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The Journal of Neuroscience, December 15, 1999, 19(24):10727-10737
Control of Action Potential Timing by Intrinsic Subthreshold
Oscillations in Olfactory Bulb Output Neurons
David
Desmaisons,
Jean-Didier
Vincent, and
Pierre-Marie
Lledo
Centre National de la Recherche Scientifique, Institut Alfred
Fessard, 91198 Gif-sur-Yvette Cedex, France
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ABSTRACT |
Rhythmic patterns of neuronal activity have been found at multiple
levels of various sensory systems. In the olfactory bulb or the
antennal lobe, oscillatory activity exhibits a broad range of
frequencies and has been proposed to encode sensory information. However, the neural mechanisms underlying these oscillations are unknown. Bulbar oscillations might be an emergent network property arising from neuronal interactions and/or resulting from intrinsic oscillations in individual neurons. Here we show that mitral cells (output neurons of the olfactory bulb) display subthreshold
oscillations of their membrane potential. These oscillations are
mediated by tetrodotoxin-sensitive sodium currents and range in
frequency from 10 to 50 Hz as a function of resting membrane potential. Because the voltage dependency of oscillation frequency was found to be
similar to that for action potential generation, we studied how
subthreshold oscillations could influence the timing of action potentials elicited by synaptic inputs. Indeed, we found that subthreshold oscillatory activity can trigger the precise occurrence of
action potentials generated in response to EPSPs. Furthermore, IPSPs were found to set the phase of subthreshold oscillations and can lead to "rebound" spikes with a constant latency.
Because intrinsic oscillations of membrane potential enable very
precise temporal control of neuronal firing, we propose that these
oscillations provide an effective means to synchronize mitral cell
subpopulations during the processing of olfactory information.
Key words:
mitral cells; sodium current; synchronization; timing
device; inhibitory interneurons; olfactory processing
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INTRODUCTION |
Determining how neurons transform
synaptic inputs into spike trains is a necessary step toward
understanding information processing in sensory systems (Rieke et al.,
1997 ). The key question is what temporal pattern of spike trains are
detected and integrated by postsynaptic neurons. One might consider
that a sequence of action potentials contains information either based
on the average number of spikes per unit time (rate coding) or,
alternatively by the precise timing of individual action potentials
(temporal coding): a slow time-scale integration is thought to underlie
rate coding mechanisms (Parker and Newsome, 1998), whereas fast
time-scale integration is thought to involve coincidence detection in
which the precise timing of spikes carries information (for review, see
König et al., 1996). Of course, the dichotomy between rate and
temporal coding may not be so distinct because the brain might use a
combination of these coding systems (Riehle et al., 1997).
Synaptic integration and transformation into spikes depends on
voltage-gated ionic conductances that influence both the resting membrane potential and the firing properties of neurons (Hille, 1992).
We now know that ionic conductances provide the basis for numerous
nonlinear operations, and thus endow neurons with much greater
information processing capacities than was previously thought (Spencer
and Kandel, 1961; Stuart and Sakmann, 1995; Haag and Borst, 1996). This
nonlinearity allows neurons to integrate synaptic events in a very
precise temporal fashion (Larkum et al., 1999), with a temporal
fidelity on the order of milliseconds. Voltage-gated conductances can
also control the timing of action potentials through the generation of
membrane potential oscillations (for review, see Llinás, 1988).
Indeed, the interaction between these oscillations and synaptic inputs
can provide an effective means to synchronize neurons in an oscillatory
assembly (for review, see Connors and Amitai, 1997; Ritz and Sejnowski,
1997). This phenomenon has been reported in various brain structures
such as the neocortex (Silva et al., 1991), hippocampus (Cobb et al., 1995), and thalamus (von Krosigk et al., 1993) and may be essential for
encoding sensory information (Gray, 1994).
The present study focuses on membrane potential oscillations in output
neurons of the main olfactory bulb (mitral cells) and the temporal
relationship between these oscillations and action potentials. Temporal
coding has been proposed to play an important role in olfactory
processing (Tank et al., 1994; Laurent, 1996a). For example,
electroencephalogram (EEG) and local field potential (LFP) recordings
in the olfactory bulb have revealed network oscillations in a wide
range of animals: Limax (Gelperin and Tank, 1990), locust (Laurent et al., 1996), fish (Satou and Ueda, 1978), frog (Delaney and
Hall, 1996), and insectivore (Adrian, 1942). This oscillatory activity
results from neuronal synchronization (Laurent and Davidowitz, 1994;
for review, see Laurent, 1996a,b). In the honey bee antennal lobe (the
analog of mammalian olfactory bulb), data suggests that neuronal
synchronization may be necessary for neural processing because
pharmacologically induced desynchronization of output neurons results
in olfactory discrimination deficits (Stopfer et al., 1997).
Here we report that intrinsic oscillations act as a timing device for
the integration of postsynaptic potentials into spikes. We propose that
interactions between the intrinsic properties of mitral cells and their
synaptic inputs can efficiently synchronize the activity of multiple
mitral cells. This mechanism may allow the representation of odors by
temporally distributed ensembles of coherently firing mitral cells.
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MATERIALS AND METHODS |
Slice preparation. Experiments were performed on
olfactory bulb slices from 4- to 6-week-old Wistar rats. Animals were
anesthetized by intraperitoneal injection of pentobarbital (100 µl)
and then killed by decapitation. The olfactory bulbs were rapidly
removed and immediately placed in a standard 4°C artificial
CSF (ACSF) solution in which NaCl was replaced with sucrose (the
osmolarity was maintained to 310 mOsm). The standard ACSF contained (in
mM): 124 NaCl, 3 KCl (unless otherwise indicated), 2 CaCl2, 1.3 MgCl2, 25 NaHCO3, 1.25 NaH2PO4, and 10 D-glucose, pH 7.3, when bubbled with 95%
O2 and 5%
CO2. Horizontal slices (400 µm) were cut on a
vibrating microslicer (Vibratome 1000; Ted Pella, St. Louis, MO),
incubated in normal oxygenated ACSF at 32°C for ~60 min, and then
maintained at room temperature (20-22°C) except when indicated.
Individual slices were then transferred to a submerged-slice recording
chamber where they were superfused with oxygenated ACSF at a rate of
1-2 ml/min. The horizontal plane was chosen because its allows the
best orientation for preserving mitral cells with intact primary and
secondary dendrites.
Electrophysiological recordings. Extracellular recordings
were performed using glass microelectrodes containing 1 M
NaCl and amplified using a DAM-80 (World Precision Instruments,
Hertfordshire, UK) differential amplifier. Intracellular recordings
were performed with borosilicate glass microelectrodes (Clark
Electromedical Instruments, Reading, UK) pulled with the Flaming-Brown
microelectrode puller. In some experiments, intracellular recording
electrodes were back-filled with 2% biocytin in 2 M
potassium acetate for morphological confirmation. Intracellular
current-clamp recordings (electrode resistances ranged between 120 and
140 M ) were performed with an Axoclamp-2A (Axon Instruments, Foster
City, CA) amplifier, and, during experiments, the bridge balance was
continuously monitored. To evoke synaptic responses, stimuli (100 µsec duration) were delivered through fine bipolar tungsten
electrodes placed in the olfactory nerve layer (ONL), in the external
plexiform layer (EPL), or alternately in both layers. Membrane
potentials were filtered (3 kHz low-pass), collected on-line via the
Digidata interface (Axon Instruments) on an IBM-compatible computer,
and stored on videotape for later analysis.
Neurons were identified by antidromic stimulation of the lateral
olfactory tract, orthodromic stimulation of the olfactory nerve, by
their location in the mitral cell layer, and occasionally by biocytin
injection. Only those having stable membrane potentials more negative
than  50 mV, input resistances >80 M, and action potentials
overshooting 0 mV were retained for analysis. At the end of each
experiment, possible changes in electrode tip potential were controlled
by measuring the DC offset of the electrode in the bathing medium, and
the value of the measured membrane potential was corrected accordingly.
Changes in electrode tip potential were usually <3 mV. The current
injected to produce oscillations of the membrane potential was a sine
wave:
where T is the period (50 msec for all the experiments), and
Imax the maximal current that was adjusted
on-line. The current injected to mimic EPSP was given by the equation:
where  r is the rise time,
d is the decay time, and
Imax is the maximum of the current, all three
values of which were controlled on-line.
Solutions and drugs. Pharmacologically active substances
were applied in the bathing medium using a gravity-driven perfusion system. To block EPSPs, mitral cells were recorded in the presence of a
nonspecific ionotropic glutamate receptor antagonist, kynurenic acid
(5-10 mM) or in the presence of the NMDA receptor
antagonist D,L-2-amino-5-phosphonopentanoic acid
(D,L-APV; 100 µM) and the AMPA receptor
antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX; 10 µM). To block IPSPs mediated by the activation of
GABAA receptors, cells were bathed with either a
GABAA receptor antagonist (20-40
µM bicuculline or 20 µM SR 95531) or a
chloride channel blocker (100 µM picrotoxin). CNQX and SR
95531 were obtained from Research Biochemicals International (Natick,
MA), D,L-APV from Tocris (Illkirch, France), and all other
drugs and salts from Sigma (Strasbourg, France).
Data analysis. Both on- and off-line analyses were performed
with Acquis1 software (Gérard Sadoc, Centre National de la
Recherche Scientifique-Agence Nationale pour la Valorisation de la
Recherche, Paris, France). Mitral cell membrane potential was taken as
the average potential during a period without spikes (but which
included subthreshold oscillations). Because fast Fourier
transformations (FFTs) and autocorrelation of the membrane voltage gave
similar results, we used FFT to analyze the rhythmic nature of the
subthreshold oscillations and to calculate their dominant frequency.
Oscillations were triggered by detecting local maxima of the membrane
potential. To characterize the relationship between oscillation phases
and spike latency, we arbitrarily attributed the phase value 0 to stimulations performed at a time corresponding to the peak of an
oscillation and giving rise to a spike with a latency longer than 25 msec. Similarly, we attributed the phase value 2 to a stimulation
that triggers spikes with a latency longer than 25 msec. Such an
operation does not bias the results because analyzing data without
these two values (0 and 2) gave similar results (i.e., dependency
between oscillation phase and latency, p < 0.005; slopes of linear regressions, 6.4 vs 8.7 msec; and
y-intercept values, 35.7 vs 40.2 msec, respectively).
Average cross-correlations (ACC) were used to evaluate the precise
timing of subthreshold oscillations after a triggering event. These
were calculated by averaging the cross-correlations obtained from all
pairs of traces measured in different trials, and were expressed
according to the formula:
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where Cc(i,j) is the cross-correlation between the
ith and the
jth trial of the triggering
event (maximal value normalized to 1), and n the number of
trials. Spontaneous IPSPs were detected using a negative threshold on
the slope of membrane voltage and manually accepted or refused. The
decay time constant () of synaptic events was calculated by fitting
the average membrane potential (at least ten trials) with an
exponential function:
All the peristimulus-time, autocorrelation, and
cross-correlation histograms were computed using methods previously
described (Perkel et al., 1967). Unless otherwise indicated, the bin
width of all histograms is 10 msec. Statistical significance of
differences between means was assessed using a Student's t
test. Kolmogorov-Smirnov statistics were also used to determine
whether there were statistically significant changes between different
groups, and the level of significance was set at p < 0.05. Unless otherwise indicated, data are expressed as mean ± SEM.
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RESULTS |
During the course of this study, intracellular recordings were
performed on 85 mitral cells (see Fig.
1A for the experimental setup and Fig. 1B for mitral cell morphology). With
pipettes containing a K+-based
intracellular solution, these neurons had a mean resting membrane
potential of 62.7 ± 1.7 mV (range, 71 to 51 mV;
n = 12), a mean spike width of 1.98 ± 0.10 msec
when measured at half-maximum amplitude (n = 19), a
mean time constant of 19 ± 3 msec (range, 7-40 msec;
n = 19), and a mean membrane input resistance of
128 ± 10 M (range, 88-280 M; n = 19).
Successfully impaled neurons could be held up to 7 hr without any
change in these membrane properties.
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Figure 1.
The horizontal rat olfactory bulb slice
preparation. A, Experimental arrangement for bipolar
stimulating electrodes (S1 and S2) and
the recording electrode (REC) in a schematic
representation of the main olfactory bulb. ONL,
Olfactory nerve layer; GL, glomerular layer;
EPL, external plexiform layer; MCL,
mitral cell body layer; GCL, granule cell layer.
B, Camera lucida drawing of a mitral cell labeled
intracellularly with biocytin.
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The frequency of subthreshold oscillations
is voltage-dependent
In all mitral cells, spontaneous subthreshold oscillations in
membrane potential were observed at resting membrane potential (Fig.
2A,B). These
oscillations were not dependent on synaptic input because they
persisted in the presence of the selective GABAA antagonist bicuculline (40 µM; n = 16), the broad-spectrum ionotropic glutamatergic antagonist kynurenate (10 mM; n = 7), or a mixture of both
(n = 6). They were observed at a wide spectrum of
membrane potentials, and their amplitude ranged from 2.1 to 5.7 mV (a
mean of 3.7 ± 0.1 mV; n = 51). Figure 2,
A and B displays the voltage traces of a mitral
cell that was depolarized by steady current injections of increasing
amplitude. Subthreshold oscillations first appeared in the absence of
action potentials (approximately 67 mV). After further
depolarization, clear rhythmic deflections of the membrane potential
were detected whose amplitude was sufficient to reach the action
potential threshold (65 mV). The firing pattern at this potential was
characteristic of mitral cells recorded in slice preparations: clusters
of spikes interspersed with long period of subthreshold oscillations
(Fig. 2A, top trace; see also Chen and Shepherd,
1997, their Fig. 2B). To characterize the voltage dependency of these oscillations, we used FFTs and autocorrelations to
calculate their frequencies. As shown in Figure 2, B and
C, the oscillation frequency was found to be ~10 Hz near
their threshold and rose to 20 Hz at the spike threshold. Further
depolarization of the cell progressively increased the frequency of
subthreshold oscillations up to 40 Hz at 59 mV (Fig.
2B, top graphs). All cells analyzed showed
similar voltage dependency (n = 5).
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Figure 2.
Subthreshold membrane potential oscillations are
voltage-dependent. A, Increasing depolarization by
injection of a constant current triggers clustered action potentials
interspersed by membrane potential oscillations (top two
traces). Slow oscillations are observed at more hyperpolarized
membrane potentials (bottom trace). B,
Power spectrum (arbitrary units) reveals the dominant frequency of
these oscillations (middle panel), whereas the
autocorrelation indicates their rhythmic nature (right
panel). Left, Expanded view of the
oscillations at three different potentials used for the
corresponding graphs (same cell as in A).
C, Voltage dependency of oscillation frequency
(filled symbols) and spike frequency (open
symbols) from the same cell. Symbols represent the mean, and
error bars indicate SEM. The spike frequency is calculated as the
inverse of the mean of the first interspike interval within a
cluster.
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We found that across potentials, the frequency of oscillations and the
instantaneous spike frequency (measured as the inverse of the mean of
the first interval interspike within a cluster) were highly correlated.
As illustrated in Figure 2C, the frequency of subthreshold
oscillations and spikes exhibited almost parallel increases as a
function of membrane potential (n = 4 of 4). This close
relationship was further demonstrated by a linear regression analysis
of oscillation frequency plotted against spike frequency [Fig.
3A; mean slope linear
regression (k); 1.05 ± 0.14 (n = 4), with all cells presenting r > 0.97].
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Figure 3.
Relationship between subthreshold oscillations and
spikes. A, The average oscillation frequency is plotted
as a function of the mean spike frequency with a linear regression
(k = 0.997). Each symbol represents the mean ± SEM. B, Temporal correlation between spikes and
oscillations. a, Twenty superimposed spike-triggered
traces and corresponding average of traces with one spike.
b, Phase relationship between spikes and subthreshold
potential oscillations. Traces were triggered on the last (trace
1) or on the penultimate oscillation (trace
2) before spike emission to establish a probability
histogram of spike emission as a function of the oscillation phase.
Results were fitted with Gaussian functions. Note the tendency for the
cell to fire with a small phase advance. Inset,
Autocorrelation of the subthreshold oscillations preceding a spike
(continuous line), and autocorellogram of spikes
(histogram) for the same potential, illustrating similar
frequencies for the two events (spikes are clipped for clarity).
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To further characterize the relationship between subthreshold
oscillations and firing activity, the temporal relationship between
spikes and oscillations was investigated. As previously described in
other preparations (Llinás et al., 1991; Gutfreund et al., 1995),
we found that action potentials were synchronized with the peak of the
subthreshold oscillations (n = 5 of 5). Figure 3Ba shows spike-triggered voltage traces illustrating that
spikes and oscillations are phase-locked. The rising slopes of these two events are superimposable, and the spike initiation point is always
during the rising phase. Similarly, calculating the probability that
the cell will spike after one oscillation clearly shows that mitral
cell spiking is triggered by these oscillations (Fig. 3Bb).
From the probability histograms for spiking shown in Figure
3Bb, it can be seen that the generation of a spike coincides with the peak of the following oscillation with, however, a small phase
advance. This anticipation can also be seen by close inspection of the
superimposed voltage traces displayed in Figure 3Ba.
Ionic conductance underlying the subthreshold oscillations
We next investigated the potential role of ionic conductances in
the generation of membrane potential oscillations (Fig.
4). First, the broad-spectrum sodium
channel blocker tetrodotoxin (TTX; 1 µM), completely
blocked these membrane potential oscillations (Fig.
4A; n = 4 of 4). Then, to determine
whether calcium also could be involved, it was removed from the
extracellular medium and substituted with cobalt (2 mM). We were not able to detect any changes in
either the frequency or the amplitude of the oscillations (n = 4 of 4), even after a perfusion time of >12 min
(Fig. 4B), when synaptic transmission was blocked
(data not shown). Finally, the potential role of potassium conductances
was first addressed with the potassium channel blocker
tetraethylammonium (TEA). When 10 mM TEA was
added to the perfusate, input membrane resistances and firing activity
changed dramatically (data not shown), so evaluating their
participation in oscillatory activity was meaningless. We thus decided
to reduce the external concentration of potassium (from 3 to 2 mM) to modify all potassium currents. Lowering
extracellular potassium affects AHP amplitude (5 mV more
hyperpolarized; data not shown), but no change in subthreshold
oscillations amplitude or frequency was observed (Fig. 4C;
n = 4 of 4). From these pharmacological studies, it can
be concluded that subthreshold oscillations may result from the
activation of a voltage-gated sodium-permeable channel, but neither
voltage-gated calcium nor potassium currents seem to be involved.
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Figure 4.
Pharmacology of subthreshold oscillations. Control
traces recorded in standard medium (left) and in
external medium containing either 1 µM tetrodotoxin
(A), cobalt substituted for calcium
(B), or with a lower KCl concentration
(C), after a perfusion time, indicated in
parentheses. Right panels, Corresponding
autocorrelations. Note that oscillations were only abolished after bath
application of TTX, whereas calcium withdrawal or lowering of external
potassium affected neither their frequency nor their amplitude
(Vhold = 62 mV for all traces).
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Spontaneous IPSPs reset the phase of subthreshold oscillations
In some neurons (18 of 85 neurons), we observed spontaneous
synaptic inhibitory events (Fig.
5A) that were completely
antagonized by bicuculline (20-40 µM) or SR
95531 (20 µM). The mean amplitude of these
spontaneous synaptic potentials was 3.2 ± 0.4 mV
(n = 7), and they were easily distinguished from
oscillations by their kinetics (rise time, 5.1 ± 0.5 msec vs
22.4 ± 3.2 msec for IPSPs and oscillations, respectively;
n = 7). Because it has been reported that GABAergic
events can synchronize neuronal activity (Cobb et al., 1995; MacLeod
and Laurent, 1996), the temporal relationship between spontaneous IPSPs
and membrane potential oscillations was studied. As illustrated in
Figure 5, spontaneous IPSPs (n = 7) could reset the
phase of spontaneous oscillations without affecting their amplitude
(Fig. 5A) or frequency (Fig. 5C).
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Figure 5.
IPSPs or hyperpolarizing pulses reset the phase of
the oscillations. A, Intrinsic membrane potential
oscillations recorded at the same potential in the absence
(bottom trace) or the presence (top trace,
asterisk) of a spontaneous IPSP. Note that the IPSP only resets
the phase of the oscillations without affecting its frequency or
amplitude. B, Mitral cell recorded in the presence of
bicuculline (40 µM) and kynurenate (10 mM). A
hyperpolarizing current pulse (arrowhead) is injected to
mimic spontaneous IPSPs. From top to bottom, An
individual voltage trace showing the triggering of oscillations by a
current pulse; six superimposed traces; average of 20 trials compared
to the average of traces without pulse. C, The
hyperpolarizing pulse reset the phase of the oscillations without
affecting their frequency. Left, Corresponding averaged
autocorrelations without (Spont.), 5 msec (Stim + 5 msec), or 150 msec after the hyperpolarizing pulse
(Stim + 150 msec). No change in frequency can be
detected. Right, Averaged cross-correlation obtained
from voltage recordings in the same conditions. Results from this
correlation can be taken as an index of phase coherence. Note that the
subthreshold oscillations are only rephased during 150 msec after the
pulse.
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Interestingly, this effect induced by spontaneous IPSPs could be
mimicked by injecting hyperpolarizing current pulses (<4 mV) of short
duration (10 msec). Hence, as described for spontaneous IPSPs, these
current pulses were able to reset the oscillation phase with no effect
on the frequency or amplitude (n = 13; Fig. 5B), indicating that the reset only results from the
IPSP-associated hyperpolarization interacting with intrinsic
membrane conductances. To further quantify the effect of these current
pulses, we performed autocorrelation analysis (Fig. 5C).
First, the averaged autocorrelation of membrane potential measured
either, (1) without the pulse, (2) 5 msec after the pulse, and (3) 150 msec after the pulse, demonstrates that the oscillation periods was not
modified by the current pulse (45 ± 4.8 msec in control
conditions and 50 ± 5.1 msec after the pulse; n = 9; p > 0.05). We then used averaged cross-correlations
(ACC) as an index of phase coherence. Indeed, ACC of phased signals
resulted in a periodic function similar to the autocorrelation, whereas
nonphased signals gave a flat ACC. We found that ACC and
autocorrelation were only similar when calculated immediately after the
hyperpolarizing pulse, but not 150 msec later, nor without the pulse
(n = 10; Fig. 5C). Hence, the ACC value
corresponding to time 0 is significantly different between sweeps with
and without pulse (0.79 ± 0.05 vs 0.06 ± 0.03, respectively; p < 0.0001), indicating a resetting of
the oscillation phase after the pulse. Moreover, these ACC showed
pronounced multiple peaks that reflect a phasing lasting more than one
cycle (corresponding period, 48 ± 5 msec). Together these results
show that small hyperpolarizations can reset the oscillation phase for
a period of three cycles. This finding is consistent with the average
potential traces collected during the IPSP-mimicking current pulse
protocol (Fig. 5B, bottom traces).
Spontaneous IPSPs control the timing of mitral cell firing
For membrane potentials more depolarized than 65 mV, the same
hyperpolarizing pulses generated either a single "rebound" action
potential or a cluster of spikes (n = 21; Fig.
6A). Interestingly, evoked or spontaneous IPSPs are also able to produce rebound spikes (n = 11; Fig. 6B). Because IPSP
amplitudes fluctuated substantially, variable delays in spike
initiation could be expected. For example, in the cerebellum, the delay
for spike occurrence is proportional to IPSP amplitude (Häusser
and Clark, 1997). However, recordings from mitral cells (Fig.
6C) showed no correlation between IPSP slope and spike
latency (p > 0.3; n = 6). This
result demonstrates that even with a high variability of IPSP amplitude
(Fig. 6B,C), inhibitory events were still effective
in generating action potentials within a narrow time window. It is
noteworthy that this temporal window corresponds to the depolarizing
overshoot of subthreshold oscillations after IPSPs, as depicted in
Figure 5B (bottom trace, arrow). We further
studied the relationship between the spike delay and the holding
membrane potential. Figure 7A
shows that the spike latency was constant and independent of the
membrane potential. This observation held true for membrane potentials ranging from 62 to 52 mV (p > 0.5;
n = 5). Finally, the distribution of spike delays
induced either by spontaneous IPSPs or mimicked IPSPs was investigated
in the recorded mitral cell population. Interestingly, spike delays
generated either by spontaneous GABAergic events (31.1 ± 0.6 msec; n = 11) or current-induced hyperpolarizations (29.2 ± 0.4 msec; n = 21) were similar (Fig.
7B; p > 0.05). It is also noteworthy that
the temporal control for the generation of action potentials was highly
uniform because >85% of mitral cells generated an action potential
within a time window of 10 msec (the distribution of delays ranged from
to 33 to 43 msec).
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Figure 6.
Spontaneous IPSPs or hyperpolarizing current
pulses can generate rebound spikes. A, A hyperpolarizing
current pulse (arrowhead) can elicit a rebound spike.
Left, Superimposed sweeps (n = 8)
illustrating the phasing of spiking after the current pulse.
Right, Associated PSTH from all trials
(n = 16). B, Spontaneous IPSPs
(asterisk) can also elicit a rebound spike. Five
superimposed traces triggered by spontaneous IPSPs
(left), and the corresponding PSTH obtained from 23 sweeps (right) showing a marked peak at 30 msec after
the spontaneous IPSP. C, Relationship between the IPSP
slope and the spike delay. Note the absence of any correlation
(r = 0.38). Inset, Histogram of
spike latency.
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Figure 7.
The rebound spike delay is independent from the
membrane potential and does not depend on how it is generated.
A, Mean ± SD of the delay between the
hyperpolarizing pulse and the spike initiation as a function of the
membrane potential for the same cell. Note the absence of correlation
(r = 0.08; p > 0.8).
B, Cumulative probability plots for the spike latency
after hyperpolarizing pulses (filled symbols;
n = 21 cells) or IPSPs (open
symbols; n = 8 cells). The difference
between these two populations is not significant
(p > 0.05). Inset shows two
superimposed voltage traces from the same cell illustrating the rebound
spike after a current pulse or a spontaneous IPSP.
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Subthreshold oscillations are a timing device for
synaptic integration
Having found that IPSPs could synchronize the subthreshold
oscillations, we further tested whether the temporal integration of
EPSPs into spikes was dependent on this oscillatory activity. Excitatory synaptic responses were evoked by extracellular stimuli applied in the EPL (see Fig. 1 for the location of the stimulating electrode S1). This stimulation induced short-latency (<3
msec), small amplitude (2.8 ± 0.5 mV), and long duration
(half-time decay of 61.8 ± 10.7 msec) EPSPs (Fig.
8Ac; n = 12). They were mediated by glutamate receptors because a mixture of
D,L-APV (100 µM) and CNQX
(10 µM; n = 3), or kynurenate
(10 mM; n = 4) completely blocked these responses. Moreover, bath application of APV alone revealed an
AMPA component with a shorter duration (46.1 ± 14.6 msec;
n = 5).
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Figure 8.
Subthreshold membrane potential oscillations and
EPSP integration. A, Voltage traces illustrating the
experimental protocol. Aa, A trace showing spontaneous
membrane potential oscillations and (Ab) sinusoidal
current-induced membrane oscillations. Ac, Averaged
evoked EPSP (10 trials) induced by stimulation
(arrowhead) in the EPL. B, Membrane
oscillations act as a timing device for EPSP integration.
Ba, Traces in which the EPL stimulation occurred at the
peak (considered as  = 0) or the trough ( = ) of the
oscillations. Bb, Superimposition of four sweeps
differing by /2. Note the constant timing for spike initiation
(spikes are truncated). Inset, Expanded view of these
traces illustrating the initial EPSPs. Bc, Relationship
between the delay for spike initiation and the phase of the stimulation
for this cell (open circles) and averaged data ± SEM (filled circles) plotted with a linear
regression (r = 0.91; black line).
C, Summary graph (n = 5 cells) in
which the averaged delay is plotted as a function of the oscillations
phase, with corresponding SD (filled
circles). Dashed line, Delay corresponding to a
spike occurring at the peak of one oscillation. Open
circle, Spike latency for identical holding potentials without
induced oscillations (DC current). Note that for phases
exceeding the spike latency variability (quantified by the SD) is
smaller in the presence of oscillations than in their absence (see
Materials and Methods for more details).
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To test the ability of membrane oscillations to synchronize firing
activity, we induced oscillations by sinusoidal current injection (mean
amplitude, 2.7 ± 0.2 mV) near spike threshold (62.5 ± 2.5 mV) to mimic spontaneous oscillations (Fig. 8Ab). Current-induced oscillations were chosen over endogenous oscillatory activity because they were easier to control. Using this protocol, the
relationship between the timing of the EPL stimulation and the spiking
activity was studied (Fig. 8B). Although synaptic potentials were triggered at different phases of the sinusoidal waveform, we found that action potentials induced by EPL stimulation (n = 5) systematically occurred in a very narrow time
window corresponding to the peak of the next oscillation (Fig.
8Bb,Bc). This time window corresponds to the peak of
the following oscillations with a 10 msec jitter (SD, 10.8 msec;
n = 5). As a consequence, the delay for generating
action potentials was found to depend on the EPL stimulation phase
(Fig. 8C). When the delay of spike emission was plotted
against the stimulation phase (Fig. 8Bc,8C), a clear relationship was found (p < 0.0001;
n = 5). The delay was longer for stimulations occurring
during the trough of the oscillation ( = /2) and shorter for
stimulations near the peak of the oscillation ( = 3/2, for
example). To further characterize the temporal relationship between
EPSPs and spiking activity, the relationship between the EPSP slope and
the stimulation phase was determined after the sine wave responses were
subtracted. In all tested cells (n = 3), no dependency
was found between these two parameters (p > 0.5) or between membrane potentials and EPSP slopes
(p > 0.9). These observations suggest that
voltage-dependent conductances do not participate in the phasing of spikes.
Stimulation of primary afferent axons triggers
subthreshold oscillations
We tried to elucidate a functional role of subthreshold
oscillations by electrically stimulating primary afferent axons (see Fig. 1 for the location of the stimulating electrode S2). A
single stimulation of the olfactory nerve (n = 10)
evoked large EPSPs (9.1 ± 1.2 mV) of long duration (mean decay
time, 272 ± 38 msec). These synaptic responses were mediated by
glutamatergic receptors because they were virtually eliminated by bath
application of 10 mM kynurenate
(n = 7; Fig.
9Aa). To determine the
participation of NMDA and non-NMDA receptors, the amplitude and time
decay of synaptic responses were measured in the absence and the
presence of APV (100 µM) (Fig. 9Aa;
n = 7). The results from 10 experiments performed in
standard external solution are summarized in Figure 9Ab.
These experiments revealed that the slow component to the EPSP decay
was abolished by APV (mean decay time in APV, 108 ± 34 msec),
whereas the peak amplitude was relatively unaffected (6.6 ± 1 mV). Thus, excitatory transmission between receptor neurons and mitral
cells is mediated by both NMDA and non-NMDA receptors, a finding that
is consistent with previous reports (Ennis et al., 1996; Keller et al.,
1998).
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Figure 9.
Electrical membrane properties of mitral cells in
response to olfactory nerve stimulation. A, Pharmacology
of olfactory nerve (O.N.)-induced EPSPs.
a, Averaged traces recorded in standard medium
(STD), in presence of 100 µM APV
(APV), and 10 mM kynurenate
(Kyn). b, Histograms showing the effect
of APV on O.N.-evoked synaptic responses. Note that APV had small
effects on EPSP amplitude (left) but consistently
reduced the decay time (right). B,
Subthreshold oscillations occur after O.N. stimulation without current
injection. Ba, Two representative synaptic responses
evoked by O.N. stimulations (arrowheads) illustrate the
coexistence of action potentials with subthreshold oscillatory
activity. Insets zoom on the induced subthreshold
oscillations (left) and a rebound spike triggered by an
IPSP (noted by an asterisk on right);
Calibration: 25 msec, 2.5 mV. Bb, Injection of a somatic
current recorded in presence of kynurenate (10 mM) with
bicuculline (40 µM) mimicking O.N.-evoked response. Note
the subthreshold oscillation (inset, same scale as in
Ba) and the rebound spike induced by a short
hyperpolarization current pulse (open arrowhead).
|
|
At resting membrane potentials (62.7 ± 1.7 mV), olfactory nerve
stimulation produced a long depolarization triggering action potentials
(n = 14; Fig. 9B). Consequently, mitral
cells fired in a rhythmic manner with a clustered spike discharge
interspersed with subthreshold membrane potential oscillations (Fig.
9Ba). To confirm that these oscillations were truly
intrinsic, an EPSC-like current was injected in the soma of mitral
cells recorded in the presence of kynurenate (10 mM) and bicuculline (40 µM). Figure 9Bb shows that simulated
olfactory nerve impulses give rise to similar clusters of spikes
separated by subthreshold oscillations. Identical results were obtained
from four different experiments.
The long-lasting response to olfactory nerve stimulation was confirmed
with extracellular recordings (data not shown). Most mitral cells were
silent at rest (n = 10 of 15) or fired at a low rate
(range, 0.6-9 Hz; n = 5 of 15), and they responded to olfactory nerve stimulation with a prolonged period of spiking (475 ± 50 msec; n = 10) compatible with the
generation of steady-state depolarizations and subthreshold
oscillations shown in our intracellular recordings. Finally, we tested
whether IPSPs triggered by olfactory nerve stimulation could reset
action potentials. GABAergic events elicited during olfactory nerve
stimulation (n = 4 cells) were indeed followed by
action potentials (Fig. 9Ba; the IPSP is indicated by an
asterisk). This effect was also reproduced in neurons bathed with
kynurenate and bicuculline in which olfactory nerve-induced depolarizations were mimicked by somatic current injection. In these
conditions, rebound spikes were similarly triggered by mimicked IPSPs (Fig. 9Bb; the simulated IPSP is noted by an open
arrowhead). Finally, at 32°C, olfactory nerve stimulation evoked
intracellularly recorded mitral cell responses similar to those
recorded at room temperature, including subthreshold oscillations,
IPSPs, and rebound spikes (data not shown; n = 5).
| |
DISCUSSION |
The present study focused on subthreshold oscillations of mitral
cells and their functional implications. We show that subthreshold oscillations, ranging from 10 to 50 Hz, are generated exclusively from
a TTX-sensitive ionic conductance. The functional role of such rhythmic
activity was evaluated by analyzing how mitral cells integrate synaptic
potentials into action potentials. We have found that these
oscillations were crucial for spike timing as well as for filtering
EPSPs. We have also shown how these oscillations and spikes generated
in mitral cells are temporally controlled by GABAergic synaptic inputs.
Finally, we report that stimulation of the olfactory nerve, which
generates excitatory inputs onto mitral cells, was sufficient to
trigger stable endogenous subthreshold oscillations. We therefore
propose that subthreshold oscillatory activity of the membrane
potential may precisely control the timing of spiking activity and
thereby provide a mechanism by which the outputs of a mitral cell
population can be synchronized.
Subthreshold oscillations of mitral cell membrane potentials
Many neurons from the CNS exhibit intrinsic subthreshold
fluctuations in membrane potential (Alonso and Llinás,
1989; Llinás et al., 1991; Pedroarena and Llinás, 1997).
However, most of these oscillations are calcium-dependent, and few
studies have described TTX-sensitive calcium-independent oscillations
(Llinás, 1988). The fact that mitral cell oscillations were
affected neither by changes in extracellular potassium nor by
extracellular cobalt ions suggests that potassium and calcium
conductances are not critical to the generation of oscillations.
Low voltage-activated calcium conductances have been described in
olfactory bulb output neurons (Wang et al., 1996). Although this
current might induce either subthreshold oscillations or the rhythmic
generation of calcium spikes in thalamic (Pedroarena and Llinás,
1997) and neocortical (Amitai, 1994) neurons, they do not seem to be
directly involved in the generation of subthreshold oscillations of
mitral cells for two reasons. First, in experiments in which calcium
was removed from the extracellular bath there was no modification of
either the amplitude or the frequency of oscillations. Second, the
hyperpolarizing current pulse protocol used to reset oscillations (Fig.
5B,C) was also insensitive to calcium removal. We propose
that such oscillations are generated by a TTX-sensitive ionic
conductance that operates within a range of voltages above and below
spike threshold. Interestingly, the biophysical properties of the
noninactivating sodium current (INaP) described in many excitable cells make it a suitable candidate for
generating these intrinsic oscillations (for review, see Crill, 1996).
In particular, its activation threshold in certain neurons was found to
be approximately 70 mV (Parri and Crunelli, 1998), a value close to
the activation of intrinsic mitral cell oscillations. Further studies
of INaP in mitral cells will be
important to understand its potential role in signal integration.
Synchronization by recurrent inhibition requires
subthreshold oscillations
It has been emphasized that recurrent inhibition may generate
synchronized activity in many neural networks (for review, see Jefferys
et al., 1996). In the olfactory bulb, models have proposed that
GABAergic inhibition exerted by inhibitory interneurons (i.e., granule
cells) participates in the generation of synchronized network
oscillations (for review, see Rall and Shepherd, 1968; Shepherd, 1972;
Freeman, 1975). These neurons release GABA at dendrodendritic
reciprocal synapses, each of which consist of an excitatory synapse
directly adjacent to an inhibitory granule-to-mitral cell synapse
(Price and Powell, 1970). Interestingly, this reciprocal inhibitory
synapse makes the olfactory bulb an exception in the CNS and allows for
fast recurrent inhibition. Our study demonstrates that interactions
between GABAergic inhibition and intrinsic membrane properties is a
very efficient way to precisely time spike generation. As shown in
Figure 5A, spontaneous IPSPs were sufficient to reset the
phase of endogenous subthreshold oscillations. A single granule cell
which may be connected to several mitral cells could therefore effectively synchronize their intrinsic oscillations (Fig.
10A). This
possibility is supported by both physiological observations (Isaacson
and Strowbridge, 1998), synaptic organization (Price and Powell, 1970),
and the estimated ratio of granule cells to mitral cells of 100:1 (for
review, see Mori, 1987; Shepherd and Greer, 1998). Furthermore, because
granule cells are electrotonic-coupled via gap junctions (Reyher et
al., 1991), granule cell activity may be synchronized, thus permitting
synchronization of GABA release onto clusters of mitral cells.
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Figure 10.
Proposed models for synchronization involving
mitral cell intrinsic oscillations and synaptic inputs. These models
require a single granule cell impinging on at least two mitral cells.
A, Synchronization of subthreshold oscillations can
result from two IPSPs in distinct mitral cells coming from the same
granule cell. B, The synchronized oscillations can then
act as a timing device for EPSP integration, thus allowing
synchronization of mitral cell firing. C, For different
membrane potentials, IPSPs can trigger rebound spikes with similar
timing in two or more distinct mitral cells.
|
|
Synchronization of intrinsic or synaptically induced membrane potential
oscillations is a widespread phenomenon in the CNS. Synchronized
oscillations have been described in several brain structures: neocortex
(Steriade et al., 1996), striatum (Stern et al., 1998), and insect
antennal lobe (Laurent and Davidowitz, 1994; Stopfer et al., 1997).
From a theoretical point of view, it has been emphasized that
synchronized subthreshold oscillations of a neural network may furnish
a "context" for information processing (Buzsaki and Chrobak, 1995).
However, only one report (Lampl and Yarom, 1993) supports this model.
Here, as illustrated in Figure 10B, we propose that
subthreshold oscillations of the membrane potential act as a timing
device for the integration of EPSP into spikes. Hence, whenever an EPSP
impinges onto a mitral cell, the time corresponding to the highest
probability for spike initiation will correspond to the peak of the
next oscillation. Thus, when EPSPs occur in neighboring mitral cells
with synchronized subthreshold oscillations, their triggered spikes
will all occur within a narrow time window.
Because we were not able to detect any voltage-dependent amplification
of the EPSPs which trigger spikes, we propose that this temporal
control exerted by oscillations on spike timing is purely passive, not
requiring any voltage-gated conductances. A passive mechanism would be
efficient only if the EPSP kinetics had a longer duration than the
oscillation period. Indeed, we observed such a relationship because the
oscillation period was 50 msec, and the EPSP decay half-time constant
was 61.8 ± 10.7 msec. This result contrasts with inferior olive
neurons in which oscillations may act as a timing device for
subthreshold EPSPs via voltage-dependent amplification of synaptic
inputs (Lampl and Yarom, 1993).
The origin of excitatory inputs recruited by stimulation in the EPL
remains unknown. One possibility supported by both anatomical (Cajal,
1911) and electrophysiological (Nicoll, 1971) data involves recurrent
excitatory inputs onto secondary dendrites of mitral cells via their
axon collaterals. Alternatively, mitral cells could be excited by
glutamate released from the secondary dendrite of other mitral cells
(Isaacson, 1999) or from excitatory centrifugal fibers. Regardless of
the source of glutamate, the interactions between EPSPs and
subthreshold oscillations should result from local processing, and
therefore should participate in the formation of modules required for
olfactory processing (Kauer, 1991).
Another mechanism that might allow mitral cell synchronization is the
generation of a rebound spike in different mitral cells connected to a
single granule cell (Fig. 10C). This mechanism could allow
rapid synchronization of a cluster of mitral cells. This phenomenon has
already been described in other brain structures known for their
rhythmic activity, such as the thalamus (von Krosigk et al., 1993) and
the hippocampus (Cobb et al., 1995). Likewise, these two phenomena
depicted in Figure 10, B and C, might also coexist in physiological conditions because stimulation of the olfactory nerve, which is known to induce firing patterns similar to
those induced by odors (Freeman, 1972; Cinelli et al., 1995), triggers
both subthreshold oscillations and clusters of spikes.
Olfactory bulb synchronization emerges from intrinsic
neuronal interactions
Extracellular recordings have revealed that olfactory stimuli
trigger the generation of network oscillations in a variety of species
(for review, see Laurent, 1996b, 1997). In particular, it has been
shown that mitral cell firing was phased with EEG oscillations in
rodents (Freeman, 1975; Gray and Skinner, 1988). This oscillatory
activity seems to be intrinsic to the olfactory bulb neural network,
because olfactory or antennal nerve stimulation gives rise to EEG or
LFP oscillations (Freeman, 1972) that persist after cryogenic blockade
of centrifugal afferents (Gray and Skinner, 1988). These observations
are consistent with our model in which synchronization emerges from
intrinsic properties of the bulbar neuronal network.
Distributed olfactory processing requires temporal coding
The idea that odor information is represented in a distributed
fashion within the olfactory bulb (Stewart et al., 1979; Cinelli et
al., 1995) is consistent with two experimental findings. First, both
olfactory sensory neurons (Getchell and Shepherd 1978; Malnic et al.,
1999) and mitral cells (Wellis et al., 1989; Motokizawa, 1996; Bhalla
and Bower, 1997) are very broadly tuned as they respond to a wide
variety of odors. Second, it has been shown that rats with large
olfactory bulb lesions can still detect as wide a variety of odors and
discriminate odor mixtures as well as intact animals (Lu and Slotnick,
1994). Thus, the role of oscillations may be to allow the
synchronization of populations of neurons that simultaneously process
multiple odors. In the antennal lobe, pharmacological manipulations
that desynchronize the activity of output cells results in a loss of
discrimination between similar odors (Stopfer et al., 1997). Our
study demonstrates the high degree of precision with which oscillations
can time neuronal outputs, and we propose that these oscillations are
crucial to olfactory processing in facilitating the synchronization of
individual members of mitral cell subpopulations that may be widely
distributed in the bulb.
| |
FOOTNOTES |
Received July 12, 1999; revised Aug. 30, 1999; accepted Sept. 28, 1999.
This work was supported by the Centre National de la Recherche
Scientifique, the Institut Universitaire de France, and a grant from
the Direction des Recherches et Etudes Techniques to D.D. We are
grateful to G. Sadoc for help with the analysis and acquisition software. We thank G. M. Shepherd and the members of our
laboratory for comments on this manuscript.
Correspondence should be addressed to Dr. Pierre-Marie Lledo, Centre
National de la Recherche Scientifique, Institut Alfred Fessard, Avenue
de la Terrasse, 91198 Gif-sur-Yvette Cedex, France. E-mail:
lledo{at}iaf.cnrs-gif.fr.
| |
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ABSTRACT
INTRODUCTION
