272 research outputs found
A constructive mean field analysis of multi population neural networks with random synaptic weights and stochastic inputs
We deal with the problem of bridging the gap between two scales in neuronal
modeling. At the first (microscopic) scale, neurons are considered individually
and their behavior described by stochastic differential equations that govern
the time variations of their membrane potentials. They are coupled by synaptic
connections acting on their resulting activity, a nonlinear function of their
membrane potential. At the second (mesoscopic) scale, interacting populations
of neurons are described individually by similar equations. The equations
describing the dynamical and the stationary mean field behaviors are considered
as functional equations on a set of stochastic processes. Using this new point
of view allows us to prove that these equations are well-posed on any finite
time interval and to provide a constructive method for effectively computing
their unique solution. This method is proved to converge to the unique solution
and we characterize its complexity and convergence rate. We also provide
partial results for the stationary problem on infinite time intervals. These
results shed some new light on such neural mass models as the one of Jansen and
Rit \cite{jansen-rit:95}: their dynamics appears as a coarse approximation of
the much richer dynamics that emerges from our analysis. Our numerical
experiments confirm that the framework we propose and the numerical methods we
derive from it provide a new and powerful tool for the exploration of neural
behaviors at different scales.Comment: 55 pages, 4 figures, to appear in "Frontiers in Neuroscience
Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses
We investigate the effect of electric synapses (gap junctions) on collective
neuronal dynamics and spike statistics in a conductance-based
Integrate-and-Fire neural network, driven by a Brownian noise, where
conductances depend upon spike history. We compute explicitly the time
evolution operator and show that, given the spike-history of the network and
the membrane potentials at a given time, the further dynamical evolution can be
written in a closed form. We show that spike train statistics is described by a
Gibbs distribution whose potential can be approximated with an explicit
formula, when the noise is weak. This potential form encompasses existing
models for spike trains statistics analysis such as maximum entropy models or
Generalized Linear Models (GLM). We also discuss the different types of
correlations: those induced by a shared stimulus and those induced by neurons
interactions.Comment: 42 pages, 1 figure, submitte
Exact computation of the Maximum Entropy Potential of spiking neural networks models
Understanding how stimuli and synaptic connectivity in uence the statistics
of spike patterns in neural networks is a central question in computational
neuroscience. Maximum Entropy approach has been successfully used to
characterize the statistical response of simultaneously recorded spiking
neurons responding to stimuli. But, in spite of good performance in terms of
prediction, the fitting parameters do not explain the underlying mechanistic
causes of the observed correlations. On the other hand, mathematical models of
spiking neurons (neuro-mimetic models) provide a probabilistic mapping between
stimulus, network architecture and spike patterns in terms of conditional
proba- bilities. In this paper we build an exact analytical mapping between
neuro-mimetic and Maximum Entropy models.Comment: arXiv admin note: text overlap with arXiv:1309.587
Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains
We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions
with spatio-temporal constraints from experimental spike trains. This is an
extension of two papers [10] and [4] who proposed the estimation of parameters
where only spatial constraints were taken into account. The extension we
propose allows to properly handle memory effects in spike statistics, for large
sized neural networks.Comment: 34 pages, 33 figure
Linear response for spiking neuronal networks with unbounded memory
We establish a general linear response relation for spiking neuronal
networks, based on chains with unbounded memory. This relation allows us to
predict the influence of a weak amplitude time-dependent external stimuli on
spatio-temporal spike correlations, from the spontaneous statistics (without
stimulus) in a general context where the memory in spike dynamics can extend
arbitrarily far in the past. Using this approach, we show how linear response
is explicitly related to neuronal dynamics with an example, the gIF model,
introduced by M. Rudolph and A. Destexhe. This example illustrates the
collective effect of the stimuli, intrinsic neuronal dynamics, and network
connectivity on spike statistics. We illustrate our results with numerical
simulations.Comment: 60 pages, 8 figure
Spatio-temporal spike trains analysis for large scale networks using maximum entropy principle and Monte-Carlo method
Understanding the dynamics of neural networks is a major challenge in
experimental neuroscience. For that purpose, a modelling of the recorded
activity that reproduces the main statistics of the data is required. In a
first part, we present a review on recent results dealing with spike train
statistics analysis using maximum entropy models (MaxEnt). Most of these
studies have been focusing on modelling synchronous spike patterns, leaving
aside the temporal dynamics of the neural activity. However, the maximum
entropy principle can be generalized to the temporal case, leading to Markovian
models where memory effects and time correlations in the dynamics are properly
taken into account. In a second part, we present a new method based on
Monte-Carlo sampling which is suited for the fitting of large-scale
spatio-temporal MaxEnt models. The formalism and the tools presented here will
be essential to fit MaxEnt spatio-temporal models to large neural ensembles.Comment: 41 pages, 10 figure
A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks
We present a mathematical analysis of the effects of Hebbian learning in
random recurrent neural networks, with a generic Hebbian learning rule
including passive forgetting and different time scales for neuronal activity
and learning dynamics. Previous numerical works have reported that Hebbian
learning drives the system from chaos to a steady state through a sequence of
bifurcations. Here, we interpret these results mathematically and show that
these effects, involving a complex coupling between neuronal dynamics and
synaptic graph structure, can be analyzed using Jacobian matrices, which
introduce both a structural and a dynamical point of view on the neural network
evolution. Furthermore, we show that the sensitivity to a learned pattern is
maximal when the largest Lyapunov exponent is close to 0. We discuss how neural
networks may take advantage of this regime of high functional interest
Spike trains statistics in Integrate and Fire Models: exact results
We briefly review and highlight the consequences of rigorous and exact
results obtained in \cite{cessac:10}, characterizing the statistics of spike
trains in a network of leaky Integrate-and-Fire neurons, where time is discrete
and where neurons are subject to noise, without restriction on the synaptic
weights connectivity. The main result is that spike trains statistics are
characterized by a Gibbs distribution, whose potential is explicitly
computable. This establishes, on one hand, a rigorous ground for the current
investigations attempting to characterize real spike trains data with Gibbs
distributions, such as the Ising-like distribution, using the maximal entropy
principle. However, it transpires from the present analysis that the Ising
model might be a rather weak approximation. Indeed, the Gibbs potential (the
formal "Hamiltonian") is the log of the so-called "conditional intensity" (the
probability that a neuron fires given the past of the whole network). But, in
the present example, this probability has an infinite memory, and the
corresponding process is non-Markovian (resp. the Gibbs potential has infinite
range). Moreover, causality implies that the conditional intensity does not
depend on the state of the neurons at the \textit{same time}, ruling out the
Ising model as a candidate for an exact characterization of spike trains
statistics. However, Markovian approximations can be proposed whose degree of
approximation can be rigorously controlled. In this setting, Ising model
appears as the "next step" after the Bernoulli model (independent neurons)
since it introduces spatial pairwise correlations, but not time correlations.
The range of validity of this approximation is discussed together with possible
approaches allowing to introduce time correlations, with algorithmic
extensions.Comment: 6 pages, submitted to conference NeuroComp2010
http://2010.neurocomp.fr/; Bruno Cessac
http://www-sop.inria.fr/neuromathcomp
Revisiting time discretisation of spiking network models
International audienc
Stochastic firing rate models
We review a recent approach to the mean-field limits in neural networks that
takes into account the stochastic nature of input current and the uncertainty
in synaptic coupling. This approach was proved to be a rigorous limit of the
network equations in a general setting, and we express here the results in a
more customary and simpler framework. We propose a heuristic argument to derive
these equations providing a more intuitive understanding of their origin. These
equations are characterized by a strong coupling between the different moments
of the solutions. We analyse the equations, present an algorithm to simulate
the solutions of these mean-field equations, and investigate numerically the
equations. In particular, we build a bridge between these equations and
Sompolinsky and collaborators approach (1988, 1990), and show how the coupling
between the mean and the covariance function deviates from customary
approaches
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