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14
Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity
 Neural Computation
, 2003
"... In model networks of Ecells and Icells (excitatory and inhibitory neurons) , synchronous rhythmic spiking often comes about from the interplay between the two cell groups: the Ecells synchronize the Icells and vice versa. Under ideal conditions  homogeneity in relevant network parameters, ..."
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Cited by 75 (9 self)
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In model networks of Ecells and Icells (excitatory and inhibitory neurons) , synchronous rhythmic spiking often comes about from the interplay between the two cell groups: the Ecells synchronize the Icells and vice versa. Under ideal conditions  homogeneity in relevant network parameters, and alltoall connectivity for instance  this mechanism can yield perfect synchronization.
Characterization of Subthreshold Voltage Fluctuations in Neuronal Membranes
, 2003
"... Synaptic noise due to intense network activity can have a significant impact on the electrophysiological properties of individual neurons. This is the case for the cerebral cortex, where ongoing activity leads to strong barrages of synaptic inputs, which act as the main source of synaptic noise affe ..."
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Cited by 36 (14 self)
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Synaptic noise due to intense network activity can have a significant impact on the electrophysiological properties of individual neurons. This is the case for the cerebral cortex, where ongoing activity leads to strong barrages of synaptic inputs, which act as the main source of synaptic noise affecting on neuronal dynamics. Here, we characterize the subthreshold behavior of neuronal models in which synaptic noise is represented by either additive or multiplicative noise, described by OrnsteinUhlenbeck processes. We derive and solve the FokkerPlanck equation for this system, which describes the time evolution of the probability density function for the membrane potential. We obtain an analytic expression for the membrane potential distribution at steady state and compare this expression with the subthreshold activity obtained in HodgkinHuxleytype models with stochastic synaptic inputs. The differences between multiplicative and additive noise models suggest that multiplicative noise is adequate to describe the highconductance states similar to in vivo conditions. Because the steadystate membrane potential distribution is easily obtained experimentally, this approach provides a possible method to estimate the mean and variance of synaptic conductances in real neurons.
Spike train probability models for stimulusdriven leaky integrateandfire neurons
 Neural Computation
, 2008
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Interspike Interval Variability for Balanced Networks With Reversal Potentials for Large Numbers of Inputs
, 2000
"... The hypothesis that the variability in the discharge of cortical neurons results from balanced excitation and inhibition is analyzed. A method is presented for analyzing the integrate and re neural model with reversal potentials which enables the interspike interval distribution to be calculated in ..."
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Cited by 2 (0 self)
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The hypothesis that the variability in the discharge of cortical neurons results from balanced excitation and inhibition is analyzed. A method is presented for analyzing the integrate and re neural model with reversal potentials which enables the interspike interval distribution to be calculated in the Gaussian approximation. Results are presented for the perfect integrator model, for which a stable value of the membrane potential in the absence of the spiking mechanism exists. The results show close agreement with numerical simulations for large numbers of small amplitude inputs. The coecient of variation is consistently less than 1.0, as observed in cortical neurons. Key words: Integrate and re neurons, Reversal potentials, Perfect integrator, Interspike interval variability, Firstpassage time. 1 Introduction The origin of the variability in the discharge of cortical neurons is not well understood. An analysis of the coecient of variation in the spikes generated by an integrate an...
Inferring network activity from synaptic noise
, 2004
"... During intense network activity in vivo, cortical neurons are in a highconductance state, in which the membrane potential (Vm) is subject to a tremendous fluctuating activity. Clearly, this ‘‘synaptic noise’ ’ contains information about the activity of the network, but there are presently no method ..."
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Cited by 1 (0 self)
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During intense network activity in vivo, cortical neurons are in a highconductance state, in which the membrane potential (Vm) is subject to a tremendous fluctuating activity. Clearly, this ‘‘synaptic noise’ ’ contains information about the activity of the network, but there are presently no methods available to extract this information. We focus here on this problem from a computational neuroscience perspective, with the aim of drawing methods to analyze experimental data. We start from models of cortical neurons, in which highconductance states stem from the random release of thousands of excitatory and inhibitory synapses. This highly complex system can be simplified by using global synaptic conductances described by effective stochastic processes. The advantage of this approach is that one can derive analytically a number of properties from the statistics of resulting Vm fluctuations. For example, the global excitatory and inhibitory conductances can be extracted from synaptic noise, and can be related to the mean activity of presynaptic neurons. We show here that extracting the variances of excitatory and inhibitory synaptic conductances can provide estimates of the mean temporal correlation—or level of synchrony—among thousands of neurons in the network. Thus, ‘‘probing the network’ ’ through intracellular V m activity is possible and constitutes a promising approach, but it will require a continuous effort combining theory, computational models and intracellular physiology.
An InformationTheoretic Analysis of the Coding of a Periodic Synaptic Input by IntegrateandFire Neurons
, 2002
"... An expression for the mutual information between the phase of a periodic stimulus and the timing of the output spikes generated by the stimulus is given in the low output spikingrate regime. The mutual information is calculated for the leaky integrateandfire neuron in the Gaussian approximation. ..."
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An expression for the mutual information between the phase of a periodic stimulus and the timing of the output spikes generated by the stimulus is given in the low output spikingrate regime. The mutual information is calculated for the leaky integrateandfire neuron in the Gaussian approximation. The mutual information is found to be e#ectively described as a function of the synchronization of the output spikes and their average spikingrate. The results in the subthreshold input regime shed light upon the role of stochastic resonance in such models.
LETTER Communicated by Ad Aertsen Coding of Temporally Varying Signals in Networks of Spiking Neurons with Global Delayed Feedback
"... Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant ..."
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Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant inputs. On the other hand, real recurrent neural networks are abundant and continually receive informationrich inputs from the outside environment or other parts of the brain. We examine how feedforward networks of spiking neurons with delayed global feedback process information about temporally changing inputs. We show that the network behavior is more synchronous as well as more correlated with and phaselocked to the stimulus when the stimulus frequency is resonant with the inherent frequency of the neuron or that of the network oscillation generated by the feedback architecture. The two eigenmodes have distinct dynamical characteristics, which are supported by numerical simulations and by analytical arguments based on frequency response and bifurcation theory. This distinction is similar to the class I versus class II classification of single neurons according to the bifurcation from
ARTICLE Communicated by Bard Ermentrout Synchronization in Networks of Excitatory and Inhibitory Neurons with Sparse, Random Connectivity
"... In model networks of Ecells and Icells (excitatory and inhibitory neurons, respectively), synchronous rhythmic spiking often comes about from the interplay between the two cell groups: the Ecells synchronize the Icells and vice versa. Under ideal conditions—homogeneity in relevant network parame ..."
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In model networks of Ecells and Icells (excitatory and inhibitory neurons, respectively), synchronous rhythmic spiking often comes about from the interplay between the two cell groups: the Ecells synchronize the Icells and vice versa. Under ideal conditions—homogeneity in relevant network parameters and alltoall connectivity, for instance—this mechanism can yield perfect synchronization. We �nd that approximate, imperfect synchronization is possible even with very sparse, random connectivity. The crucial quantity is the expected number of inputs per cell. As long as it is large enough (more precisely, as long as the variance of the total number of synaptic inputs per cell is small enough), tight synchronization is possible. The desynchronizing effect of random connectivity can be reduced by strengthening the E!I synapses. More surprising, it cannot be reduced by strengthening the I!E synapses. However, the decay time constant of inhibition plays an important role. Faster decay yields tighter synchrony. In particular, in models in which the inhibitory synapses are assumed to be instantaneous, the effects of sparse, random connectivity cannot be seen. 1
LETTER Communicated by Anthony Burkitt Implications of Noise and Neural Heterogeneity for VestibuloOcular Reflex Fidelity
"... The vestibuloocular reflex (VOR) is characterized by a shortlatency, highfidelity eye movement response to head rotations at frequencies up to 20 Hz. Electrophysiological studies of medial vestibular nucleus (MVN) neurons, however, show that their response to sinusoidal currents above 10 to 12 Hz ..."
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The vestibuloocular reflex (VOR) is characterized by a shortlatency, highfidelity eye movement response to head rotations at frequencies up to 20 Hz. Electrophysiological studies of medial vestibular nucleus (MVN) neurons, however, show that their response to sinusoidal currents above 10 to 12 Hz is highly nonlinear and distorted by aliasing for all but very small current amplitudes. How can this system function in vivo when single cell response cannot explain its operation? Here we show that the necessary wide VOR frequency response may be achieved not by firing rate encoding of head velocity in single neurons, but in the integrated population response of asynchronously firing, intrinsically active neurons. Diffusive synaptic noise and the pacemakerdriven, intrinsic firing of MVN cells synergistically maintain asynchronous, spontaneous spiking in a population of model MVN neurons over a wide range of input signal amplitudes and frequencies. Response fidelity is further improved by a reciprocal inhibitory link between two MVN populations,
Gain Modulation and Balanced Synaptic Input in ConductanceBased Neural Model
, 2003
"... Gain modulation of neural responses by the balanced component of the synaptic input is analyzed in the gaussian approximation using a single compartment conductancebased neural model. The model is analyzed in the "normal operating regime", in which the output spikingrate of the neuron is ..."
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Gain modulation of neural responses by the balanced component of the synaptic input is analyzed in the gaussian approximation using a single compartment conductancebased neural model. The model is analyzed in the "normal operating regime", in which the output spikingrate of the neuron is equal to the spontaneous spikingrate in the absence of any stimulus. The gain in response to both additional excitatory synaptic input and injected current is found to be modulated in a nonlinear way by the level of balanced synaptic input.