Dynamical mean-filed approximation to small-world networks of spiking neurons: From local to global, and/or from regular to random couplings
Hideo Hasegawa (Tokyo Gakugei Univ.)

TL;DR
This paper develops a semianalytical dynamical mean-field theory for small-world networks of noisy FitzHugh-Nagumo neurons, capturing effects of local to global and regular to random couplings on synchronization and firing precision.
Contribution
The paper extends a previous DMA to account for a wide range of couplings and correlations in small-world networks, transforming stochastic equations into a manageable deterministic form.
Findings
Synchronization worsens with increased randomness p.
Network distance shortens as p increases.
The theory agrees well with direct simulations.
Abstract
By extending a dynamical mean-field approximation (DMA) previously proposed by the author [H. Hasegawa, Phys. Rev. E {\bf 67}, 41903 (2003)], we have developed a semianalytical theory which takes into account a wide range of couplings in a small-world network. Our network consists of noisy -unit FitzHugh-Nagumo (FN) neurons with couplings whose average coordination number may change from local () to global couplings () and/or whose concentration of random couplings is allowed to vary from regular () to completely random (p=1). We have taken into account three kinds of spatial correlations: the on-site correlation, the correlation for a coupled pair and that for a pair without direct couplings. The original -dimensional {\it stochastic} differential equations are transformed to 13-dimensional {\it deterministic} differential equations expressed in…
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