  Modeling the neuronal processes underlying short-term working memory remains
the focus of many theoretical studies in neuroscience. Here we propose a
mathematical model of spiking neuron network (SNN) demonstrating how a piece of
information can be maintained as a robust activity pattern for several seconds
then completely disappear if no other stimuli come. Such short-term memory
traces are preserved due to the activation of astrocytes accompanying the SNN.
The astrocytes exhibit calcium transients at a time scale of seconds. These
transients further modulate the efficiency of synaptic transmission and, hence,
the firing rate of neighboring neurons at diverse timescales through
gliotransmitter release. We show how such transients continuously encode
frequencies of neuronal discharges and provide robust short-term storage of
analogous information. This kind of short-term memory can keep operative
information for seconds, then completely forget it to avoid overlapping with
forthcoming patterns. The SNN is inter-connected with the astrocytic layer by
local inter-cellular diffusive connections. The astrocytes are activated only
when the neighboring neurons fire quite synchronously, e.g. when an information
pattern is loaded. For illustration, we took greyscale photos of people's faces
where the grey level encoded the level of applied current stimulating the
neurons. The astrocyte feedback modulates (facilitates) synaptic transmission
by varying the frequency of neuronal firing. We show how arbitrary patterns can
be loaded, then stored for a certain interval of time, and retrieved if the
appropriate clue pattern is applied to the input.

Neurons and Cognition;Artificial Intelligence