Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation

TL;DR

This paper investigates how long-range excitation influences gamma oscillations in coupled neural networks, revealing a transition from synchronized oscillations to chaos and fast decorrelation, with implications for understanding neural dynamics.

Contribution

It demonstrates how increasing long-range excitation causes a transition from phase-locked oscillations to chaos and decorrelation, across different neural network models.

Findings

Weak excitation leads to phase-locked gamma oscillations.

Strong excitation induces chaos and decorrelation.

Results are consistent across firing-rate and conductance-based models.

Abstract

We consider two neuronal networks coupled by long-range excitatory interactions. Oscillations in the gamma frequency band are generated within each network by local inhibition. When long-range excitation is weak, these oscillations phase-lock with a phase-shift dependent on the strength of local inhibition. Increasing the strength of long-range excitation induces a transition to chaos via period-doubling or quasi-periodic scenarios. In the chaotic regime oscillatory activity undergoes fast temporal decorrelation. The generality of these dynamical properties is assessed in firing-rate models as well as in large networks of conductance-based neurons.