The interplay of inhibitory and electrical synapses results in complex persistent activity

TL;DR

This paper investigates how the combined nonlinear effects of electrical and inhibitory chemical synapses in neuronal networks lead to complex persistent activity, including oscillations and chaos, with implications for memory maintenance.

Contribution

It identifies parametric conditions for self-sustained oscillations in coupled neurons considering both electrical and inhibitory synapses, revealing complex dynamics in minimal and ring network models.

Findings

Strong coupling induces nonlinear interactions between synapses.

Networks exhibit periodic, complex, and chaotic oscillations.

Optimal conditions for sustained activity are determined.

Abstract

Inhibitory neurons play a crucial role in maintaining persistent neuronal activity. Although connected extensively through electrical synapses (gap-junctions), these neurons also exhibit interactions through chemical synapses in certain regions of the brain. When the coupling is sufficiently strong, the effects of these two synaptic modalities combine in a nonlinear way. Hence, in this work, we focus on the strong inhibition regime and identify the parametric conditions that result in the emergence of self-sustained oscillations in systems of coupled excitable neurons, in the presence of a brief sub-threshold stimulus. Our investigation on the dynamics in a minimal network of two neurons reveals a rich set of dynamical behaviors viz., periodic and various complex oscillations including period-n (n=2,4,8...) dynamics and chaos. We further extend our study by considering a system of inhibitory neurons arranged in a one-dimensional ring topology and determine the optimal conditions for sustained activity. Our work highlights the nonlinear dynamical behavior arising due to the combined effects of gap-junctions and strong synaptic inhibition, which can have potential implications in maintaining robust memory patterns.