Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

TL;DR

This study investigates how spontaneous activity patterns in random neural networks depend on synaptic noise and inhibitory strength, revealing mechanisms behind cortical oscillations and state transitions.

Contribution

It introduces a comprehensive model of mixed neuron types with conductance-based synapses and noise, elucidating how noise transforms transient activity into persistent oscillatory or asynchronous states.

Findings

Noise induces persistent activity patterns in neural networks.

Oscillatory and asynchronous states depend on inhibitory synaptic strength.

Networks exhibit state switching similar to cortical activity during sleep and wakefulness.

Abstract

Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.