A dynamical network approach to uncovering hidden causality relationships in collective neuron firings

TL;DR

This paper introduces a dynamical network framework to analyze collective neuron firing patterns, revealing hidden causal relationships through topological and correlation-based network analysis.

Contribution

It presents a novel approach combining dynamical systems and complex network theory to uncover hidden causality in neuronal firing data.

Findings

Network links reflect correlated neuron firings

Topological features reveal collective behavior

Correlation measures effectively weight network connections

Abstract

We analyze the synchronous firings of the salamander ganglion cells from the perspective of the complex network viewpoint where the network's links reflect the correlated behavior of firings. We study the time-aggregated properties of the resulting network focusing on its topological features. The behavior of pairwise correlations has been inspected in order to construct an appropriate measure that will serve as a weight of network connection.