On the Dynamical Complexity of Small-World Networks of Spiking Neurons

TL;DR

This paper uses a computational model to investigate how small-world network topology influences the dynamical complexity of spiking neuron networks, revealing a narrow parameter range where complexity is maximized and inversely related to phase synchrony.

Contribution

It introduces a novel application of causal density to assess dynamical complexity in small-world spiking neuron networks, highlighting the impact of network topology.

Findings

Small-world topology promotes dynamical complexity within a narrow parameter range.

Dynamical complexity is inversely correlated with phase synchrony in this range.

The measure employed is sensitive to temporally smeared evidence of system integration.

Abstract

A computer model is described which is used to assess the dynamical complexity of a class of networks of spiking neurons with small-world properties. Networks are constructed by forming an initially segregated set of highly intra-connected clusters and then applying a probabilistic rewiring method reminiscent of the Watts-Strogatz procedure to make inter-cluster connections. Causal density, which counts the number of independent significant interactions among a system's components, is used to assess dynamical complexity. This measure was chosen because it employs lagged observations, and is therefore more sensitive to temporally smeared evidence of segregation and integration than its alternatives. The results broadly support the hypothesis that small-world topology promotes dynamical complexity, but reveal a narrow parameter range within which this occurs for the network topology under investigation, and suggest an inverse correlation with phase synchrony inside this range.