On strongly connected networks with excitable-refractory dynamics and delayed coupling.

Abstract:

:We consider a directed graph model for the human brain's neural architecture that is based on small scale, directed, strongly connected sub-graphs (SCGs) of neurons, that are connected together by a sparser mesoscopic network. We assume transmission delays within neuron-to-neuron stimulation, and that individual neurons have an excitable-refractory dynamic, with single firing 'spikes' occurring on a much faster time scale than that of the transmission delays. We demonstrate numerically that the SCGs typically have attractors that are equivalent to continual winding maps over relatively low-dimensional tori, thus representing a limit on the range of distinct behaviour. For a discrete formulation, we conduct a large-scale survey of SCGs of varying size, but with the same local structure. We demonstrate that there may be benefits (increased processing capacity and efficiency) in brains having evolved to have a larger number of small irreducible sub-graphs, rather than few, large irreducible sub-graphs. The network of SCGs could be thought of as an architecture that has evolved to create decisions in the light of partial or early incoming information. Hence the applicability of the proposed paradigm to underpinning human cognition.

journal_name

R Soc Open Sci

authors

Grindrod P,Lee TE

doi

10.1098/rsos.160912

subject

Has Abstract

pub_date

2017-04-05 00:00:00

pages

160912

issue

4

issn

2054-5703

pii

rsos160912

journal_volume

4

pub_type

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