This article is cited in 4 papers

BIOPHYSICS

Percolation transition in active neural networks with adaptive geometry

F. D. Iudin^ab, D. I. Iudin^ab, V. B. Kazantsev<sup>ab

a</sup> National Research Nizhni Novgorod University, pr. Gagarina 23, Nizhni Novgorod, 603950, Russia
^b Institute of Applied Physics, Russian Academy of Sciences, ul. Ul'yanova 46, Nizhni Novgorod, 603950, Russia

Abstract: A mathematical model has been proposed for a neural network whose morphological structure varies dynamically depending on activity. This is the property of the so-called structural plasticity typical of developed neural systems of a brain. It has been shown that the spontaneous generation and propagation of a signal in such networks correspond to a percolation transition and the appearance of the connectivity component covering the entire system. Furthermore, adaptive change in the geometric structure of a network results in the clustering of cells and in the reduction of the effective percolation threshold, which corresponds to experimental neurobiological observations.

Received: 28.11.2014
Revised: 12.01.2015

DOI: 10.7868/S0370274X15040116

 English version: Journal of Experimental and Theoretical Physics Letters, 2015, 101:4, 271–275

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