title: Dynamical mean-field theory of noisy spiking neuron ensembles: application to the Hodgkin-Huxley model.
abstract: A dynamical mean-field approximation (DMA) previously proposed by the present author [H. Hasegawa, Phys. Rev E 67, 041903 (2003)] has been extended to ensembles described by a general noisy spiking neuron model. Ensembles of N-unit neurons, each of which is expressed by coupled K-dimensional differential equations (DEs), are assumed to be subject to spatially correlated white noises. The original KN-dimensional stochastic DEs have been replaced by K(K+2)-dimensional deterministic DEs expressed in terms of means and the second-order moments of local and global variables: the fourth-order contributions are taken into account by the Gaussian decoupling approximation. Our DMA has been applied to an ensemble of Hodgkin-Huxley (HH) neurons (K=4), for which effects of the noise, the coupling strength, and the ensemble size on the response to a single-spike input have been investigated. Numerical results calculated by the DMA theory are in good agreement with those obtained by direct simulations, although the former computation is about a thousand times faster than the latter for a typical HH neuron ensemble with N=100.
authors.affiliation: Department of Physics, Tokyo Gakugei University, Koganei, Tokyo 184-8501, Japan. hasegawa@u-gakugei.ac.jp
authors.forename: Hideo
authors.initials: H
authors.lastname: Hasegawa
mesh_terms: D000200:Action Potentials; D003198:Computer Simulation; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013269:Stochastic Processes; D009435:Synaptic Transmission / Q000502:physiology*