  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 this effect by in-phase gamma
inputs compared to anti-phase inputs. Finally, we consider a multi-circuit
system comprised of two clusters, each containing a group of circuits receiving
a common noise input and a group of circuits receiving independent noise. Each
cluster is associated with its own local gamma generator, so all its circuits
receive gamma-band input in the same phase. We find that gamma-band input
differentially stabilizes the activity of the "common-noise" groups compared to
the "independent-noise" groups. If the inter-cluster connections are fast, this
effect is more pronounced when the gamma-band input is delivered to the
clusters in the same phase rather than in the anti-phase. Assuming that the
common noise comes from a large-scale distributed WM representation, our
results demonstrate that local gamma oscillations can stabilize the activity of
the corresponding parts of this representation, with stronger effect for fast
long-range connections and synchronized gamma oscillations.
