Shot noise in next-generation neural mass models

TL;DR

This paper investigates how finite network size introduces shot noise into next-generation neural mass models, affecting their collective dynamics and resonance phenomena, with implications for understanding brain rhythms and activity patterns.

Contribution

It demonstrates that finite-size effects can be modeled as shot noise in neural mass models, revealing their influence on neural dynamics and resonance effects.

Findings

Shot noise appears as a stochastic term in neural mass models due to finite network size.

Power spectrum analysis shows peaks at frequencies near the mean firing rate.

Shot noise can significantly influence collective neural behavior despite being weak in large networks.

Abstract

Recently, the so-called next-generation neural mass models have received a lot of attention of the researchers in the field of mathematical neuroscience. The ability of these models to account for the degree of synchrony in neural populations proved useful in many contexts such as the modeling of brain rhythms, working memory and spatio-temporal patterns of activity. In the present Letter we study the effects of finite size on the collective behaviour of neural networks and show that they can be captured by appropriately modified neural mass models. Namely, the finite size of the network leads to the emergence of the shot noise appearing as a stochastic term in the neural mass model. We calculate the power spectrum of the shot noise and show that it might demonstrate pronounced peaks in the frequencies comparable to the mean firing rate. Although the shot noise is weak in large massively connected networks, its impact on the collective dynamics might be crucial due to resonance effects.