  We propose a biologically plausible computational model of working memory
(WM) implemented by the spiking neuron network (SNN) interacting with a network
of astrocytes. SNN is modelled by the synaptically coupled Izhikevich neurons
with a non-specific architecture connection topology. Astrocytes generating
calcium signals are connected by local gap junction diffusive couplings and
interact with neurons by chemicals diffused in the extracellular space. Calcium
elevations occur in response to the increase of concentration of a
neurotransmitter released by spiking neurons when a group of them fire
coherently. In turn, gliotransmitters are released by activated astrocytes
modulating the strengths of synaptic connections in the corresponding neuronal
group. Input information is encoded as two-dimensional patterns of short
applied current pulses stimulating neurons. The output is taken from
frequencies of transient discharges of corresponding neurons. We show how a set
of information patterns with quite significant overlapping areas can be
uploaded into the neuron-astrocyte network and stored for several seconds.
Information retrieval is organised by the application of a cue pattern
representing the one from the memory set distorted by noise. We found that
successful retrieval with level of the correlation between recalled pattern and
ideal pattern more than 90% is possible for multi-item WM task. Having analysed
the dynamical mechanism of WM formation, we discovered that astrocytes
operating at a time scale of a dozen of seconds can successfully store traces
of neuronal activations corresponding to information patterns. In the retrieval
stage, the astrocytic network selectively modulates synaptic connections in SNN
leading to the successful recall. Information and dynamical characteristics of
the proposed WM model agrees with classical concepts and other WM models.
