting frequency (Hz)
Bursting frequency (Hz)
Figure 30.: Instantaneous, reflecting bursting frequencies of pyramidal cells and interneurons in
theta-related behavioural states (A), during gamma- (B) and ripple- (C) oscillations. The
pyramidal cells of the KO group display a marked increase in this parameter.
The bursting frequency of pyramidal cells was also higher for gamma- and ripple-events
(Gamma: 99.547±6.291 Hz for 34 WT and 148.940±4.169 Hz for 114 KO cells, p<0.0001; ripples:
105.840±6.541 Hz for 30 WT and 130.930±4.334 Hz for 108 KO pyramids, p<0.01, Wilcoxon rank
sum tests) even though this effect was not so pronounced as in case of the theta-rhythm.
Interneurons did not show any alteration in KO compared to WT mice (Theta: 68.348±5.109 Hz for
17 WT and 70.320±2.888 Hz for 62 KO interneurons, p=0.98, Wilcoxon rank sum test; gamma:
75.862±4.185 Hz for 17 WT and 79.292±1.956 Hz for 62 KO interneurons, p=0.43, t-test; ripples:
92.960±6.118 Hz for 17 WT and 95.220±4.162 Hz for 59 KO interneurons, p=0.78, t-test; figure
30. A, B and C.).
Ripples are highly synchronous events when 10-15 % of pyramidal cells discharge together
in a 100 ms time-window. However, a given pyramidal cell does not discharge in every ripple:
depending on the cell, the percentage of ripples a principal cell contributes to can be from 0 to up to
50 % or even more (Ylinen et al., 1995). This suggests that there are pyramidal cells that have a
more stable contribution whereas others fire rarely. We also approached this problem by computing
the firing rate of cells in all ripples and selectively in the ripples in which they were active. In this
computation we became the former measure by accounting for the overall length of all the ripples
(even if the cell was not active in most of them) present in the recording and the latter value was
received by accounting for the overall length of only those ripples where the given cell was active.
Very interestingly for pyramidal units the latter values are roughly 8-10 times as big as the first
(17.858±0.577 Hz versus 2.321±0.473 Hz for WT pyramidal cells and 20.170±0.821 Hz versus
2.409±0.391 Hz for KO pyramids, means and standard errors), indicating that on average a
pyramidal cell is involved in the generation of 10 % of the ripples (figure 31.).
Figure 31.: Distribution of pyramidal
cells regarding their involvement in
ripple-generation or ripple-participation.
The firing rate of each pyramidal cell
was computed all over the detected
ripples and only in those ripples in which
they were active. The ratio of these two
values served as an approximation to
evaluate, how extensively a given cell
contributes to ripple-oscillations with
spike-emission. WT is blue, KO is red.
However, the situation is different for interneurons: in their case these numbers are not
really different (52.005±4.832 Hz versus 46.853±5.596 Hz for WT and 50.072±3.205 Hz versus
45.228±3.607 Hz for KO interneurons), indicating that interneurons have a more general
contribution to ripples whereas pyramidal cells may contribute in a more specific way to their
generation. Overall, as suggested by previous data (Csicsvári et al., 2000) and also revealed by our
study, distinct ripples may be brought about by different cell-assemblies which might also have an
internal coordination, and interneurons can participate more generally by controlling the whole cell75
population. By analyzing the actual firing rate of interneurons during ripples, we find that in most
cases both in wild types and in mutants they increase their firing rate. However, this observation
holds true only in a general sense.
The literature suggests that interneurons can be grouped into several classes according to
their behaviour during ripple- and theta-oscillations. Most interneurons located in the stratum
pyramidale increase their firing rate all over the ripple-event. Some cells, however, have a double
peak on the cross-correlations between the ripple-peaks and their spikes. Thus, they are active in the
very beginning and at the end of the ripple-episode, with background activity levels in the middle of
the oscillatory event, most probably for they also receive a strong inhibition there. “Anti-sharp wave
cells”, located mainly in the stratum oriens decrease their firing during ripples (Csicsvári et al.,
1999b). Figure 32. shows an example of a “sharp wave-ON cell”. PV-positive basket cells indeed
have been shown to increase their firing during ripples (Klausberger et al., 2003) whereas CCKcells usually do not increase it (Klausberger et al., 2005), and axo-axonic cells are only active in the
beginning of these ultrafast oscillatory epochs and remain silent afterwards (Klausberger et al.,
2003). O-LM cells are silent during ripples in anaesthetized rats (Klausberger et al., 2003), therefore
they may correspond to the real “anti-sharp wave cells”. Interestingly, even though in anaesthetized
rats O-LM cells tend to fire in the trough of theta-waves, in freely moving animals most
interneurons in the stratum oriens were also bound to the descending slope of theta-waves, just as
most interneurons from the pyramidal cell layer (Csicsvári et al., 1999b). Axo-axonic (chandelier)
cells fire preferentially on the peak of theta-waves in anaesthetized rats (Klausberger et al., 2003).
Figure 32.: A “sharp wave-ON cell”
from a KO animal. The firing probability
of the interneuron was computed as a
function of time-difference from the
ripple-peak (cross-correlation). I plotted
a 250 ms region around the ripple-peaks
with a 10 ms bin-size. On the upper trace
an average ripple-waveform is shown,
indicating that the recording position
was at the border between stratum oriens
and pyramidale. This location also
suggests that most probably the
interneuron was a basket cell.
On the lower trace one can see that
notwithstanding the decreased excitatory
drive on PV-cells this cell was recruited
to ripple-events very efficiently.
Thus, we have some hints, which interneurons belong to the “sharp wave-ON” and “antisharp wave” categories, even though it is difficult to verify this in vivo. Altogether these results also
indicate that notwithstanding the decreased excitatory drive on PV-cells most of them can still be
efficiently recruited by ripple-oscillations.
Rhythmic modulation of unitary activity during distinct oscillations
LFP-oscillations represent the spatiotemporal summations of EPSPs and IPSPs at a
population-level. Since action potential firing is related to the course of EPSP, there is a relation
between the phase of oscillations and unitary activity. We call this phenomenon phase-locking.
Based on previous studies (Csicsvári et al., 1999b, Buzsáki et al., 2003) it was suggested that during
theta-rhythm interneurons fire preferentially on the descending slope of theta-waves while
pyramidal cells have a double peak, just shortly after the theta-trough and around the theta-peak.
During ripples pyramidal units are the most active near the ripple-troughs while interneurons prefer
the ascending slopes which is not surprising considering the monosynaptic excitatory coupling
between pyramidal cells and inhibitory neurons in CA1. As shown in pharmacologically induced
gamma-oscillation models in vitro (Mann et al., 2005) and also in vivo (Penttonen et al., 1998;
Csicsvári et al., 2003b), pyramidal cells are generally phase-locked to the trough of gammaoscillations (recorded in the pyramidal cell layer) and interneurons follow them with a small phaselag and are positioned on the ascending slope of gamma-waves indicating again a monosynaptic
connection between the participating elements.
We also examined phase-locking of pyramidal cells and interneurons in WT and PV-GluRA KO mice. The mean phase of averaged spike-phase histograms of pyramidal cells did not differ
much between WT and KO mice either during gamma- or ripple-oscillations. In agreement with the
literature, the maximum firing probability of pyramidal cells was roughly in the trough of gammaoscillations (315.98±8.92 and 330.08±3.90 degrees for 26 WT and 90 KO pyramidal neurons;
circular means and standard errors of the circular means; 0 and 360 degrees being the trough of the
oscillatory wave and 180 degrees denoting the peak of it; p=0.33, to compare circular means
Watson-Wheeler circular test is used throughout this chapter) and very close to the trough of rippleoscillations (289.98±5.08 versus 280.48±3.11 degrees from 26 and 98 pyramidal cells from WT and
KO animals respectively, p=0.28). However, in our study, contrary to the literature the maximal
firing probability of pyramidal cells is during the descending slope of theta-waves (266.83±5.99 and
288.78±4.40 degrees for 29 WT and 103 KO pyramids, p=0.02). When analyzing interneurons, we
Pyramidal cells
Relative spike-count
Relative spike-count
Relative spike-count
Relative spike-count
Relative spike-count
Relative spike-count
Figure 33.: Phase-modulation of pyramidal cells (left panels) and interneurons (right panels)
during distinct oscillations. Cells from WT animals are indicated with blue, those of KO with red.
During theta-oscillations (A) pyramidal cells were less well modulated in the mutants whereas
interneurons were tuned a bit more sharply. In theta-related gamma-rhythm (B), interneurons
displayed an increased modulation depth in KO mice whereas pyramidal cells showed a decreased
modulation. In ripples (C) both principal cells and interneurons were more sharply modulated. One
can also see that during gamma, interneurons in KO mice are relatively delayed compared to those
in WT mice. The same holds true for ripples. Note that on these plots the spike-counts in each
phase-bin are normalized to the trough of the modulation curves, therefore troughs are always
represented by the value 1. The phase-values are given in degrees, the green curve represents an
oscillatory reference-wave.
found a phase-shift in the spike-phase histograms both for theta-, gamma- and ripple-oscillations.
Surprisingly interneurons tended to discharge a bit “earlier” on the descending slope of theta-waves
in the KO group (figure 33.A, 289.68±9.73 degrees for 17 WT and 267.40±4.71 degrees for 62 KO
interneurons). However, this effect was not significant (p=0.84). During gamma-oscillations
(gamma nested within theta-rhythm) interneurons fired slightly later in the KO than in the WT
group (figure 33.B, 2.83±9.65 degrees for 16 WT and 56.175±4.05 degrees for 60 KO interneurons,
p=0.15), not in the troughs as the WT interneurons did but rather on the ascending slopes of
gamma-waves. Even though this tendency was not significant, we also found it during ripples of the
KO mice when comparing it with cells from the WT mice (figure 33.C, 288.35±11.14 versus
341.44±4.57 degrees for 16 WT and 59 KO interneurons, p=0.18). To sum up these results, it seems
that during gamma-oscillations of WT mice interneurons followed the pyramidal cells with a slight
phase lag (around 45 degrees) which became much longer (almost 90 degrees) in PV-GluR-A KO
mice. During ripples of WT mice the pyramidal cells and local circuit neurons fired almost at the
same time but in the PV-GluR-A mutants there was a delay of almost 60 degrees.
Interestingly, during theta-oscillations pyramidal cells in KO animals displayed a reduced
modulation depth (relative difference between the peak and trough of the spike-phase histograms,
p=0.0002, paired t-test for 20 phase-bins covering a complete oscillatory cycle) while the difference
between the phase-modulation of interneurons turned out not to be significant (p=0.84). During
gamma-oscillations (gamma related to theta-oscillations, this means during exploration or REMsleep) pyramidal cells from PV-GluR-A KO animals were less modulated by the local field than
their WT counterparts (p=0.0062). Interneurons, however, were more sharply tuned in the mutants
(p=0.0011). During ripples the depth of modulation was higher for both KO pyramidal units and
interneurons compared to WT units (p=0.0016 for pyramidal cells and p=0.002 for interneurons,
paired t-tests). Altogether the increased modulation depth of both cell types in the KO mice during
ripples is in line with the increased ripple-power we usually find in PV-GluR-A KO mice.
We also estimated whether the recorded units have a certain phase-preference regarding the
oscillations (figure 34.). The Rayleigh test performed on every unit indicated that all interneurons
had a certain theta-phase-preference in WT as well as in KO animals whereas a majority but not all
pyramidal cells were significantly modulated by the theta-rhythm (24 from 29 pyramids in the WT
and 94 from 103 pyramids in the KO group).
As also suggested by previous studies (Csicsvári et al., 1999b), many pyramidal cells may
be place cells that show phase-precession during theta-oscillations. Therefore they may not have
such a strong phase-preference. Interestingly, practically all interneurons were significantly
modulated by the gamma-rhythm, (16 cells from 16 WT and 58 from 60 interneurons in the KO)
whereas roughly half of the pyramidal cells showed such phase-locking to gamma (15 from 26 in
the WT and 50 from 90 in the KO group). The majority of both pyramidal cells (25 from 26 WT
and 91 from 98 KO) and interneurons (14 from 16 WT an 58 from 59 KO) were significantly
modulated by ripple-waves.
Pyramidal cells
Figure 34.: Preferred phases of significantly modulated units during theta-oscillations (A), gamma(B) and ripple-rhythms (C). Cells from WT animals are marked with blue, those from KO mice with
red colour. The preferred phases are plotted on circular histograms in a way that the zero phase
(and consequently the 360 degrees, on the right side of the circle) indicate the peak of a given
oscillatory wave and 180 degrees denote the trough (left side of the plots). Therefore the
descending slopes of oscillatory waves are represented by the upper half of the circles and the
ascending slopes by the lower halves. The wave proceeds on the plots in a counterclockwise
fashion. One can see that while there is not much difference regarding pyramidal cells,
interneurons in KO mice have altered preferred phases with respect to gamma- and rippleoscillations, corresponding well with unitary phase-modulation histograms.
Analyzing in vivo recordings from PV-GluR-A KO mice we made several interesting
observations. Even though PV-cells in the hippocampus of these mice are supposed to have a
significantly reduced excitatory input and therefore are thought to be partially uncoupled from the
excitatory network, we did not find a strong deficit in the generation of gamma- and rippleoscillations. Surprisingly, ripples tended to be of even higher amplitude in KO than in WT mice and
we did not find pronounced changes in the gamma-power either. However, we saw a modest
gamma-frequency decrease during REM-sleep of these animals. Interestingly, in vitro these mice
exhibited a strongly reduced gamma-power (Fuchs et al., 2007, figure 35.). Now we have to clarify
what circumstances could have led to these disparate results. The in vitro study was performed in
horizontal CA3-slices using a kainate-application protocol. One might speculate that an intact
hippocampus (as in our experiments) might not be as vulnerable to the genetic defect as a slicepreparation since many of the anatomical connections are destroyed in the slice. We cannot rule out
either that other brain structures might adjust their input to the changed hippocampal circuitry,
thereby providing a different sort of compensation, and it is actually known that parahippocampal
structures, like the entorhinal cortex, have an impact on hippocampal gamma-generators (Bragin et
al., 1995). Oscillations evoked in vitro peak around 30 Hz, thus, they are considerably slower than
gamma-oscillations in vivo, which have a more or less homogeneous spectral distribution between
30 and 80 Hz. In this manner the gamma-oscillations we examine may be of a different nature and
origin than gamma-oscillations in vitro. Another possibility is that different hippocampal
subregions might show different sensitivity to the genetic defect.
Figure 35.: Gamma-oscillations induced by kainateapplication in WT and PV-GluR-A KO animals. The
upper panel shows the kainate-concentration
dependence of gamma-oscillations in vitro. In the
lower panel, example traces of gamma-oscillations in
WT and KO mice are shown. The power of gammaoscillations (measured between 20 and 80 Hz, plotted
in the lower right corner) is significantly reduced in
the mutants.
Figure is from Fuchs et al., 2007.
The increased ripple-power we see in PV-GluR-A KO mice could be related to the overall
higher excitability of the pyramidal cell-network, due to the underperformance of local circuit
neurons, even though this excitability in vivo does not reach the threshold for epileptic phenomena.
Indeed, perisomatic inhibition is thought to be very strong during ripples, requiring a very strong
dendritic excitation on a given pyramidal cell to make it fire during the event. Thus, a weaker
inhibition would result in increased pyramidal cell firing, making the ripples more pronounced.
However, a higher ripple-power does not necessarily mean more cells that fire but can also mean
that individual IPSPs and EPSPs get bigger or that compound postsynaptic potentials get bigger.
The amplitude of compound postsynaptic potentials can be related to the number of cells
participating in the potential-fluctuations or to the precise short-time synchrony between them. The
same number of cells, if more synchronized, can bring about bigger local field potential
fluctuations. Interestingly, pyramidal cells in PV-GluR-A KO mice did not fire more during ripples
than pyramidal neurons from the WT group, even though their bursting frequency was higher
during ripple events. Interestingly, it is rather the increased modulation depth of cell-firing which
seems to be responsible for the higher ripple-power in the KO mice. Secondary mechanisms like
gap junctions can also take over the synchronizing function of perturbed inhibition in the
oscillogenesis. As a consequence, an increased number of spikelets could also result in a higher
ripple-power. Eventually decreased inhibition might be favourable for antidromic spike-propagation
and can shift ripple-frequency towards higher values in computer network models (Traub et al.,
2000). However, this scenario would also mean more cells firing during ripples which we actually
do not find. We cannot rule out that different interneuron-subpopulations might “reinnervate” the
network in a different pattern which would lead to an altered balance between excitation and
inhibition. In other words, in conventional knockouts we cannot rule out developmental effects.
However, since PV-expression starts only around two weeks of age in mice, early developmental
alterations can be excluded.
To understand how millisecond-scale synchrony is brought about in ranges of several
millimeters during gamma- and ripple-oscillations belongs to the daunting questions of
electrophysiology given that interneuron axons generally do not span more than several hundred
micrometers. Ripples, however, are synchronized relatively well along the longitudinal axis of the
hippocampal formation and its output pathways, regions spanning several millimeters (Chrobak &
Buzsáki, 1996), even though as our results also suggest, there is a substantial drop in the phasecoherence already in a few hundred micrometers. To exert their synchronizing effect, interneurons
need either a very precise and coordinated coupling to the excitatory network or very reliable gap
junction-coupling between themselves (Traub et al., 1998). Recent research also suggests that longrange interneuron projections exist between the hippocampus, subiculum, presubiculum and other
retrohippocampal structures and many of these interneurons increase their firing during ripples
(Jinno et al., 2007). Interestingly, most of these cells are PV-negative. Our results suggest that by
compromising the excitatory neurotransmission on PV-cells, the network can still synchronise very
efficiently. Thus, additional mechanisms, possibly also different cell-types seem to be involved as
well in this form of network synchrony. An alternative explanation could be that the “excitatory
deinnervation” of PV-cells “forces” them to switch back to their developmentally premature state
when GABA can have an excitatory effect. This mechanism could be brought about by the altered
expression of KCC2- and NKCC1-molecules, as proposed for pyramidal cells in temporal lobe
epilepsy after traumatic lesions (Cohen et al., 2002; Stein & Nicoll, 2003). In this scenario, phasic
GABAergic currents in PV-cells could substitute for the missing glutamatergic input.
Among hippocampal oscillations the theta-rhythm deserves big attention since its relation to
cognitive functions has been suggested many times. In human subjects for example the performance
of navigation tasks promotes synchrony in the theta-range and there is a correlation between the
difficulty of the task and the actual theta-power (Kahana, 2006). The theta-rhythm in the
hippocampus also seems to be preserved in the PV-GluR-A KO mice. However, given the many
neurotransmitter systems involved in theta-generation (Yoder & Pang, 2005) we cannot exclude
that compensatory effects stand behind this phenomenon. Hippocampal interneurons also receive
GABAergic innervation from septal interneurons and they provide the septum with a reciprocal
GABAergic projection. As a consequence, theta-generation could be perturbed in the
septohippocampal loop. On the other hand, the septal nuclei also provide hippocampal interneurons
with cholinergic input. In this manner, septal pacemakers can maintain a normal theta-rhythm even
in the PV-GluR-A mutants. This theta can still modulate gamma-oscillations in a normal way. In
any case, it would be interesting to record from the hippocampus and the medial septum
simultaneously, to reveal in more detail the features of theta-generation.
Our unitary findings indicate that by compromising the excitation of PV-cells, the firing
rates of interneurons do not change substantially even if we find a slight tendency towards
decreased interneuronal firing rate during theta-oscillations. However, since we cannot discriminate
between different interneuron-subpopulations in our interneuron-pool, this result can indicate a
substantially underperforming PV-cell-population together with an unaltered or slightly
compensating subpopulation of other GABAergic cells, be that CCK-positive neurons or
interneurons expressing other markers. In a different scenario, PV-cells may possess “pacemaker”properties, in which case they could generate action potentials without substantial excitatory input.
As a result, their overall firing rates might not change much but the firing precision of basket cells
could decrease, a possible outcome corroborated by the results on in vitro gamma-oscillations from
GluR-D KO mice. However, as we saw, the PV-GluR-A KO mice are also different from the GluR83
D KO animals in this respect, because interneurons in PV-GluR-A KO animals displayed an
increased phase-modulation during gamma- as well as ripple-oscillations. Interneurons in the PVGluR-A KO mice increase their firing rate during ripples as WT interneurons do, however,
additional mechanisms, such as gap junctions can also take part in this process or might become
Interestingly, the main effect of modifying interneurons can be measured on pyramidal cells
in a more sensitive way. Even though the average firing rate of principal cells was unaltered in the
PV-GluR-A mutants, these pyramidal cells could accelerate to higher values during bursts, which is
the favoured mode of pyramidal cell discharge. In addition to that, pyramidal cells in the PV-GluRA KO mice were more likely to participate in bursts than those in WT mice. The higher bursting
frequency is even more prominent during theta-oscillations. Pyramidal cells in KO mice exhibited a
lower firing rate during theta-periods than their WT counterparts but surprisingly their bursting
frequency was higher than those in WT mice. Thus, we see a complex phenotype. It seems that
during theta-oscillations pyramidal cells from PV-GluR-A KO mice reach their firing threshold
more rarely than principal cells from WT mice but when they reach it, they can fire faster.
Experiments carried out in the Buzsáki-lab (Harris et al., 2001) have shown that the longer a
pyramidal cell is silent, the more likely it responds with burst firing. It is assumed that this property
is in connection with the depolarization block of their fast Na+-channels. In this manner, it takes
longer for a perturbed inhibitory network to relieve this block on pyramidal neurons, but when this
is achieved, the pyramidal cells can fire faster most probably because of the suboptimal function of
inhibitory cells. Pyramidal cells in PV-GluR-A KO mice also exhibit normal average firing rates
during ripples, however, their bursting frequency is also higher during ripples compared to WT
As discussed in the Results section, we do not find drastic changes in the phase-preference
of either pyramidal cells or interneurons in PV-GluR-A KO mice, even though putative basket cells
preferentially discharged at a slightly earlier theta-phase in the mutant compared to WT mice and
also during gamma- and ripple-oscillations interneurons were delayed in the KO animals. However,
the modulation depth of KO interneurons exceeded that of the WT control especially in gammaand ripple-oscillations. These phenomena can result from the insufficient excitation the interneurons
receive, because PV-cells would reach their firing threshold only in more circumscribed oscillatory
phases. The delay of interneurons related to the gamma-phase (and also ripple-phase) indicates a
problem with the activation of interneurons in feed-forward or feed-back excitation and this delay
can also explain the gamma-frequency decrease we find in REM-sleep (figure 18., magnified and
replotted on figure 36.). Interestingly, PV-cells in GluR-D KO mice showed a weaker modulation in
in vitro gamma-oscillations (figure 37.) and this would also mean a decreased phase-modulation
with respect to gamma-oscillations in vivo.
Frequency inside
envelopes (Hz)
Figure 36.: PV-GluR-A KO
mice display a reduction in the
REM-gamma-frequency, which
is most pronounced on peak of
the theta-waves.
See the part on oscillatory
analysis and fig. 18 for more
Theta-phase (rad)
Figure 37.: Decreased phase-locking of
PV-cells in pharmacologically induced
gamma-oscillations in GluR-D KO mice.
On the left ten oscillatory waves and the
same number of action potentials are
seen as an overlay. On the right the
precise distribution of action potentials
with respect to the peak of the gammawaves is seen.
Figure is from Fuchs et al., 2007,
However, we cannot directly relate in vitro findings from GluR-D KO mice with in vivo
results from PV-GluR-A KO mice because the GluR-D subunit also determines the fast kinetics of
glutamate receptors. Thus, slowing down the AMPA-currents on PV-cells by deleting GluR-D may
have a more profound effect on the spiking precision which could lead to a decreased gammaphase-modulation in vitro, an outcome which we see in GluR-D KO mice but do not find in PVGluR-A mutants. The decreased phase-modulation in the GluR-D-mutants can also account for the
more pronounced gamma-power decrease and the slight gamma-frequency decrease in those
animals (Fuchs et al., 2007), a result, which is different from that obtained in PV-GluR-A KO mice
in vivo, even if we see a slight gamma-frequency decrease during REM-sleep in the latter.
We also find a higher modulation depth for pyramidal neurons and interneurons in PVGluR-A KO mice during ripple-oscillations. To understand this finding let us imagine that due to
the perturbed excitatory drive of interneurons, more pyramidal cells are recruited to a given sharpwave in the CA3-region of PV-GluR-A KO than in WT mice. As a consequence, CA1-interneurons
receive a proportionally stronger drive, which could approach or even exceed the levels in WT
mice. The stronger drive together with the altered sensitivity of interneurons could bring
interneurons into motion in a more synchronised way. Thus, by auxiliary mechanisms the
interneurons can be even sharper modulated and the sharper modulation of the interneurons can also
be responsible for the sharper modulation depth of pyramidal cells later on. The mutant ripples
would be the “fruit” of an altered, more synchronised inhibition and a more sharply tuned
excitation, but still not reaching the threshold for epileptic discharges.
Even though it is tempting to correlate decreased gamma-power with cognitive deficits, we
could not reproduce the in vitro results on gamma-oscillations in vivo. However, pyramidal cells in
the PV-GluR-A KO mice behaved in a different way than those in WT mice, especially during
theta-oscillations. The unitary analysis suggests that pyramidal cells fire overall less frequently
during awake exploration and REM-sleep in KO animals. This may affect the navigation skills and
memory-acquisition of these mice since hippocampal principal cells may be less accessible for
excitatory inputs in the named behavioural states. However, in certain cases, pyramidal cells could
fire with higher-frequency bursts. Altogether these features can make their activity less predictable
and less controllable. A decreased firing precision can be harmful to the stability of cell-assemblies
and in a similar way to the consolidation and stability of place fields. This can explain why PVGluR-A KO animals underperform in short-term memory tests, such as T-maze and novel object
recognition. The increased bursting frequency of CA1 pyramidal neurons during ripples might also
mean that less well potentiated pyramidal cells also respond with burst firing in the actual
oscillatory episode. This may lead to memory-consolidation in a less contrasted way and therefore
may also lead to memory problems in the long run, even though the reference memory of the PVGluR-A mutant mice seemed to be intact. Another possibility could be that as a consequence of
compromised inhibition gap junctional coupling might be enhanced and this would interfere with
the function of pyramidal neurons more profoundly, making their output less controllable. This
model, however, is unlikely and would suggest that gap junctions are atavistic remains in the central
nervous system and instead of being useful devices, they increase its noise-level.
The altered network synchrony observed in the CA1 hippocampal subregion in PV-GluR-A
KO mice raises many interesting questions. Analysis of place cells and place fields in these mice
will be carried out next and will be useful to establish links between single-cell activity and
behaviour. The impaired performance of the mutants in the novel object recognition tests suggests
that pattern completion and pattern separation may also be perturbed in these animals. These
functions have been associated with the CA3 region (Nakazawa et al., 2002) and DG (McHugh et
al., 2007). Therefore in vivo recordings from CA1, CA3 and DG during cognitive tasks and in
remapping experiments could further enhance our understanding of cognitive alterations in the PVGluR-A KO mice.
To reduce the probability of developmental alterations in knockout mice, we will resort to
viral-injection exteriments in distinct hippocampal subfields using adult “floxed” GluR-A mice. The
Cre-gene in these viruses, however, should be under the control of PV-promoter, which has not yet
been achieved.
First, I would like to say thanks to my Mother. Without her psychological support and
sacrifice this research would not have been possible.
I am extremely grateful to my supervisor, Dr. Alexey Ponomarenko, who taught me all the
techniques and provided me with many self-written programs, besides being an excellent
supervisor. Practically he set up the in vivo part in our lab with its facilities.
Andrey Sergiyenko’s help deserves a big acknowledgement as well, his help on compiling
programs under the LINUX-platform made our work much easier.
Dr. Elke Fuchs generated and provided me the mice for analysis in an extraordinarily
helpful way, therefore I would like to return thanks to her as well. I also would like to emphasize
that without the self-sacrifice of the PV-GluR-A mice this project could not have been pursued at
I would like to express great thanks to Prof. Hannah Monyer for giving me the opportunity
to work in her lab and for being my supervisor during this time period and Prof. Peter Seeburg for
being my first supervisor. Without their help and support, the in vivo electrophysiological lab could
not have been established at all. They guided me in an extremely interesting research field and
evoked my interest in many scientific questions.
I am extremely grateful to our two excellent secretaries, Dr. Laura Winkel and Catherine
Munzig for providing help in everything, be they of organizational or practical nature. I would like
to thank Dr. Anne Herb for corrections on the German version of the summary. I would also like to
acknowledge the GC791 (Graduate College 791 of the Biology and Medicine Faculties,
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