Neural networks with excitatory and inhibitory components: direct and inverse problems by a mean-field approach

TL;DR

This paper analyzes neural network dynamics with excitatory and inhibitory neurons using a mean-field approach, revealing complex behaviors and solving an inverse problem to reconstruct network properties from activity data.

Contribution

It introduces a mean-field framework for studying mixed excitatory-inhibitory networks and provides a method to infer network structure from activity measurements.

Findings

Rich dynamical phase diagram as a function of inhibitory neuron fraction

Successful reconstruction of network degree distributions from activity data

New insights into neural network behavior and data analysis methods

Abstract

We study the dynamics of networks with inhibitory and excitatory leaky-integrate-and-fire neurons with short-term synaptic plasticity in the presence of depressive and facilitating mechanisms. The dynamics is analyzed by a Heterogeneous Mean-Field approximation, that allows to keep track of the effects of structural disorder in the network. We describe the complex behavior of different classes of excitatory and inhibitory components, that give rise to a rich dynamical phase-diagram as a function of the fraction of inhibitory neurons. By the same mean field approach, we study and solve a global inverse problem: reconstructing the degree probability distributions of the inhibitory and excitatory components and the fraction of inhibitory neurons from the knowledge of the average synaptic activity field. This approach unveils new perspectives in the numerical study of neural network dynamics and in the possibility of using these models as testbed for the analysis of experimental data.