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According to mechanistic theories of working memory (WM), information isretained as persistent spiking activity of cortical neural networks. Yet, howthis activity is related to changes in the oscillatory profile observed duringWM tasks remains an open issue. We explore joint effects of input gamma-bandoscillations and noise on the dynamics of several firing rate models of WM. Theconsidered models have a metastable active regime, i.e. they demonstratelong-lasting transient post-stimulus firing rate elevation. We start from asingle excitatory-inhibitory circuit and demonstrate that either gamma-band ornoise input could stabilize the active regime, thus supporting WM retention. Wethen consider a system of two circuits with excitatory intercoupling. We findthat fast coupling allows for better stabilization by common noise compared toindependent noise and stronger amplification of this effect by in-phase gammainputs compared to anti-phase inputs. Finally, we consider a multi-circuitsystem comprised of two clusters, each containing a group of circuits receivinga common noise input and a group of circuits receiving independent noise. Eachcluster is associated with its own local gamma generator, so all its circuitsreceive gamma-band input in the same phase. We find that gamma-band inputdifferentially stabilizes the activity of the "common-noise" groups compared tothe "independent-noise" groups. If the inter-cluster connections are fast, thiseffect is more pronounced when the gamma-band input is delivered to theclusters in the same phase rather than in the anti-phase. Assuming that thecommon noise comes from a large-scale distributed WM representation, ourresults demonstrate that local gamma oscillations can stabilize the activity ofthe corresponding parts of this representation, with stronger effect for fastlong-range connections and synchronized gamma oscillations.

Activity stabilization in a population model of working memory by sinusoidal and noisy inputs