Metastability in networks of nonlinear stochastic integrate-and-fire neurons

TL;DR

This paper investigates how nonlinear properties of individual neurons influence the collective activity in neural networks, revealing that such nonlinearities can create metastable states and modulate firing rates.

Contribution

It introduces a field-theoretic approach to analyze a stochastic integrate-and-fire neuron model, highlighting the role of nonlinearities in emergent network dynamics.

Findings

Nonlinear spike intensity functions can induce metastable active states.

Membrane potential resets interact with nonlinearities to modulate firing rates.

Network activity can be enhanced or suppressed depending on nonlinear properties.

Abstract

Neurons in the brain continuously process the barrage of sensory inputs they receive from the environment. A wide array of experimental work has shown that the collective activity of neural populations encodes and processes this constant bombardment of information. How these collective patterns of activity depend on single-neuron properties is often unclear. Single-neuron recordings have shown that individual neurons' responses to inputs are nonlinear, which prevents a straightforward extrapolation from single neuron features to emergent collective states. Here, we use a field-theoretic formulation of a stochastic leaky integrate-and-fire model to study the impact of single-neuron nonlinearities on macroscopic network activity. In this model, a neuron integrates spiking output from other neurons in its membrane voltage and emits spikes stochastically with an intensity depending on the membrane voltage, after which the voltage resets. We show that the interplay between nonlinear spike intensity functions and membrane potential resets can i) give rise to metastable active firing rate states in recurrent networks, and ii) can enhance or suppress mean firing rates and membrane potentials in the same or paradoxically opposite directions.