Collective behavior of heterogeneous neural networks

TL;DR

This paper studies a network of heterogeneous integrate-and-fire neurons, revealing a transition to complex collective behavior with irregular macroscopic dynamics and stable microscopic evolution as coupling strength increases.

Contribution

It demonstrates that such neural networks exhibit irregular collective dynamics and stable individual neuron behavior, contrasting with classical models like Kuramoto.

Findings

Transition from asynchronous to collective behavior with increased coupling

Irregular macroscopic dynamics persist in large networks

Microscopic neuron dynamics remain linearly stable

Abstract

We investigate a network of integrate-and-fire neurons characterized by a distribution of spiking frequencies. Upon increasing the coupling strength, the model exhibits a transition from an asynchronous regime to a nontrivial collective behavior. At variance with the Kuramoto model, (i) the macroscopic dynamics is irregular even in the thermodynamic limit, and (ii) the microscopic (single-neuron) evolution is linearly stable.