1Department of Physiology and 2Department of Physics, Laval University, Quebec G1K 7P4, Canada; and 3Unité de Neurosciences Intégratives et Computatìonnelles, UPR 2191, Centre National de la Recherche Scientifique, 91198 Gif-sur-Yvette, France
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ABSTRACT |
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Hô, Nicolas and Alain Destexhe. Synaptic Background Activity Enhances the Responsiveness of Neocortical Pyramidal Neurons. J. Neurophysiol. 84: 1488-1496, 2000. Neocortical pyramidal neurons in vivo are subject to an intense synaptic background activity but little is known of how this activity affects cellular responsiveness and what function it may serve. These issues were examined in morphologically reconstructed neocortical pyramidal neurons in which synaptic background activity was simulated based on recent measurements in cat parietal cortex. We show that background activity can be decomposed into two components: a tonically active conductance and voltage fluctuations. Previous studies have mostly focused on the conductance effect, revealing that background activity is responsible for a decrease in responsiveness, which imposes severe conditions of coincidence of inputs necessary to discharge the cell. It is shown here, in contrast, that responsiveness is enhanced if voltage fluctuations are taken into account; in this case the model can produce responses to inputs that would normally be subthreshold. This effect is analyzed by dissecting and comparing the different components of background activity, as well as by evaluating the contribution of parameters such as the dendritic morphology, the distribution of leak currents, the value of axial resistivity, the densities of voltage-dependent currents, and the release parameters underlying background activity. Interestingly, the model's optimal responsiveness was obtained when voltage fluctuations were of the same order as those measured intracellularly in vivo. Possible consequences were also investigated at the population level, where the presence of background activity allowed networks of pyramidal neurons to instantaneously detect inputs that are small compared with the classical detection threshold. These results suggest, at the single-cell level, that the presence of voltage fluctuations has a determining influence on cellular responsiveness and that these should be taken into account in models of background activity. At the network level, we predict that background activity provides the necessary drive for detecting events that would normally be undetectable. Experiments are suggested to explore this possible functional role for background activity.
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INTRODUCTION |
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The membrane potential
(Vm) of neocortical neurons in vivo is
continuously fluctuating due to the presence of synaptic background activity (Azouz and Gray 1999; Contreras et al.
1996
; Lampl et al. 1999
; Matsumara et al.
1988
; Nowak et al. 1997
; Paré et
al. 1998b
), which reflects ongoing activity in the cortical
network. This activity may influence dendritic integration due to
tonically activated synaptic conductances in dendrites. This theme was
explored by modeling studies (Barrett 1975
;
Bernander et al. 1991
; Destexhe and Paré
1999
; Holmes and Woody 1989
; Rapp et al.
1992
), which have suggested that these conductances indeed
affect dendritic integration and impose strict conditions concerning
the convergence or coincidence of synaptic inputs necessary to
discharge the cell.
The electrophysiological properties of synaptic background activity
were characterized recently in cat parietal cortex by comparing
intracellularly recorded pyramidal neurons in vivo before and after
application of tetrodotoxin (TTX) (Destexhe and Paré 1999; Paré et al. 1998b
). It was found
that background activity accounts for up to 80% of the input
conductance, depending on the type and depth of the anesthesia. A
significant conductance increase due to background activity has also
been demonstrated in cerebellar Purkinje cells (Hausser and
Clark 1997
).
Another component of background activity is the presence of
high-amplitude Vm fluctuations, which
is a consistent feature of intracellular recordings in vivo, although
paradoxically these are rarely taken into account. In this paper, we
have used computational models to investigate the responsiveness of
neocortical pyramidal neurons in the presence of synaptic background
activity with Vm fluctuations.
Possible consequences at the network level were also investigated. Some
of these results have appeared previously in a congress abstract
(Destexhe and Hô 1999).
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METHODS |
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Computational models were designed based on four morphologically
reconstructed neocortical pyramidal cells from cats (1 from layer
II-III, 2 from layer V, and 1 from layer VI), which were obtained from
two previous studies (Contreras et al. 1997;
Douglas et al. 1991
). The layer VI pyramidal cell (Fig.
1A) was used primarily, and
the results were checked using the three other geometries (see
RESULTS). The cellular geometries were incorporated into the NEURON simulation environment (Hines and Carnevale
1997
). The dendritic surface was corrected for spines, assuming
that spines represent about 45% of the dendritic membrane area
(DeFelipe and Fariñas 1992
). Passive parameters,
such as the membrane resistance, capacitance and axial resistivity were
estimated by matching the models to passive responses obtained
intracellularly after application of TTX and synaptic blockers
(Destexhe and Paré 1999
). Different combinations
of passive parameters were used, such as a supplementary shunt
conductance of 10 nS in the soma due to electrode impalement, different
values of the axial resistivity (80-250
cm) and different distributions of leak conductances in soma and dendrites (nonuniform distributions taken from Stuart and Spruston 1998
).
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Voltage-dependent conductances were inserted in soma, dendrites, and
axon. Na+ and K+ currents
were simulated using Hodgkin and Huxley (1952) type models and had the following densities (in
mS/cm2): Na+, 12 in soma,
12 in dendrites, 120 in axon; K+, 10 in soma, 10 in dendrites, 100 in axon. Kinetics of the currents were taken from a
model of hippocampal pyramidal cells (Traub and Miles
1991
) in which inactivation was shifted by 10 mV toward hyperpolarized values to match voltage-clamp data of cortical pyramidal
cells (Huguenard et al. 1988
). Action potential
threshold was set to about
55 mV at the soma. In some simulations, a
high-threshold Ca2+ current and a
voltage-dependent and Ca2+-dependent
K+ current were inserted in soma and dendrites
with densities and kinetics identical to previous studies
(Paré et al. 1998a
; Yamada et al.
1998
). The model was based on neocortical neurons from cat
association cortex, which show few or no evidence for the hyperpolarization-activated current Ih
or for low-threshold calcium currents (Paré et al.
1998a
,b
). These currents were therefore not included in the simulations.
Synaptic currents were simulated by kinetic models of glutamatergic and
GABAergic receptors: glutamate
-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA),
glutamate N-methyl-D-aspartate (NMDA), and
-aminobutyric acid type-A (GABAA) receptor
types were simulated according to two-state kinetic models
(Destexhe et al. 1994
, 1998
). NMDA receptors were only
included in some simulations (see RESULTS) and were modeled
with a maximal conductance of 25% as that of AMPA receptors, and the
voltage-dependent block was modeled assuming an extracellular Mg2+ concentration of 1 mM (see details in
Destexhe et al. 1998
). Quantal conductances were
estimated from miniature synaptic events and were 1200 pS for AMPA and
600 pS for GABAA (Destexhe and Paré 1999
). Metabotropic receptors were not included. Equations for voltage-dependent and synaptic currents were identical to a previous model (Destexhe and Paré 1999
).
The densities of synapses in different regions of the cell were
estimated from morphological studies in neocortical pyramidal cells
(DeFelipe and Fariñas 1992; Fariñas
and DeFelipe 1991a
,b
; Larkman 1991
;
Mungai 1967
; White 1989
). The number of
synapses per 100 µm2 of membrane were: 10-20
(GABAA, soma), 40-80
(GABAA, axon initial segment), 8-12
(GABAA, dendrites), and 55-65 (AMPA-NMDA,
dendrites), leading to a total of 16563 glutamatergic and 3376 GABAergic synapses for the layer VI cell shown in Fig. 1A.
The release conditions corresponding to synaptic background activity
were estimated based on recent data from intracellular recordings of
pyramidal neurons before and after application of TTX (Destexhe
and Paré 1999; Paré et al. 1998b
).
To match in vivo recordings, high-frequency release conditions with a
significant correlation had to be introduced, such that the model
displayed Vm fluctuations, low input
resistance and depolarized Vm
consistent with in vivo measurements (Fig. 1B,
Correlated). These conditions correspond to all presynaptic
terminals releasing randomly according to Poisson processes (average
rates of 1 Hz at glutamatergic and 5.5 Hz at GABAergic synapses).
The correlation between release events was obtained by forcing some of
the synapses to release simultaneously while keeping the random nature
of the release at each synapse. This was achieved by generating
N2 Poisson-distributed random
presynaptic trains and by redistributing these trains among the
N synaptic sites in the model. If
N2 < N, all synapses still
released randomly with identical statistical properties, but at any
given instant some of the N synapses released
simultaneously, and were therefore "correlated." The
N2 inputs were redistributed randomly
among the N synapses at every time step, such that the
average correlation was the same for every pair of synapses
irrespective of their location in the dendritic tree. An advantage of
this procedure is that the correlation can be controlled independently,
by changing N2, without affecting the
average release frequency at each synapse and therefore with no change
in the overall synaptic conductance due to background activity. The
conductance and correlation parameters can therefore be adjusted
independently. Details of this algorithm were given in Destexhe
and Paré (1999).
A synaptic input was simulated in addition to the background activity.
For this purpose, an additional set of AMPA-mediated synapses was
considered such that the conductance density of these additional
synapses was uniform in dendrites. The range considered (0-1.5
mS/cm2) represents a small fraction of the total
AMPA conductance available in dendrites (72 mS/cm2 assuming 1200 pS per synapse and 0.6 synapse per µm2, as in Larkman
1991). Stimuli consisting of single shocks were used here, but
the results were also checked with bursts of stimuli consisting of
several shocks (2-5) at high frequency (100-300 Hz) and similar
results were obtained (not shown).
All computational models were run on Sparc 20 and Ultra 1 workstations (Sun Microsystems, Mountain View, CA).
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RESULTS |
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We start by illustrating the procedure to calculate the response of pyramidal neurons in the presence of background activity. We investigate the properties of responsiveness and which parameters are critical to explain them. Finally, we illustrate a possible consequence of these properties at the network level.
Measuring responsiveness in neocortical pyramidal neurons
In the layer VI pyramidal cell shown in Fig. 1A,
synaptic background activity was simulated by Poisson-distributed
random release events at glutamatergic and GABAergic synapses (see
METHODS). The model was constrained by intracellular
measurements of the Vm and input
resistance before and after application of TTX (Destexhe and
Paré 1999; Paré et al. 1998b
). A
random release rate of about 1 Hz for excitatory synapses and 5.5 Hz
for inhibitory synapses was necessary to reproduce the correct
Vm and input resistance. In addition,
it was necessary to include a correlation between release events to
reproduce the amplitude of Vm
fluctuations observed experimentally (Fig. 1B,
Correlated). This model thus reproduces the
electrophysiological parameters measured intracellularly in vivo: a
depolarized Vm, a reduced input
resistance and high-amplitude Vm fluctuations.
To investigate the response of the modeled cell in these conditions, a
set of excitatory synapses was activated in dendrites, in addition to
the synapses involved in generating background activity (see
METHODS). Simultaneous activation of these additional synapses, in the presence of background activity, evoked action potentials with considerable variability in successive trials (Fig.
1C), as expected from the random nature of the background activity. A similar high variability of synaptic responses is typically
observed in vivo (Arieli et al. 1996; Azouz and
Gray 1999
; Contreras et al. 1996
; Lampl
et al. 1999
; Nowak et al. 1997
; Paré et al. 1998b
). The evoked response, expressed
as a probability of evoking a spike in successive 0.5 ms intervals, is
shown in Fig. 1D (cumulative probability shown in Fig.
1E). The variability of responses depended on the strength
of synaptic stimuli, with stronger stimuli leading to narrower
probabilities of evoking a spike (Fig. 1D).
Thus the most appropriate measure of synaptic response in the presence of highly fluctuating background activity is to compute probabilities of evoking a spike. In the following, we use this measure to characterize the responsiveness of pyramidal neurons in different conditions.
Enhanced responsiveness in the presence of background activity
To characterize responsiveness, the cumulative firing
probability was computed for increasing input strength. In quiescent conditions, the cell responded in an all-or-none manner (Fig. 2A), reflecting the threshold
for action potentials. Additional conductances were included by
calculating the total conductance due to synaptic background activity
in each compartment of the neuron. In the presence of this additional
dendritic shunt, the response curve was shifted to higher input
strength (Fig. 2A, dashed line). Thus consistent with the
overall gain decrease evidenced in previous studies (Barrett
1975; Bernander et al. 1991
; Holmes and
Woody 1989
), the conductance of background activity decreased responsiveness and imposed strict conditions of convergence to discharge the cell.
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However, in the presence of correlated background activity, the response was qualitatively different (Fig. 2B). The cell was more responsive, because small excitatory inputs that were subthreshold in quiescent conditions (e.g., 0.1 mS/cm2 in Fig. 2, A and B) could generate action potentials in the presence of background activity (arrow). More importantly, the model cell produced a different response to a wide range of input strength, thus producing a different response to inputs that were indistinguishable in the absence of background activity.
These simulations thus suggest that the presence of background activity
at a level similar to in vivo measurements (Destexhe et al.
1999; Paré et al. 1998b
) is responsible
for a significant effect on the responsiveness of pyramidal neurons.
The specific role of the different components of background activity is
investigated below.
Enhanced responsiveness is caused by voltage fluctuations
To investigate the role of voltage fluctuations, we compared two models with background activity of equivalent conductance but different Vm fluctuations. By using uncorrelated and correlated background activities (Fig. 1B), the neuron received the same amount of random inputs, but combined differently, resulting in equivalent average conductance but different amplitudes of Vm fluctuations. With uncorrelated background activity, small inputs became subthreshold again (e.g., 0.1 mS/cm2 in Fig. 3A). The response curve was steeper (Fig. 3A, right), closer to the equivalent leak conductance (compare with Fig. 2A, dashed line). Thus comparing correlated and uncorrelated activity, it appears that the presence of high-amplitude Vm fluctuations significantly affects cellular responsiveness. The persistence of small-amplitude Vm fluctuations in the uncorrelated case is presumably responsible for the sigmoid shape in Fig. 3A.
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To dissociate the effect of Vm
fluctuations from that of shunting conductance, background activity was
replaced by injection of noisy current waveforms at all somatic and
dendritic compartments. The total net currents due to background
activity were recorded at each compartment and injected in the same
locations in a model without background activity. This "replay"
procedure led to Vm fluctuations
similar to those produced by synaptic background activity (Fig.
3B, left) but without the important tonically
activated synaptic conductance, allowing us to dissociate these two
factors. With noisy current injection, the input resistance was
comparable to that of quiescent conditions
(Rin = 45.5 vs. 46.5 M), but the
cell was more responsive, with subthreshold inputs in quiescent conditions evoking a significant response in the presence of
Vm fluctuations (e.g., 0.05 mS/cm2 in Fig. 3B).
The case of a fluctuating conductance without Vm fluctuations was also tested. The total conductance was recorded in each compartment during correlated background activity and was assigned to the leak conductance in each compartment of a model without background activity. This procedure led to a relatively steep response curve (Fig. 3B, dotted curve). Although these conductance fluctuations slightly enhanced responsiveness, this effect was small compared with that of Vm fluctuations.
To assess the importance of these different factors, their effect was compared in Fig. 3C. The effect of conductance is to decrease responsiveness, as shown by the shift of the response curve toward larger input strength (Fig. 3C, Conductance). The effect of voltage fluctuations is to increase responsiveness by shifting the curve to the opposite direction (Fig. 3C, Voltage fluctuations). Combining these two factors led to a response curve (Fig. 3C, Both) which was qualitatively similar to the correlated background activity (compare with Fig. 2B, right).
We therefore conclude that the behavior of the neocortical cell in the presence of correlated background activity can be understood qualitatively by a combination of two opposite influences: a tonically active conductance, which decreases responsiveness, and voltage fluctuations, which increase responsiveness.
Robustness of enhanced responsiveness
The robustness of this finding was examined by performing variations in the configuration of the model. Simulations using four different reconstructed pyramidal cells from cat neocortex, including a layer II-III cell and two layer V cells, showed a similar enhancement in responsiveness for all cases (Fig. 4). For each cell, correlated background activity (continuous curves) was compared with the equivalent shunt conductance (dashed curves), showing that the presence of background activity significantly enhanced responsiveness for the four cellular morphologies considered.
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To test the influence of dendritic excitability, the density of Na+ and K+ channels were varied in dendrites, soma, and axon. Rescaling these densities by the same factor resulted in a different global excitability of the cell and gave rise to a shift in the response curves, as expected (Fig. 5A). However, in all cases, comparing correlated background activity (continuous curves) to models with equivalent shunt (dashed curves), revealed an enhancement in responsiveness irrespective of the exact position of the response curves.
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The same phenomenon was also present for various other parameters, such as the distribution of leak currents (Fig. 5B), different axial resistivities (Fig. 5B), different sets and densities of voltage-dependent currents (Fig. 5C), different combinations of synaptic receptors, and different release frequencies (Fig. 5D). For all cases, variations of parameters had an expected effect of shifting the response curve, but the presence of background activity always led to a significant enhancement in responsiveness similar to Fig. 5A.
We also tested whether the enhancement in responsiveness was sensitive to the proximity of the excitatory inputs to the somatic region. We calculated the synchronized stimulation of increasing densities of AMPA-mediated synapses located exclusively in the distal region of dendrites (>200 µm from soma; see Fig. 6A). The response curve following stimulation of distally located AMPA-mediated inputs was computed similarly as for uniform stimulation. Here again, the presence of background activity led to an enhancement of the responsiveness of the cell (Fig. 6B), showing that the mechanisms described above also apply to distally located inputs.
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Optimal conditions for enhanced responsiveness
To evaluate the range of voltage fluctuations at which
responsiveness is optimally enhanced, the response probability was computed for subthreshold inputs at different conditions of
Vm fluctuations. These different
conditions were obtained by varying the value of the correlation,
leading to background activities of identical conditions of conductance
and average Vm but different amplitudes of Vm fluctuations (see
Destexhe and Paré 1999). The probability of spikes
specifically evoked by subthreshold stimuli was represented as a
function of the amplitude of Vm
fluctuations in Fig. 7 (symbols). This
figure shows that there are no spikes evoked without background
activity or with background activity with fluctuations of too
small-amplitude. However, for Vm
fluctuations larger than about
v = 2 mV (as
measured by the standard deviation of
Vm), the response probability shows a
steep increase and stays above zero for background activities with
larger fluctuation amplitudes.1 The
responsiveness is therefore enhanced for a range of
Vm fluctuations of
v between 2 and 6 mV and more. Interestingly,
this optimal range approximately matches the level of
Vm fluctuations measured
intracellularly in cat parietal cortex in vivo
(
v = 4.0 ± 2.0 mV in Destexhe
and Paré 1999
; indicated by a gray area in Fig. 7).
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Possible consequences at the network level
The above results show that enhanced responsiveness can be demonstrated at the single-cell level, in which case the high variability of responses makes it necessary to perform averages over a large number of successive stimuli. Because the nervous system does not perform such temporal averaging, the physiological meaning of enhanced responsiveness would therefore be unclear if it relied exclusively on performing a large number of trials. We illustrate below that this averaging can also be performed at the population level (spatial averaging), leading to an instantaneous enhancement in responsiveness for single-trial stimuli.
We have examined the simple case of a feedforward network of pyramidal neurons, whose behavior was compared with and without synaptic background activity. This simple paradigm is illustrated in Fig. 8A. One thousand identical presynaptic pyramidal neurons received simultaneous afferent AMPA-mediated inputs with conductance randomized from cell-to-cell and projected to a single postsynaptic cell. The differences in afferent input thus created variations in the amplitude of the excitation and on the timing of the resulting spike in the presynaptic cells. The output of this population of cells was monitored through the EPSP evoked in the postsynaptic cell.
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In quiescent conditions (absence of background activity), the EPSP evoked in the postsynaptic cell was roughly all-or-none (Fig. 8B), reflecting the action potential threshold in the presynaptic cells (similar to Fig. 2A). When the presynaptic cells received correlated synaptic background activity (which was different in each cell), the EPSPs were more graded (Fig. 8, C and D), compatible with the sigmoid response curve in Fig. 2B (left). Perhaps the most interesting property was that the smallest inputs, which were subthreshold in quiescent conditions, led to a detectable EPSP in the presence of background activity (0.1-0.15 mS/cm2 in Fig. 8D). This shows that the network transmitted some information about these inputs, while this was filtered out in quiescent conditions.
Thus although this paradigm is greatly simplified (identical
presynaptic cells, independent background activities), it illustrates the important possibility that the enhanced responsiveness shown in
Fig. 2B may be used in populations of pyramidal neurons to instantaneously detect a single afferent stimulus. In these
conditions, the network can detect a remarkably wide range of afferent
input amplitudes. Similar to an effect previously reported in neural networks with additive noise (Collins et al. 1995),
networks of pyramidal cells with background activity can detect inputs
that are very small compared with the threshold for action potentials.
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DISCUSSION |
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In this paper, we have demonstrated using computational models that background activity can significantly enhance the responsiveness of neocortical pyramidal neurons. The significance of this finding is discussed below, as well as possible predictions to test it experimentally.
Background activity enhances synaptic responsiveness
Previous models (Barrett 1975; Bernander et
al. 1991
; Destexhe and Paré 1999
;
Holmes and Woody 1989
; Rapp et al. 1992
)
established that the conductance of background activity
decreases cellular responsiveness and imposes strict conditions of
convergence or coincidence. We show here that the voltage
fluctuations of background activity also have a determining
influence on cellular responsiveness. Models in the presence of
background activity with voltage fluctuations can produce responses to
inputs that would be subthreshold in quiescent conditions (Fig. 2).
This result challenges the intuitive view that neurons with low input
resistance should also have a low responsiveness.
These different contributions to background activity were evaluated by representing the conductance by an equivalent dendritic shunt, and representing voltage fluctuations by injection of noisy current waveforms. Models in which background activity was represented by the equivalent conductance have a clearly different responsiveness (Fig. 2), suggesting that representing background activity only by a shunt conductance is not sufficient. A similar disparity was obtained in models with only voltage fluctuations (Fig. 3B), showing that representing background activity by injection of noisy current waveforms is not sufficient either. On the other hand, a good approximation of the cell's responsiveness was obtained when these two factors were combined (Fig. 3C). This analysis therefore suggests that noisy current injection together with a shunt conductance should provide an acceptable representation of the effect of background activity.
The model also indicates that the enhancement in responsiveness due to background activity is relatively insensitive to factors such as dendritic morphology, passive parameters, the type of synaptic receptors, and the nature and distribution of voltage-dependent conductances in dendrites (Figs. 4 and 5). Changing these parameters affected the response curve, as expected, but in all cases, the presence of background activity significantly modified the responsiveness. The same conclusions also hold for inputs arising at distal dendritic sites (Fig. 6) or for inputs consisting of high-frequency bursts of stimuli (not shown). Consequently, similar conditions of release at excitatory and inhibitory synapses should result in background activity that affects the responsiveness of a wide range of cell types in cerebral cortex, irrespective of the details of their morphology and biophysical properties.2
There is a possibility that the enhancement in responsiveness due to
voltage fluctuations stems from mechanisms similar to stochastic
resonance, which is an amplification or optimization of weak inputs by
the assistance of noise in nonlinear systems (Gammaitoni et al.
1998; Wiesenfeld and Moss 1995
). Preliminary results (Rudolph and Destexhe 2000
) show that the
presence of stochastic resonance can indeed be demonstrated in this
model, but that it occurs only for a limited range of parameters. On the other hand, enhanced responsiveness is seen for a remarkably wide
range of parameters, even in cases where there is no evidence for
stochastic resonance (not shown). More detailed investigations will be
necessary to characterize the exact relation between these two phenomena.
Possible functional consequences
An interesting observation was that the enhanced responsiveness was obtained for a range of Vm fluctuations comparable to that measured intracellularly during activated states in vivo (Fig. 7). This suggests that the level of background activity present in vivo represents conditions close to optimal for enhancing the responsiveness of pyramidal neurons. It is possible that the network maintains a level of background activity whose functional role is to keep its cellular elements in a highly responsive state.
In agreement with this view, Fig. 8 illustrated that, in a simple feedforward network of pyramidal neurons, the presence of background activity allowed the network to instantaneously detect synaptic events that would normally be subthreshold (0.1-0.15 mS/cm2 in Fig. 8D). In this case, background activity sets the population of neurons into a state of more efficient and more sensitive detection of afferent inputs, which are transmitted to the postsynaptic cells, while the same inputs would be filtered out in the absence of background activity.
These results should be considered in parallel with the observation
that background activity is particularly intense in
intracellularly-recorded cortical neurons of awake animals
(Matsumara et al. 1988; Steriade et al.
1999
). In the light of the present model, we interpret the occurrence of intense background activity as a factor that facilitates information transmission. It is therefore conceivable that
background activity is an active component of arousal or attentional mechanisms, which is a possible direction to explore in
future models.
Predictions
The general prediction of this model is that neurons with
low input resistance can be more responsive if
Vm fluctuations are taken into
account. This prediction could be tested intracellularly in vivo by
considering two different states of background activity in the same
cell and comparing the response to synaptic stimuli in these two
states. For example, this could be performed under urethane or
ketamine-xylazine anesthesia, in which neocortical neurons oscillate
synchronously between an active phase and a silent phase
(Contreras and Steriade 1995). During the active phase,
cortical neurons display intense background activity with high-amplitude Vm fluctuations,
whereas the hyperpolarized phase is characterized by a virtual absence
of background activity (Paré et al. 1998b
). The
model predicts that excitatory inputs that are subthreshold or
indistinguishable during the silent phase should become detectable and
distinguishable if analyzed during the active phase at the same
Vm.
This prediction could also be tested in slices by injecting conductances combined with noisy current waveforms, leading to neurons with low input resistance and large-amplitude Vm fluctuations similar to in vivo measurements. The model predicts an increased sensitivity to synaptic stimulation in such conditions.
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ACKNOWLEDGMENTS |
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We thank Drs. D. Contreras, K. Grant, N. Kopell, D. Paré, and M. Rudolph for comments on the manuscript.
This research was supported by Medical Research Council of Canada Grant MT-13724 and National Institute of Neurological Disorders and Stroke Grant R01-NS-37711. N. Hô was supported by a fellowship from the Centre de Recherche en Neurobiologie.
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FOOTNOTES |
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1
After reaching a peak, the probability of
evoking spikes decreased steadily with v in
Fig. 7. This decrease was partly attributable to a decrease in
Rin because states with large
v also have a high rate of spontaneous
firing, and therefore activate voltage-dependent currents which
contribute to lower the Rin. Simulations
were run in which this Rin decrease was
compensated, which led to about twice less decrease of the probability
(not shown).
2
Note that the effect of background activity was
only considered here for regular-spiking cells and not for the
different types of bursting cells present in cerebral cortex
(Connors and Gutnick 1990). The characterization of the
effect of background activity on the latter type should be done in
future studies.
Address for reprint requests: A. Destexhe, Unité de Neurosciences Intégratives et Computationnelles, CNRS, 1 Avenue de la Terrasse, Bat. 32-33, 91198 Gif-sur-Yvette, France (E-mail: Alain.Destexhe{at}iaf.cnrs-gif.fr).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 16 February 2000; accepted in final form 30 May 2000.
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REFERENCES |
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