Macroscopic description for networks of spiking neurons

TL;DR

This paper derives exact macroscopic equations for networks of spiking neurons, linking microscopic neuron dynamics to collective network behavior, including synchronization, and connecting to low-dimensional Kuramoto models.

Contribution

It provides the first exact macroscopic equations for spiking neuron networks, capturing all dynamical states including synchronization, and relates firing rate to the Kuramoto order parameter.

Findings

Exact macroscopic equations derived for spiking neuron networks

Firing rate and membrane potential are coupled through neuron spike mechanisms

Connection established between firing rate dynamics and Kuramoto order parameter

Abstract

A major goal of neuroscience, statistical physics and nonlinear dynamics is to understand how brain function arises from the collective dynamics of networks of spiking neurons. This challenge has been chiefly addressed through large-scale numerical simulations. Alternatively, researchers have formulated mean-field theories to gain insight into macroscopic states of large neuronal networks in terms of the collective firing activity of the neurons, or the firing rate. However, these theories have not succeeded in establishing an exact correspondence between the firing rate of the network and the underlying microscopic state of the spiking neurons. This has largely constrained the range of applicability of such macroscopic descriptions, particularly when trying to describe neuronal synchronization. Here we provide the derivation of a set of exact macroscopic equations for a network of spiking neurons. Our results reveal that the spike generation mechanism of individual neurons introduces an effective coupling between two biophysically relevant macroscopic quantities, the firing rate and the mean membrane potential, which together govern the evolution of the neuronal network. The resulting equations exactly describe all possible macroscopic dynamical states of the network, including states of synchronous spiking activity. Finally we show that the firing rate description is related, via a conformal map, with a low-dimensional description in terms of the Kuramoto order parameter, called Ott-Antonsen theory. We anticipate our results will be an important tool in investigating how large networks of spiking neurons self-organize in time to process and encode information in the brain.