Open AccessStructural phase transitions in neural networks
Author(s) -
Tatyana S. Turova
Publication year - 2014
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2014.11.139
Subject(s) - artificial neural network , graph , random graph , stochastic neural network , computer science , statistical physics , biological system , stochastic process , network structure , topology (electrical circuits) , artificial intelligence , mathematics , theoretical computer science , physics , recurrent neural network , combinatorics , statistics , biology
A model is considered for a neural network that is a stochastic process on a random graph. The neurons are represented by "integrate-and-fire" processes. The structure of the graph is determined by the probabilities of the connections, and it depends on the activity in the network. The dependence between the initial level of sparseness of the connections and the dynamics of activation in the network was investigated. A balanced regime was found between activity, i.e., the level of excitation in the network, and inhibition, that allows formation of synfire chains.