Download Rhythmic Spontaneous Activity in the Piriform Cortex

Cerebral Cortex May 2008;18:1179--1192
doi:10.1093/cercor/bhm152
Advance Access publication October 8, 2007
Rhythmic Spontaneous Activity in the
Piriform Cortex
Maria V. Sanchez-Vives, V. F. Descalzo, R. Reig, N. A. Figueroa,
A. Compte1 and R. Gallego
Instituto de Neurociencias de Alicante, Universidad Miguel
Hernández-Consejo Superior de Investigaciones Cientı́ficas,
03550 Sant Joan d’Alacant, Alicante, Spain and 1Current address:
Institut d’Investigacions Biomèdiques August Pi i Sunyer
(IDIBAPS), Villarroel 170, 08036 Barcelona, Spain
Descalzo and Reig have contributed equally to this work
Slow spontaneous rhythmic activity is generated and propagates in
neocortical slices when bathed in an artificial cerebrospinal fluid
with ionic concentrations similar to the ones in vivo. This activity is
extraordinarily similar to the activation of the cortex in physiological conditions (e.g., slow-wave sleep), thus representing a unique
in vitro model to understand how cortical networks maintain and
control ongoing activity. Here we have characterized the activity
generated in the olfactory or piriform cortex and endopiriform
nucleus (piriform network). Because these structures are prone to
generate epileptic discharges, it seems critical to understand how
they generate and regulate their physiological rhythmic activity. The
piriform network gave rise to rhythmic spontaneous activity
consisting of a succession of up and down states at an average
frequency of 1.8 Hz, qualitatively similar to the corresponding
neocortical activity. This activity originated in the deep layers of the
piriform network, which displayed higher excitability and denser
connectivity. A remarkable difference with neocortical activity was
the speed of horizontal propagation (114 mm/s), one order of
magnitude faster in the piriform network. Properties of the piriform
cortex subserving fast horizontal propagation may underlie the
higher vulnerability of this area to epileptic seizures.
Keywords: endopiriform, epilepsy, olfactory, oscillations, up states
Introduction
The olfactory or piriform cortex is a 3-layered structure
(paleocortex), with excitatory and inhibitory neurons that are
more densely packed in layer II and in the more superficial part
of layer III, whereas layer I is mostly formed by axons from the
olfactory bulb and other cortical and extracortical areas (for
a review, see Neville and Haberly 2004). Deeper to the piriform
cortex is the endopiriform nucleus, considered by some
authors the fourth layer of this cortex because of their
extensive reciprocal connections (O’Leary 1937; ValverdeGarcia 1965; Luskin and Price 1983; Behan and Haberly 1999).
We will refer to the piriform cortex and the endopiriform
nucleus as the piriform network. The piriform cortex was
classically considered a primary sensory area, but it has been
recently proposed that it functions as an association area
because it is widely and reciprocally connected within itself
and with other cortical and extracortical areas and lacks
a definite columnar architecture (Neville and Haberly 2004).
Functionally, it seems to process odor information on relation
to other contextual clues and appears to be involved in
elaborate behavioral responses (Neville and Haberly 2004) by
activating distributed cortical ensembles (Rennaker et al.
2007). In this regard, individual superficial pyramidal neurons
in layer II have widespread axons that extend over most of one
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cerebral hemisphere and that arborize extensively within the
piriform cortex (Johnson et al. 2000). These long connections
are probably one of the factors contributing to the high
excitability of these structures (Behan and Haberly 1999). The
deep anterior piriform cortex has been called ‘‘area tempestas’’
because of its high excitability, which can provoke epileptic
discharges in certain conditions (Piredda and Gale 1985;
Hoffman and Haberly 1996). Spontaneous interictal spikes in
c-aminobutyric acid (GABA)-blocked slices also spread from
the piriform to the neocortex (Rigas and Castro-Alamancos
2004). In addition to the endopiriform nucleus, the deepest
part of layer III adjacent to it is also the origin of epileptiform
activity (Hoffman and Haberly 1991; Demir et al. 2001). As
a consequence of its high seizure susceptibility, the functionality of the piriform cortex has been thoroughly studied in
relation to epileptic activity (Piredda and Gale 1985; Racine
et al. 1988; Haberly and Sutula 1992; Hoffman and Haberly
1993, 1996; De Curtis et al. 1994, 1996, 1999; Forti et al. 1997;
Demir et al. 1999a, 1999b).
Here we describe spontaneous, non-epileptiform physiological activity generated by the piriform network in vitro, when
bathed in an artificial cerebrospinal fluid (ACSF) that mimicks
the ionic concentrations in situ (Sanchez-Vives and McCormick
2000). This rhythmic activity is similar to that described in the
piriform cortex in vivo during ketamine anesthesia (Fontanini
et al. 2003) and to some extent analogous to the slow
rhythmicity generated by the neocortex during both anesthesia
and natural slow-wave sleep (Steriade 1993; Timofeev et al.
2001). This physiological emergent activity from the piriform
network can eventually be transformed into epileptiform
activity, for example, by decreasing inhibition. Our main
interest has been to analyze the distinct properties of the
spontaneous rhythmic activity in the piriform network in order
to understand its control mechanisms and identify what makes
this area more prone to generate epileptiform activity.
Materials and Methods
Ferrets (2--12 months old, either sex) were anesthetized with sodium
pentobarbital (40 mg/kg) and decapitated. The entire forebrain was
rapidly removed to oxygenated cold (4--10 °C) bathing medium.
Horizontal slices (0.4 mm thick) were cut from the ventral side of the
temporal lobe. The first 4--5 slices obtained in this way contained the
piriform cortex and endopiriform nucleus (Fig. 1A). This slicing
orientation was chosen because it best preserves rostrocaudal
association fibers (Demir et al. 2001). In some experiments, additional
slices from the primary and secondary visual cortical areas (areas 17, 18,
and 19) were also placed in the bath, and recordings were made for
comparison.
During preparation of slices, the tissue was placed in a solution in
which NaCl was replaced with sucrose while maintaining the
Figure 1. Oscillatory activity in the piriform cortex. (A) Nissl-stained horizontal slice from the ferret, where the recordings illustrated in (B) were obtained. Anterior is down and
the 3 layers of the piriform cortex (I, II, and III) plus the endopiriform nucleus (EP) can be seen. (B) Extracellular multiunit activity recorded simultaneously in the 3 locations of layer
III indicated in (A). Spike times of the units that reached the threshold level (23SD; see Materials and Methods) are shown in the upper lines (events). (C) Autocorrelograms of
the 3 recordings shown in (B) reveal a frequency of oscillation of 1.7 Hz; the horizontal lines are the mean firing frequency. Dashed lines indicate 95% confidence interval (see
Materials and Methods for details).
osmolarity. After preparation, slices were placed in an interface style
recording chamber (Fine Sciences Tools, Foster City, CA). For the first
15 min, cortical slices were superfused with an equal mixture in
volume of the normal bathing medium and the sucrose-substituted
solution. Following this, normal bathing medium was switched into the
chamber and superfused the slices for 1--2 h. The normal bathing
medium contained (in mM) NaCl, 126; KCl, 2.5; MgSO4, 2; NaH2PO4,
1.25; CaCl2, 2; NaHCO3, 26; and dextrose, 10 and was aerated with 95%
O2 and 5% CO2 to a final pH of 7.4. Following this recovery time, the
solution was switched to one of ‘‘in vivo--like’’ ionic composition, which
had the same ionic composition except for different levels of (in mM)
KCl, 3.5; MgSO4, 1; and CaCl2, 1--1.2 (Sanchez-Vives and McCormick
2000). Bath temperature was maintained at 34.5--36 °C.
Electrophysiological recordings started once in in vivo--like ACSF.
Drugs were applied either in the bath or locally through the delivery of
a brief pressure pulse (10--150 ms; 100--350 KPa) to a drug-containing
micropipette (volumes of 1--20 pl per pulse). The following drugs were
used: D-2-amino-5-phosphonopentanoic acid (APV, local 500 lM),
Bicuculline methiodide (local 200 lM; bath 20 lM), 6-cyano-7nitroquinoxaline-2,3-dione
with
2-hydroxypropyl-b-cyclodextrin
(CNQX--HBC complex, local 250 lM), and L-glutamic acid (local 500
lM); all from Sigma (Sigma-Aldrich Chemie, Steinheim, Germany).
Ferrets were cared for and used in accordance with the Spanish
regulatory laws (BOE 256; 10/25/1990), which comply with the
European Union guidelines on protection of vertebrates used for
experimentation (Strasbourg 3/18/1986).
Spike Recording and Analysis
Extracellular multiunit recordings were obtained with 2--4 MX tungsten
electrodes (FHC, Bowdoinham, ME), amplified, and high passed ( >500
Hz) using a Neurolog system (Digitimer, Hertfordshire, UK). All
measurements of speed of propagation were obtained from recordings
whose exact location was anatomically identified (see below; Figs 1A,
7A, and 9B). Horizontal (same layer) and vertical (across layers)
propagation was measured by means of simultaneous recordings from 2
or 3 tungsten electrodes placed on the surface of the slice. Often, one
electrode was left in the same position and the others moved to
different recording locations along the same vertical (across layers)
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line, such that up to 8 measurements were done, and the rate of
propagation between different layers measured. To analyze the time
structure of the multiunit extracellular recordings, the signal was
digitized into ‘‘events’’: upward swings of the multiunit recording (120 s
acquired at 10 KHz) that crossed an arbitrary threshold were counted
as events (Fig. 1B). Three different thresholds were used: 1) 23
standard deviation (SD) of the noise, 2) 23 SD of the same recording
that was going to be analyzed, therefore including the activity and the
noise, and 3) a threshold chosen by eye at approximately 23
background noise levels. No significant differences were observed in
the duration or frequency of the oscillations measured with any of the
methods. All the data and figures presented here correspond to
a threshold located at 23 SD of the noise. The propagation, synchrony,
and duration of the oscillations were measured in auto- or crosscorrelograms. The Y axes in the auto- and crosscorrelograms are
expressed as ‘‘multiunit firing frequency (Hz)’’ as a measure of
correlation; however, this unit of spikes per second should not be
confused with the frequency of firing of a neuron. What multiunit firing
frequency (Hz) represents is the total number of accumulated spikes
per bin, divided by the number of sweeps, and then divided by the bin
width. To calculate the duration of the up states, we obtained
autocorrelograms of the spikes (events) occurring during a period of
120 s. The estimated mean frequency—expected value for a Poisson
distributed spike train—and the 95% confidence interval were
calculated and plotted (Fig. 2A, inset). The mean frequency represents
spikes per second without taking into account whether those spikes
were organized into rhythmic discharges or not. The duration of the up
states was taken as the half-width of the central peak in the
autocorrelogram (bins = 20 ms) at the point were the mean frequency
crossed the peak (Fig. 2A, inset).
The temporal relationship between the neuronal activities at varying
recording sites was examined with crosscorrelograms. The speed of
propagation was calculated from the offset of the central peak in
crosscorrelograms of the activity recorded by electrodes located at
a known distance, which was measured with a calibrated grid in the
microscope. The main frequency of the oscillations was estimated as
the tallest peak in the raw spike train power spectrum (0--20 Hz; Fig.
2B,D) obtained for the same 120 s of spike discharge used for the
autocorrelograms. A similar value for the main frequency of the
Figure 2. Characteristics of the spontaneous rhythmic activity in the piriform network. (A) Autocorrelogram and mean frequency (horizontal line) of multiunit activity in layer III.
The inset shows how duration was measured at the point where the mean frequency line crossed the central peak. The 2 dashed lines represent the 95% confidence interval. The
next peak in the autocorrelogram reveals the period of the oscillation (see Materials and Methods for details). (B) Power spectral density for the recording shown in (A), peaking
at 2 Hz. (C). Autocorrelogram illustrating an example of multiunit activity in endopiriform nucleus. Note higher frequency of oscillation and mean frequency of firing than in (A). (D)
Power spectral density of the same recording shown in (C). Notice that there are 2 peaks in the power spectrum at 2 and 6 Hz. In this case, 6 Hz was taken as the primary
frequency because the power was larger at this frequency. (E) Duration of the up states of the extracellularly recorded oscillations in different layers. Because many points
overlap, we give here the number of measurements per layer: layer I (n 5 6); layer II (n 5 12); border II/III (n 5 2); layer III (n 5 46); border III/endopiriform (n 5 5); and
endopiriform (n 5 21). The black points are the averages for the different layers (some in F and G), and the points in between layers represent the measurements taken at the
border between layers (total n 5 112). (F) Mean frequency of firing across different layers. (G) Primary frequency of the oscillations in the different layers taken as the main peak
in the power spectrum. (H) Average power spectrum for a total of 71 recordings in layers 2, 3, and endopiriform nucleus (recordings between layers have not been included here).
oscillations could be obtained by measuring the interpeak interval in
the autocorrelogram (Fig. 2A).
Intra- and extracellular recordings were digitized, acquired, and
analyzed with a CED interface and Spike 2 software (Cambridge
Electronic Design, Cambridge, UK). Neuroexplorer software
(Nex Technologies, Littleton, MA) was used for some of the spike
analysis (autocorrelograms, power spectra). Data are reported as
mean ± SD.
Intracellular Recordings
Sharp intracellular recording electrodes were formed on a Sutter
Instruments (Novato, CA) P-97 micropipette puller from mediumwalled glass (Clark capillaries GC100FS-10, Harvard App. Edenbridge,
UK or 1B100F-4, WPI, Sarasota, FL) and beveled on a Sutter
Instruments beveller to final resistances of 60--100 MOhms. Micropipettes were filled with 2 M potassium acetate, and in some
experiments, 50 mM QX-314 was added to prevent action potential
generation, allowing us to record the synaptic events at different
membrane potentials. Current clamp intracellular recordings were
obtained using an Axoclamp 2B Amplifier (Axon Instruments, Foster
City, CA). Only intracellular recordings of a stable membrane
potential <–60 mV and overshooting action potentials were included
in the sample.
Anatomical Reconstructions
Following each experiment, the slices were fixed in paraformaldehyde,
Nissl stained, and examined under the microscope to identify the
location of the recording sites. Some of these reconstructions have
been included (Figs 1A, 7A, and 9B). In some cases, biocytin (1.5--2%)
was added to the 2 M potassium acetate in the intracellular electrodes.
Biocytin-filled neurons were visualized through standard avidin--biotin-horseradish peroxidase reaction (ABC Vectastain kit) and processed
with diaminobenzidine (Sigma) as described by Horikawa and
Armstrong (1988).
Results
Spontaneous rhythmic activity was recorded from ferret
horizontal brain slices containing piriform cortex and endopiriform nucleus (n = 94 slices; Fig. 1A). The slices were
maintained in a modified ACSF that mimics the ionic concentrations in the cerebrospinal fluid (Sanchez-Vives and
McCormick 2000). Extracellular multiunit recordings revealed
rhythmic bouts of activity that propagated across the slice
(Fig. 1B,C).
The primary frequency of this activity was measured as the
tallest peak of the spikes power spectrum (see Materials and
Methods; examples in Fig. 2B,D) and was 1.8 ± 1.15 Hz (n = 92
measurements in different, anatomically identified locations of
the slices; range of frequencies: 0.2--8 Hz). No significant
difference was found in the main oscillatory frequency across
layers, which was close to 2 Hz in layers II, III, and in the
endopiriform nucleus (Fig. 2G,H).
The duration of the bouts of activity, or up states of the
network, was measured in autocorrelograms of the extracellularly recorded multiunit activity (Fig. 2A,C; see Materials and
Methods). The duration of the up states across different layers
is represented in Figure 2E for anatomically identified recording locations. The average duration was 90.0 ± 35.8 ms and
spanned between 20 and 238 ms. No significant differences in
duration were observed in correlation with the layer. When
recorded intracellularly, these bouts of activity appeared as
Cerebral Cortex May 2008, V 18 N 5 1181
Figure 3. Intracellular and extracellular recordings of rhythmic activity in piriform cortex. (A) Intracellular suprathreshold recording (Vm 5 55 mV) from a layer III neuron and
extracellular multiunit recording (bottom trace) obtained in the close vicinity of the neuron. Note how the intracellular barrages of synaptic activity and action potentials coincide
with the extracellularly recorded bouts of activity. (B) Subthreshold (Vm 5 70) recording of the same neuron. (C) Expanded barrages of synaptic activity corresponding to a and
b in (B). (D) Intracellular recordings from a different layer III neuron with an electrode containing 50 mM QX-314. Holding the membrane potential at different values unveils the
EPSPs and IPSPs concurring during the up states. (E) Intra (top) and simultaneous extracellular recordings in close vicinity (bottom) obtained from a different slice of piriform
cortex. At the beginning (left), there were subthreshold barrages of synaptic potentials. Blocking IPSPs with local application of 250 lM bicuculline strongly increased the slope of
depolarization and the amplitude of the depolarized state that now reached threshold and generated a burst of action potentials. While still in bicuculline, the glutamatergic
blocker CNQX (250 lM) was locally applied and its effect occurred progressively over the course of the next 10 min. At that time, a depolarization of approximately 1 mV in
synchrony with a decreased extracellular discharge remained, and it was promptly blocked by 500 lM APV (last trace to the right). The last 3 traces are expanded above.
barrages of synaptic potentials that could bring the membrane
potential to threshold and generate a brisk discharge (Fig. 3).
Intracellular Recordings
Intracellular recordings were obtained from 73 neurons in the
PC and endopiriform nucleus. In all cases, ongoing multiunit
rhythmic activity was recorded extracellularly in the vicinity of
the recorded cell. Out of 73 neurons, 61 showed barrages of
synaptic potentials in synchrony with the extracellularly
recorded activity (Fig. 3A). These bursts had amplitudes
between 0.5 and 8 mV and often did not reach threshold
(Fig. 3B,C). When they did, usually no more than 2--3 action
potentials were generated (Fig. 3A). These characteristics are
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different from the neocortex, where every recorded neuron
seems to participate in the global slow rhythm and reach
threshold in each cycle (Steriade 1993; Contreras et al. 1996;
Sanchez-Vives and McCormick 2000), although sparse firing
has been reported in layers 2/3 of some neocortical areas
(Waters and Helmchen 2006). Therefore, the neuronal
participation underlying spontaneous rhythmic activity in the
PC appeared to be more sparse than in the neocortex, but
when the global neuronal network is considered, its emergent
activity still consistently produced a regular pattern of activity
interspersed with silent periods.
Spontaneous rhythmic oscillations were associated with
periodic depolarizations or up states consisting of barrages of
Figure 4. Rhythmic activity in identified cells from the piriform cortex. (A) Intracellular recordings from the layer II neuron represented in (B). Top traces, response to the injection
of a 0.5 nA depolarizing pulse, showing spike frequency adaptation. Middle traces, subthreshold bouts of excitatory synaptic potentials corresponding to the up states and
simultaneous with the multiunit discharges below. Bottom trace, inhibitory synaptic potentials observable from a depolarized membrane potential coincidental with extracellularly
recorded oscillations. (B) Semilunar-pyramidal neuron from layer II in the piriform cortex. (C) Layer III pyramidal neuron. (D). Recordings of oscillations from the neuron in (C) at 2
different membrane potentials.
excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs). This was demonstrated for layer II
neurons (Fig. 4A,B), layer III (Fig. 4C), and endopiriform (Fig. 5)
neurons. The excitatory and inhibitory components were
evidenced by holding the membrane potential at different
voltage levels; thus, IPSPs could be observed at depolarized
potentials (Fig. 3D, QX-314 in the electrode; Fig. 4A; n = 20).
Also, blocking the IPSPs with local application of bicuculline
(GABAA receptor blocker) strongly increased the amplitude of
the depolarized state, which then would invariably reach
threshold (Fig. 3E).
Spontaneous Rhythmic Activity versus Ictal-Like Activity
Ongoing network activity has been previously described in the
piriform cortex and endopiriform nucleus while in 0 Mg2+, in
the presence of GABAA blockers, or during modifications of the
chloride levels (see Introduction). The resulting activity
following those procedures is ictal-like activity. However, the
spontaneous emergent activity that we describe in this study
had different properties from seizure-like activity, and it was
similar to the one described in vivo during xylazine--ketamine
anesthesia (Fontanini et al. 2003).
First, it appeared in the absence of any of the aforementioned conditions and as a consequence of ionic modifications
in the ACSF that are known to induce slow wave sleep-like
oscillations in the neocortex (Sanchez-Vives and McCormick
2000). Second, the characteristics of the activity itself were
different from the ictal-like episodes. Figure 6 illustrates the
spontaneous activity in a slice before and after the application
of the GABAA blocker bicuculline. Previous to bicuculline
application, there was a rhythmic discharge in the piriform
network (Fig. 6C) with bursts of synaptic potentials (Fig. 6B)
that evoked action potentials when suprathreshold (Fig. 6A).
Following local application of 200 lM bicuculline (n = 4
intracellular recordings; n = 6 extracellular recordings),
seizure-like responses appeared (Fig. 6D), consisting of highfrequency bursts of action potentials at 1--20 Hz in epochs that
lasted up to several tens of seconds. Intracellular recordings
revealed a paroxysmal depolarizing shift followed by fast
repetitive bursts of spikes during ictal-like activity (Fig.
6D,E,F). Synaptic activation showed faster rise time and higher
Cerebral Cortex May 2008, V 18 N 5 1183
Figure 5. Rhythmic activity in the endopiriform nucleus. (A) Intracellular recording (top trace) from the neuron in (B), and synchronous extracellular multiunit recording in the vicinity of
the neuron in the endopiriform nucleus. Note that the extracellularly recorded bursts of activity correspond intracellularly with bursts of synaptic potentials that often remain
subthreshold. Inset, autocorrelogram of the extracellular activity showing an 8 Hz oscillation. (B) Reconstruction of a multipolar neuron in the endopiriform nucleus whose intracellular
recording is illustrated in (A). The fine trace corresponds to the axon. (C) Reconstruction of an endopiriform multipolar neuron. Note the dense axonal arborization in the vicinity of the
neuron. Inset, intracellular recording for the same neuron. Note the rhythmic synaptic potentials corresponding to a 12-Hz oscillation of the membrane potential. (D) Microphotograph
(203) of the same neuron reconstructed in (C). Note the en passant synaptic boutons in the close vicinity of the neuron. An inset at higher magnification illustrates the dendritic spines
in this neuron. (E) Autocorrelogram of the multiunit extracellular activity in the endopiriform nucleus from a different slice illustrating the characteristic high frequency, in this case of 10
Hz. (F) Extracellular multiunit recording (bottom trace) from which the autocorrelogram in (G) was obtained. The mean frequency of the spikes that crossed the threshold (see Materials
and Methods) is shown in the middle trace. Intracellular recording (top trace) obtained next to the extracellular recording. Note that the high frequency of the extracellular recording is
reflected in the membrane potential. (G) Autocorrelogram of the voltage recorded intracellularly (black trace; left vertical axes) showing the rhythmicity in the membrane deflections.
Superimposed, autocorrelogram of the multiunit extracellularly recorded activity (right vertical axes). Note the overlap between both traces.
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Figure 6. Differences between oscillatory activity and ictal-like discharges induced by bicuculline. (A) Intracellular recording from a layer III neuron (top trace; Vm5 55 mV) and
multiunit extracellular recording obtained in the vicinity of the neuron (bottom trace). Note that the bouts of extracellular activity correlate with intracellular deflections that reach
threshold and evoke one action potential. Action potentials are truncated. Inset shows the response of this neuron to a square depolarizing pulse (0.8 nA). Scale of inset 25 mV,
50 ms. (B) Subthreshold recording of the same neuron (Vm5 63 mV) in the same electrode configuration. Intracellular voltage trace (top) shows the bursts of EPSPs and IPSPs
occurring simultaneously with the bouts of extracellularly recorded discharge (bottom). (C) Autocorrelogram of the extracellular activity recorded in the vicinity of the cell shown in
(A and B). Mean firing frequency and 95% confidence interval are displayed. (D) Intracellular recording (top trace; Vm5 64 mV) of the same neuron as in (A, B) and extracellular
(bottom trace) recording in the vicinity following application of bicuculline. Observe the long duration (16 s) burst of activity followed by a long period of silence that intracellularly
appears as a hyperpolarization. Note the absence of synaptic activity during the period of silence, reflecting the lack of activity in the network and not only in this neuron. Synaptic
activity is expanded in 2 insets, pre- and postictal-like burst, and they correspond to the zones indicated by the horizontal lines. The beginning and the end of the ictal-like burst
are expanded in (E and F), respectively. (E) Intra- (top) and extracellular recording (bottom) of the beginning of the ictal-like burst. (F) Same as (E) for the end of the burst.
amplitude than synaptic barrages during spontaneous rhythmic
activity (Figs 3E and 6F). Following these periods of intense
discharge, there were silent intervals that lasted up to several
minutes, during which all synaptic activity disappeared (Fig. 6D).
Spontaneous oscillations were blocked by the local applications of CNQX (non-N-Methyl-D-aspartic acid (NMDA) glutamatergic receptor blocker; 250 lM concentration in
micropipette) as revealed by both multiunit recorded activity
(n = 12) and intracellular recordings of rhythmic synaptic
barrages (n = 3; see below). However, ictal-like discharges
induced by bicuculline were not completely blocked by CNQX
(multiunit recordings n = 3), the remnant oscillation being
blocked by local application of the NMDA blocker, APV (500
lM; n = 2 intracellular recordings; Fig. 3E). This participation of
NMDA receptors in epileptiform discharges is consistent with
previously published results by other authors (Hoffman and
Haberly 1989).
Activity Propagation across Layers
Simultaneous recordings with vertically aligned electrodes (see
Materials and Methods) revealed rhythmic activity occurring
simultaneously across the width of the cortex (n = 16 vertically
aligned sets of measurements in 11 slices), although in layer I this
activity could be recorded only in its deepest part (Supplementary Fig. 1). In all cases, for each of the bouts of activity, the
discharges appeared first in the deepest part of layer III and in the
Cerebral Cortex May 2008, V 18 N 5 1185
Figure 7. Vertical propagation of rhythmic activity across the different layers. (A) Three simultaneous multiunit extracellular recordings reveal coincident activity in the
endopiriform nucleus (electrode 1) in layer III (electrode 2) and in the border between layer II and layer III (electrode 3). The upper traces show the firing frequency of the events
that crossed the threshold (see Materials and Methods). The slice was Nissl stained after the experiment, and the sites of recording are shown on the left. (B) Crosscorrelograms,
triggered by activity in electrode 1, show that the oscillation spread toward the surface from the deeper layers. (C) Autocorrelograms (total time 250 s) show a dominant
oscillation period of 0.5 s (2 Hz) in the 3 recordings and a faster rhythm (6 Hz) in the endopiriform nucleus, which can also be discerned in the layer III recording. Notice the higher
level of activity in the deeper recordings.
endopiriform nucleus (Fig. 7). In 3 cases in which multiple
measures were taken, it was clearly observed that the site of
discharge onset for the oscillations was located between the
endopiriform nucleus and the border with layer III. In these cases,
the activity showed certain latency (0.9--7 ms) toward propagation to superficial layer III probably due to propagation delays.
The absolute speed of vertical propagation was difficult to
measure because the distances were short (150--1700 lm) and
the offset of the crosscorrelograms peaks (Fig. 7B), in many cases,
insignificant. Only the cases with a detectable offset of the peak
were included in our velocity estimate and, as a consequence,
there could be a bias towards slower rates of propagation.
Overall, 62 measurements of vertical propagation between
different layers were taken (n = 11 slices; 16 vertical alignments;
see Materials and Methods), resulting in an average rate of 541.8
mm/s. The distribution of these values was broad, between 18.7
and 4400 mm/s. Such distribution was well fitted by 2 Gaussians
(chi squared 1.08), centered at 103 and 532 mm/s, respectively.
These 2 values are compatible with 2 different pathways of
propagation between layers: a faster one for which the delay
could be caused only by axonal conduction and a slower one due
to intermediate synapses (Hoffman and Haberly 1993).
More evidence in favor of the activity being originated in the
deep layers was provided by experiments in which local
applications of CNQX were made (Fig. 8). Simultaneous
recordings made in endopiriform and layer II/III of the piriform
cortex and local application of 250 lM CNQX showed that
CNQX applied in PC did block the oscillations there, but they
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continued in the endopirifom nucleus (n = 6; Fig. 8B).
However, if CNQX was applied in the endopiriform nucleus,
the activity in the PC disappeared (n = 4; Fig. 8C).
Additional data obtained in slices in which the endopiriform
nucleus had been physically separated from the piriform cortex
by a knife cut (Supplementary Fig. 2) also support the origin of
the oscillation in the deep layers. In these cases, only in the
area of piriform cortex connected with the endopiriform
nucleus, the oscillations persisted, whereas in those separated,
rhythmic activity disappeared. On the other hand, the
endopiriform nucleus did not seem to require the connections
with the PC in order to generate rhythmic activity, and those
areas that were separated (Supplementary Fig. 2 e and g) still
showed a robust oscillation.
Spontaneous Rhythmic Activity in Deep Layers and
Endopiriform Nucleus
Several differences were observed in the spontaneous activity
in deep layers versus superficial layers, all of them suggestive of
a higher excitability in deep layers (deep layer III and
endopiriform nucleus). The mean frequency of extracellular
discharge (Fig. 2F; see Materials and Methods) was significantly
higher in deep layers when taken as a whole (III and IV; 258 ±
97 spikes/s; n = 17) than in superficial layers (I and II; 200 ± 64
spikes/s; n = 66; P = 0.02).
No significant difference was found in the main frequency of
oscillation (highest peak in the power spectrum) across layers
Figure 8. Role of deep PC layers and endopiriform nucleus in the generation of the rhythmic activity. (A) Scheme indicating the location of the tungsten electrodes performing
multiunit recordings and the puffers containing CNQX (250 lM) for local application. (B) Simultaneous recordings in the endopiriform nucleus and in the PC above (layer II--III)
before (left) and after local application of CNQX (250 lM) in the PC. Note that the bursts of activity in PC, but not in the Ep, disappear after the local application of CNQX. (C)
Synchronous multiunit recordings in PC and Ep before (left) and after local application of CNQX (250 lM) in the Ep. Note that as a result of local blockade of glutamatergic
excitation in Ep, the activity is now also vanished in the PC. After the washout of CNQX (right), the activity is recovered in both locations.
(Fig. 2F,H). However, in 83 out of 112 extracellular recordings,
a second peak in the power spectrum was also visible (e.g., Fig.
2D). This secondary frequency spanned between 0.2 and 10 Hz.
Similar to the primary frequency, no significant difference was
found in the mean secondary frequency across different layers
(data not shown), but considering together both the primary
and the secondary frequencies, we found that all the cases of
frequencies higher than 4 Hz (n = 14) occurred in deep layers
(see Figs 2C,D, 5, and 7C and Supplementary Fig.1C). These
higher frequencies often did not propagate toward more
superficial layers. Supplementary Figure 1 illustrates a slice in
which the recordings in endopiriform nucleus had a primary
frequency of 5 Hz that progressively faded away toward the
surface. Therefore, the 5 Hz component was restricted to deep
layers. A similar situation is shown in the simultaneous multiunit
extracellular recordings of Figure 7A. In this case, there was an
oscillation in the endopiriform nucleus of 2 Hz that propagated
toward superficial layer III and layer II, interleaved with a 6-Hz
oscillation of lesser amplitude that was not evident in the
superficial layers. These higher-frequency oscillations were
sometimes reflected intracellularly as membrane voltage
deflections of the same frequency and duration as the
extracellularly recorded events. Figure 5 illustrates the intracellular recordings corresponding to 2 reconstructed endopiriform neurons. Both of them (Fig. 5B,C) are multipolar
neurons, with numerous spines (Fig. 5D) and axons that form
numerous local en passant boutons (Fig. 5D). The recordings
from both neurons reveal a high-frequency (8--12 Hz) component that is reflected in the multiunit and in the intracellular
recordings (see insets in Fig. 5A,C). A slower component ( <1
Hz), similar to the slow rhythm in neocortex (Steriade 1993;
Sanchez-Vives and McCormick 2000), was also present in some
cases either as the primary or as the secondary frequency. The
average power spectra represented in Figure 2H reflect that
slow component in the oscillations for values around 0.2 Hz.
To better analyze the activation of superficial layers, we
studied the propagation of activity induced by local applications of glutamate (500 lM; n = 4 slices; see Materials and
Methods). Glutamate activation of deep layers induced a burst
of activity in superficial layers (data not shown). Not every
burst of activity induced in deep layers did propagate toward
superficial layers: a threshold in the intensity of the discharge
in the deep layers had to be reached in order to induce
a consistent propagated response in the superficial layers. This
finding was coherent with the observation that local rhythms of
higher frequencies recorded from deep layers did not
propagate toward superficial layers, suggesting that they were
not massive enough (Fig 7A). All the findings described in this
section are suggestive of a prominent excitability of deep layers
along with a higher threshold for recruitment of neurons in
superficial layers. These results are in agreement with previous
reports of both neuronal and network excitability in deeper
layers and with the attribution of a high epileptogenic potential
to the border between layer III and endopiriform nucleus (see
Discussion).
Horizontal Propagation of Activity
Using multiunit recordings with 2 electrodes, we studied the
rate of propagation of the rhythmic bouts of activity. The
recording electrodes were placed at the same layer (usually
layer III) at distances between 0.6 and 5 mm (Fig. 9B) and the
conduction delay measured as the peak offset in the crosscorrelograms between the activities recorded by each electrode (Fig. 9A,C; see Materials and Methods). Figure 9D shows
the distribution of the measured propagation rates that
resulted in an average speed of propagation of 114 ± 54 mm/s
(n = 32 measurements in anatomically identified locations from
different areas in 13 slices). This value was one order of
magnitude faster than the one obtained by the same means in
the ferret visual cortex (10.9 mm/s; Sanchez-Vives and
McCormick 2000).
Because a minimum delay in the activity recorded with the 2
electrodes was required to calculate the speed of propagation,
we did not include those recordings where the peaks of the
crosscorrelogram were not clearly separated. Therefore, we
cannot rule out a bias in our measurements toward lower
values of propagation speed. It should also be considered that
horizontal propagation may include discontinuities (Chervin
et al. 1988; Chagnac-Amitai and Connors 1989). In any event,
a bias toward low propagation speeds would not hinder but
Cerebral Cortex May 2008, V 18 N 5 1187
Figure 9. The oscillatory activity spreads horizontally along the slice. (A) Two simultaneous recordings at sites separated 2.4 mm reveal propagating oscillatory activity. The
upper traces indicate the firing frequency. (B) Approximate points of recording in layer III are shown in the slice stained with the Nissl method. (C) The crosscorrelogram, triggered
by electrode 1, shows that the oscillatory activity reached electrode 2 with a delay of 19.2 ms, which corresponds to a conduction velocity of 125 mm/s. (D) Distribution of
conduction velocity values for 32 measurements as in (C).
reinforce the remarkable difference in propagation rate
between the piriform network and the neocortex.
Discussion
This is, to our knowledge, the first description of spontaneous
rhythmic activity in slices of piriform cortex maintained in
vitro. Slow oscillations of analogous characteristics have been
described in the rat piriform cortex in vivo during ketamine-xylazine anesthesia (Fontanini et al. 2003), but so far, there has
been no thorough characterization of spontaneous rhythmic
oscillations in the piriform cortex during natural sleep. In vivo
olfactory cortex oscillations are functionally correlated with
respiration, although they also occur in tracheotomized
animals (for differences, see Fontanini et al. 2003), strongly
suggesting that they are an emergent property of the neuronal
network. Here we have demonstrated that the isolated piriform
network does generate per se spontaneous, nonepileptiform
rhythmic activity, and we have characterized some of the
underpinning mechanisms.
In the neocortex, slow ( <1 Hz) rhythmic activity consisting
of recurring generation of up and down states has been
described through all recorded areas (Metherate and Ashe
1993; Steriade 1993; Cowan and Wilson 1994; Contreras et al.
1996; Stern et al. 1997; Sanchez-Vives and McCormick 2000;
McCormick et al. 2003; Shu et al. 2003; Isomura et al. 2006;
Volgushev et al. 2006; Luczak et al. 2007). These recurring up
states or periods of sustained depolarization consist of
concurrent excitatory and inhibitory synaptic events that give
rise to outbursts of action potentials. Slow neocortical
oscillations appear to occur as a result of the recurrent
connectivity of the cerebral cortex, their particular pattern
being a result of the intrinsic and synaptic properties of the
neurons in the network. With respect to the piriform cortex,
its functionality has been thoroughly studied but mostly on
relation to the generation of epileptiform activity, for which
synaptically mediated positive feedback is also the subserving
mechanism (Piredda and Gale 1985; Racine et al. 1988; Haberly
and Sutula 1992; Hoffman and Haberly 1993, 1996; De Curtis
et al. 1994, 1999; Forti et al. 1997; Demir et al. 1999b, 2000).
Persistent epileptiform excitatory activity has been induced in
1188 Rhythmic Spontaneous Activity in the Piriform Cortex
d
Sanchez-Vives et al.
slices of piriform cortex by bursting activity stimulation
(Hoffman and Haberly 1989, 1991), with low chloride ACSF
(Demir et al. 1999b), low Mg2+ (Hoffman and Haberly 1989),
disinhibition in bicuculline (De Curtis et al. 1994, 1999;
Halonen et al. 1994; Ebert et al. 2000; Demir et al. 2001; Rigas
and Castro-Alamancos 2004), or in slices prepared from
previously kindled rats (McIntyre and Wong 1986; Hoffman
and Haberly 1991; Haberly and Sutula 1992).
Our aim here was to study if the piriform network
maintained in vitro could generate any spontaneous, rhythmic,
nonepileptiform activity and to characterize it. Through the
analysis of this spontaneous activity, we can identify some of
the rules governing the functionality of the piriform network,
which is crucial to comprehend both the neuronal ensembles
underpinning odor processing and the reason why this area of
the brain is prone to the generation of epileptic discharges.
Spontaneous Rhythmic Activity in the Piriform Network
In Vitro
Here we have described that horizontal ferret slices containing
the piriform network and maintained in an ACSF with an in
vivo--like ionic composition (Sanchez-Vives and McCormick
2000) generate a spontaneous and continuous rhythmic
activity consisting in a cyclic sequence of up and down states
occurring at an average frequency of 1.8 Hz. Underlying the up
states were concurrent EPSPs and IPSPs building up a depolarization that not always reached threshold but that was
invariably coincident with the extracellularly recorded outbursts of activity. Such concurrency of EPSPs and IPSPs and the
build-up of activity are signs that these rhythmic bursts are the
result of recurrent connections in the network.
There is a difference between the rhythmic activity
generated by the piriform network in the slices and the slow
oscillation recorded in vivo (Fontanini et al. 2003). The latter is
functionally correlated with the flow of air through the nostrils,
therefore following the respiratory frequency (average of 0.97
Hz). However, after tracheotomy, there is still a slow oscillation
in the PC with a dominant frequency of 0.8 Hz (Fontanini et al.
2003), which is slower than that one observed in the in vitro
network. This could be due to the influence of ketamine-xylazine in the in vivo recordings or to the lack of peripheral
and central afferences in the slices. Given that piriform cortex
slices display both faster (6--12 Hz) and slower ( <1 Hz)
rhythms, it is feasible that those could become more or less
prominent in vivo under modulatory influences, given that the
substrate for the rhythm generation exists in the network.
Where Is the Spontaneous Rhythmic Activity Generated?
Crosscorrelograms of multiunit rhythmic activity along an axis
perpendicular to the cortical surface revealed that the
oscillations are initiated close to the border between layer III
of the piriform cortex and the endopiriform nucleus and
propagate toward superficial layers and, eventually, deeper into
the endopiriform nucleus. Deep layer III and endopiriform
nucleus are highly excitable due to the intrinsic properties of
their neurons (Tseng and Haberly 1989; Banks et al. 1996;
Magistretti and de Curtis 1998) and to the existence of
regenerative feedback loops based on a dense plexus of
intrinsic recurrent connections (Hoffman and Haberly 1991,
1993). This is also illustrated by the abundant axonal synaptic
boutons that endopiriform neurons have in their close vicinity
(Fig. 5B,C). In several studies, the deep layers of the PC have
been found to initiate epileptiform discharges. Piredda and
Gale (1985) gave the name of area tempestas to the area in the
brain with lowest threshold to generate generalized epilepsy by
chemoconvulsant injection and identified it as the deep
boundary of the PC. There is evidence that epileptiform
discharges induced by different methods originate in the
endopiriform nucleus and deep part of layer III (Hoffman and
Haberly 1991; Demir et al. 1999a, 2000; Ekstrand et al. 2001).
As we describe here, the higher excitability of these areas
not only explains their tendency to start epileptic discharges
but also contributes to the shaping of their activity in more
physiological conditions. In order to start a cycle of regenerative activity in a network with positive feedback
connections, it is necessary to reach a certain level of
spontaneous activity that brings to threshold a critical number
of adjoining neurons, enough to start an up state (Compte et al.
2003). Several lines of evidence indicate that physiological
rhythmic activity is initiated in the deep layer III--endopiriform
nucleus: 1) the network is more excitable there as shown by
a lower threshold in response to glutamate applications, higher
spontaneuous mean frequency of firing, and a tendency to
generate higher frequency rhythms; 2) application of CNQX to
the Ep blocks rhythmic activity in the PC; 3) there is a lack of
activity in PC when the deep layers are physically isolated by
a knife cut; and 4) a leading role in the initiation of the up states
when simultaneous vertical measures are taken.
In the neocortex, neurons in infragranular layers, namely in
layer V, exhibit some spontaneous firing during the down states
and they initiate both physiological and epileptiform activity
(Connors 1984) (see Fig. 2 in Sanchez-Vives and McCormick
2000). In the piriform network, this role seems to be played by
neurons in deep layer III and endopiriform nucleus, where
spontaneously bursting multipolar neurons are present (Tseng
and Haberly 1989).
In this study, the rate of propagation from deep to superficial
layers varied in different cases, its distribution occurring
around 2 means: one, 103 mm/s, of similar values to the ones
measured for the horizontal propagation (across the same
layer) and a second and considerable faster one at 532 mm/s.
This distribution of the vertical speeds of propagation suggests
that there are different pathways that can be alternatively used
for vertical propagation, the direct connections from endopiriform multipolar neurons to piramidal layer II cells (Haberly and
Price 1978; Haberly and Presto 1986) probably underlying the
faster rate of propagation. A speed of 500 mm/s is compatible
with axonal conduction velocity in the PC (0.2--2.5 m/s;
Hoffman and Haberly 1993) and absence of synaptic delays.
Piriform Rhythms and Neocortical Slow Oscillations
Cyclic oscillations resulting from recurrent connectivity are
a common outcome in both the neocortex and the piriform
cortex. However, as a result of the disparity in their anatomical
and functional structure, the emergent activity in each area has
different properties. At variance with the neocortex, where the
depolarizations occurring during the up states generally reach
threshold (Steriade 1993; Sanchez-Vives and McCormick 2000),
in the piriform cortex, both in vivo (Fontanini et al. 2003) and
in vitro (current study), they often did not reach firing
threshold in individual neurons. Still, whereas intracellularly
recorded oscillations may consist of none or few action
potentials, the extracellularly recorded multiunit activity
reveals a consistent network discharge, therefore suggesting
a more sparse neuronal participation but still reliable enough to
maintain a rhythmic ongoing activity.
In the neocortex, the up states are of longer duration (0.5--1 s)
and less frequent (0.3 Hz) than the ones reported here. Duration
and frequency of the oscillations depend on a constellation of
factors related with the synaptic interactions and intrinsic
properties of the cortical network. Therefore, different dynamics in different networks reflect their structural and functional
uniqueness. At this point, we have no precise explanation for all
the factors that give rise to the differences in frequency and
duration of oscillations in piriform network and neocortex. A
combined experimental and modeling approach is best to
analyze heuristically the network mechanisms underpinning
differences in emergent activity across different cortices, and
we have used that approach in the analysis of one particularly
striking difference between neocortex and the piriform cortex:
the speed of propagation of the up states along the cortex.
Speed of Propagation in Piriform Cortex versus
Neocortex In Vitro
The horizontal rate of propagation (across the same layer) of
oscillations in the piriform cortex in vitro was 114 mm/s, with
a distribution of values reaching speeds faster than 230 mm/s.
These values imply in general the participation of synaptic
connections and not only axonal conduction of which the
slowest value for unmyelinated axons in the PC is 0.2 m/s
(Hoffman and Haberly 1993). The speed of propagation in PC is
therefore 10 times faster than the one found in slices of
neocortex (10.9 mm/s; Sanchez-Vives and McCormick 2000).
Several of the known properties of the piriform cortex could
determine this difference, and we have explored them in
a cortical network model. The first one is the weaker inhibition
in the piriform network. Both experimentally and in computer
models, inhibition is known to slow down the propagation of
activity in the cortical network, whereas the lack of inhibition
accelerates it (Flint and Connors 1996; Sanchez-Vives and
McCormick 2000; Wu et al. 2001; Bazhenov et al. 2002; Golomb
and Ermentrout 2002; Compte et al. 2003). Lesser inhibition in
the piriform network as evidenced by very low GABA
transporter-1 immunoreactivity, near absence of GABAergic
cartridge endings on axonal initial segments (Ekstrand et al.
Cerebral Cortex May 2008, V 18 N 5 1189
2001), and a significant lesser inhibitory/excitatory neuronal
rate for calbindin-, calretinin-, and parvalbumin-positive neurons in the piriform network versus visual cortex of the ferret
(our own unpublished observations) is probably one contributing cause for the high propagation speeds. We confirmed this
quantitatively in a computational network model of the cortical
circuitry, indicating that indeed a decrease in inhibition
produced an augmented propagation speed (Supplementary
Fig. 3).
A second potential mechanism that may influence the rate of
propagation is the existence of long excitatory connections in
the PC that extend up to distances of 4 mm (Johnson et al.
2000). The lack of a columnar organization, the predominance
of horizontal connectivity, and the continuous distribution of
axonal boutons on the horizontal connections, rather than
contacts on restricted spots (Johnson et al. 2000), are also
factors that may contribute to increase propagation speed. In
our model simulations, doubling the footprint of horizontal
excitatory connections (leaving inhibitory connections unchanged) resulted in a several fold increase in propagation
speed (Supplementary Fig. 3). In the model, propagation
accelerates not only because excitation reaches farther but
also because this far-reaching excitation is not correspondingly
mitigated by far-reaching inhibition.
A third factor that can contribute to the fast propagation
speed in the piriform cortex is the more positive inhibitory
reversal potential in piriform cortex than in neocortex (Kapur
et al. 1997 and our unpublished observations). The computational model suggests that the reversal potential of GABAA
synapses could be a critical factor governing the propagation
speed of activity in a cortical module (Supplementary Fig. 3).
The speed of propagation of the oscillations that we have
described in this study does not differ markedly from the
propagation rates described for epileptiform discharges
(Hoffman and Haberly 1993; Demir et al. 2001) induced by
a variety of methods in vitro. This suggests that in the piriform
network, its lesser inhibition may play a weaker role in the
determination of the actual propagation speed of physiological
activity, whereas in neocortical studies, GABAergic blockade
increased the rate of propagation by one order of magnitude
(Sanchez-Vives and McCormick 2000). This poorer influence of
inhibition on propagation speed in the piriform cortex implies
the lack of one control mechanism over activity with respect to
the neocortex and may result in a higher tendency to generate
epileptic discharges.
In this heuristic exploration of the mechanisms of activity
propagation in piriform cortex, we have restricted our analysis
to the primary factors that are currently thought to subserve
the physiological differences between the piriform cortex and
neocortical circuitry. These are not, however, the only
mechanisms that influence the propagation of activity waves
in the cortical tissue. Indeed, it has been shown that other
elements that contribute markedly to the neuronal integration
time (spiking threshold, membrane time constant, synaptic
dynamics, etc.) also have a profound impact in propagation
speeds in cortical modules (Golomb and Amitai 1997; Pinto and
Ermentrout 2001). We want to emphasize at this point that we
have used a computational network model that reproduces
adequately the phenomenology of the visual cortex (Compte
et al. 2003), but none of our manipulations brings us to
a simulation that reflects all phenomenological aspects of slow
oscillatory activity in olfactory cortex. As we were concerned
1190 Rhythmic Spontaneous Activity in the Piriform Cortex
d
Sanchez-Vives et al.
with a heuristic exploration of the factors intervening in the
velocity of propagation, we did not attempt to simulate
faithfully either the more sparse involvement of neurons of
piriform cortex in this emerging oscillatory activity or the
duration and interval properties of these oscillations. A
thorough modeling of piriform cortex oscillatory activity will
be undertaken separately.
Epileptiform Activity and Slow Oscillations
Persistent epileptiform excitatory activity has been induced in
slices of piriform cortex by stimulation in low Mg2+ (Hoffman
and Haberly 1989, 1991), incubation in low chloride ACSF
(Demir et al. 1999b), disinhibition in bicuculline (De Curtis
et al. 1994; Halonen et al. 1994; Ebert et al. 2000; Demir et al.
2001; Rigas and Castro-Alamancos 2004), pilocarpine (Chen
et al. 2006), or previous kindling (McIntyre and Wong 1986;
Haberly and Sutula 1992; Hoffman and Haberly 1996). In our
preparation, application of bicuculline produced the appearance of epileptiform discharges characterized by paroxysmal
depolarizing shifts and repetitive synchronous discharges (Fig.
6), similar to those evoked by several maneuvers in neocortex
and piriform cortex. The transition from spontaneous rhythmic
activity to ictal-like discharges was progressive, with low
concentrations of bicuculline producing an increase in the
amplitude and a faster depolarization rate (Fig. 6) of the
recorded oscillations. Inhibition in the piriform network, thus,
shapes the characteristics of the slow oscillations and restrains
the network tendency to produce highly synchronized activity.
Thus, as it occurs in the neocortex, the balance between
excitation and inhibition is crucial for the generation of
physiological, in vivo--like rhythmic activity and to control the
tendency of the recurrent network to generate epileptiform
discharges. In the case of the piriform network, this balance
may be more delicate than in other areas, given the lower
proportion of inhibitory versus excitatory neurons, high
neuronal excitability, more positive GABAA reversal potential,
and long-range excitatory connections.
Funding
Ministerio de Educación y Ciencia (M.V.S.V.); Ministerio de
Educación y Ciencia (A.C.); Formación de Personal Investigador
fellowship (Ministerio de Educación y Ciencia) (V.F.D.); and
CSIC-Bancaja fellowship (R.R.).
Supplementary Material
Supplementary material
oxfordjournals.org/.
can
be
found
at:
http://www.cercor.
Notes
We want to thank A. Fairen, P. Berbel and S. Martinez for their generous
help on the anatomical reconstructions.
Address correspondence to Maria V. Sanchez-Vives, Instituto de
Neurociencias de Alicante, Universidad Miguel Hernandez—Consejo
Superior de Investigaciones Cientı́ficas (CSIC), Apartado 18, Sant Joan
d’Alacant, Alicante, Spain. Email: [email protected].
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