In this note we consider the perfect integrator driven by Poisson process input. We derive its equilibrium and response properties and contrast them to the approximations obtained by applying the diffusion approximation. In particular, the probability density in the vicinity of the threshold differs, which leads to altered response properties of the system in equilibrium.Comment: 7 pages, 3 figures, v2: corrected authors in referenc
Nonlinear Noisy Leaky Integrate and Fire (NNLIF) models for neurons networks can be written as Fokke...
Receptive profiles of V1 cortical cells are very heterogeneous and act by differentiating the stimul...
We consider the stochastic system of interacting neurons introduced in De Masi et al. (2015) and in ...
In this note we consider the perfect integrator driven by Poisson process input. We derive its equil...
The calculation of the steady-state probability density for multidimensional stochastic systems that...
A new mathematical model of memristive neural networks described by the partly diffusive reaction-di...
The integrate-and-fire neuron with exponential postsynaptic potentials is a frequently employed mode...
International audienceThe Noisy Integrate-and-Fire equation is a standard non-linear Fokker-Planck E...
We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically c...
We derive an explicit distribution for the threshold sequence of the symmetric binary perceptron wit...
The dynamics of an ensemble of particles driven out of a potential well, with replacement, by the Po...
In two recent articles, Rudolph and Destexhe (2003, 2005) studied a leaky integrator model (an RC-ci...
In this paper, we study analytically the impact of an inhibitory autapse on neuronal activity. In or...
A generalization of an earlier paper (Capocelli and Ricciardi, 1971), dealing with a diffusion appro...
The capacity defines the ultimate fidelity limits of information transmission by any system. We deri...
Nonlinear Noisy Leaky Integrate and Fire (NNLIF) models for neurons networks can be written as Fokke...
Receptive profiles of V1 cortical cells are very heterogeneous and act by differentiating the stimul...
We consider the stochastic system of interacting neurons introduced in De Masi et al. (2015) and in ...
In this note we consider the perfect integrator driven by Poisson process input. We derive its equil...
The calculation of the steady-state probability density for multidimensional stochastic systems that...
A new mathematical model of memristive neural networks described by the partly diffusive reaction-di...
The integrate-and-fire neuron with exponential postsynaptic potentials is a frequently employed mode...
International audienceThe Noisy Integrate-and-Fire equation is a standard non-linear Fokker-Planck E...
We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically c...
We derive an explicit distribution for the threshold sequence of the symmetric binary perceptron wit...
The dynamics of an ensemble of particles driven out of a potential well, with replacement, by the Po...
In two recent articles, Rudolph and Destexhe (2003, 2005) studied a leaky integrator model (an RC-ci...
In this paper, we study analytically the impact of an inhibitory autapse on neuronal activity. In or...
A generalization of an earlier paper (Capocelli and Ricciardi, 1971), dealing with a diffusion appro...
The capacity defines the ultimate fidelity limits of information transmission by any system. We deri...
Nonlinear Noisy Leaky Integrate and Fire (NNLIF) models for neurons networks can be written as Fokke...
Receptive profiles of V1 cortical cells are very heterogeneous and act by differentiating the stimul...
We consider the stochastic system of interacting neurons introduced in De Masi et al. (2015) and in ...