Activity stabilization in a population model of working memory by sinusoidal and noisy inputs
Nikita Novikov, Denis Zakharov, Victoria Moiseeva, Boris Gutkin

TL;DR
This study investigates how gamma-band oscillations and noise influence the stability of persistent neural activity in working memory models, highlighting the roles of coupling, phase, and input type in activity stabilization.
Contribution
It introduces a multi-scale neural model demonstrating how gamma oscillations and noise stabilize working memory activity, emphasizing the importance of coupling and phase synchronization.
Findings
Gamma oscillations and noise stabilize WM activity.
Fast inter-circuit coupling enhances stabilization.
In-phase gamma inputs are more effective than anti-phase.
Abstract
According to mechanistic theories of working memory (WM), information is retained as persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains an open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e. they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of…
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