Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity

TL;DR

This paper develops and compares mean-field models for networks of quadratic integrate-and-fire neurons with short-term synaptic plasticity, revealing their dynamic regimes and providing tools for analyzing complex neural activity.

Contribution

It introduces two new mean-field approaches for networks with pre-synaptic short-term plasticity and compares them to existing stochastic models, improving accuracy in describing network dynamics.

Findings

Mean-field models accurately capture bursting and bistability.

Pre-synaptic plasticity influences macroscopic network behavior.

Proposed models outperform stochastic spike timing approximations.

Abstract

Low-dimensional descriptions of neural network dynamics are an effective tool for bridging different scales of organization of brain structure and function. Recent advances in deriving mean-field descriptions for networks of coupled oscillators have sparked the development of a new generation of neural mass models. Of notable interest are mean-field descriptions of all-to-all coupled quadratic integrate-and-fire (QIF) neurons, which have already seen numerous extensions and applications. These extensions include different forms of short-term adaptation (STA) considered to play an important role in generating and sustaining dynamic regimes of interest in the brain. It is an open question, however, whether the incorporation of pre-synaptic forms of synaptic plasticity driven by single neuron activity would still permit the derivation of mean-field equations using the same method. Here, we discuss this problem using an established model of short-term synaptic plasticity at the single neuron level, for which we present two different approaches for the derivation of the mean-field equations. We compare these models with a recently proposed mean-field approximation that assumes stochastic spike timings. In general, the latter fails to accurately reproduce the macroscopic activity in networks of deterministic QIF neurons with distributed parameters. We show that the mean-field models we propose provide a more accurate description of the network dynamics, although they are mathematically more involved. Using bifurcation analysis, we find that QIF networks with pre-synaptic short-term plasticity can express regimes of periodic bursting activity as well as bi-stable regimes. Together, we provide novel insight into the macroscopic effects of short-term synaptic plasticity in spiking neural networks, as well as two different mean-field descriptions for future investigations of such networks.