Non-Boltzmann dynamics in networks of spiking neurons

Michael C. Crair, William Bialek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Networks of spiking neurons in which spikes are fixed as a Poisson process are studied. The state of a cell is determined by the instantaneous firing rate, and in the limit of high firing rates the model reduces to that studied by Hopfield. The inclusion of spiking results in several new features, such as a noise-induced asymmetry between on and off states, and probability currents which destroy the usual description of network dynamics in terms of energy surfaces. Taking account of spikes also allows calibrating of network parameters such as synaptic weights against experiments on real synapses. Realistic forms of the post synaptic response alter the network dynamics, which suggests a novel dynamical learning mechanism.

Original languageEnglish (US)
Title of host publication91 IEEE Int Jt Conf Neural Networks IJCNN 91
PublisherPubl by IEEE
Pages2508-2514
Number of pages7
ISBN (Print)0780302273
StatePublished - Dec 1 1991
Event1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore
Duration: Nov 18 1991Nov 21 1991

Publication series

Name91 IEEE Int Jt Conf Neural Networks IJCNN 91

Other

Other1991 IEEE International Joint Conference on Neural Networks - IJCNN '91
CitySingapore, Singapore
Period11/18/9111/21/91

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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