Efficiency characterization of a large neuronal network: a causal information approach

TL;DR

This paper analyzes a cortical neuronal network with inhibitory neurons using causal information measures to understand how network connectivity influences emergent dynamics and information saturation.

Contribution

It introduces a novel approach applying causal information measures to characterize the efficiency and emergent properties of large neuronal networks.

Findings

Information measures saturate as connectivity increases

Causal information captures subtle temporal structures in neuronal signals

Emergent dynamics depend on the degree of interconnectivity

Abstract

When inhibitory neurons constitute about 40% of neurons they could have an important antinociceptive role, as they would easily regulate the level of activity of other neurons. We consider a simple network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticity, representative of a cortical column or hypercolumn with large proportion of inhibitory neurons. Each neuron fires following a Hodgkin-Huxley like dynamics and it is interconnected randomly to other neurons. The network dynamics is investigated estimating Bandt and Pompe probability distribution function associated to the interspike intervals and taking different degrees of inter-connectivity across neurons. More specifically we take into account the fine temporal ``structures'' of the complex neuronal signals not just by using the probability distributions associated to the inter spike intervals, but instead considering much more subtle measures accounting for their causal information: the Shannon permutation entropy, Fisher permutation information and permutation statistical complexity. This allows us to investigate how the information of the system might saturate to a finite value as the degree of inter-connectivity across neurons grows, inferring the emergent dynamical properties of the system.