Spike train statistics and dynamics with synaptic input from any renewal process: a population density approach.
about
Divisive gain modulation with dynamic stimuli in integrate-and-fire neuronsA Diffusion Approximation and Numerical Methods for Adaptive Neuron Models with Stochastic Inputs.A new method to infer higher-order spike correlations from membrane potentials.A principled dimension-reduction method for the population density approach to modeling networks of neurons with synaptic dynamics.Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity.Firing variability is higher than deduced from the empirical coefficient of variation.Population density equations for stochastic processes with memory kernels.Finite volume and asymptotic methods for stochastic neuron models with correlated inputs.Stein's neuronal model with pooled renewal input.Coupling regularizes individual units in noisy populations.Transmission of temporally correlated spike trains through synapses with short-term depression
P2860
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P2860
Spike train statistics and dynamics with synaptic input from any renewal process: a population density approach.
description
2009 nî lūn-bûn
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2009年の論文
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2009年学术文章
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2009年学术文章
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name
Spike train statistics and dyn ...... a population density approach.
@en
Spike train statistics and dyn ...... a population density approach.
@nl
type
label
Spike train statistics and dyn ...... a population density approach.
@en
Spike train statistics and dyn ...... a population density approach.
@nl
prefLabel
Spike train statistics and dyn ...... a population density approach.
@en
Spike train statistics and dyn ...... a population density approach.
@nl
P2860
P1433
P1476
Spike train statistics and dyn ...... a population density approach.
@en
P2093
Daniel Tranchina
P2860
P304
P356
10.1162/NECO.2008.03-08-743
P577
2009-02-01T00:00:00Z