Transition from asynchronous to oscillatory dynamics in balanced spiking networks with instantaneous synapses

TL;DR

This paper investigates how balanced inhibitory and excitatory neural networks transition from asynchronous to oscillatory activity, revealing the underlying mechanisms through mean-field models and network connectivity analysis.

Contribution

It demonstrates the emergence of collective oscillations in balanced neural networks with instantaneous synapses and explains their origin via a mean-field approach.

Findings

Oscillations occur in sufficiently connected networks.

Microscopic irregular firings trigger sustained oscillations.

Mechanism applies to both inhibitory and excitatory-inhibitory networks.

Abstract

We report a transition from asynchronous to oscillatory behaviour in balanced inhibitory networks for class I and II neurons with instantaneous synapses. Collective oscillations emerge for sufficiently connected networks. Their origin is understood in terms of a recently developed mean-field model, whose stable solution is a focus. Microscopic irregular firings, due to balance, trigger sustained oscillations by exciting the relaxation dynamics towards the macroscopic focus. The same mechanism induces in balanced excitatory-inhibitory networks quasi-periodic collective oscillations.