Abstract
We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by 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 via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the 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 organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed 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 the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.
Highlights
In neuroscience, the understanding of the functional role of astrocytes in the central nervous system (CNS) is still open to debate (Savtchouk and Volterra, 2018), but there is accumulating evidence demonstrating the involvement of astrocytes in local synaptic plasticity and the coordination of network activity (Durkee and Araque, 2019), and as a result in information processing and memory encoding (Santello et al, 2019)
Its duration determines the length of the delay interval in the delayed matching to sample (DMS) task, during which the item is maintained in the memory
The elevation of the frequency in the stimulus-specific neuronal population can continue after the end of the cue, which is determined by the duration of the astrocyte-induced enhancement of the synaptic weight
Summary
The understanding of the functional role of astrocytes in the central nervous system (CNS) is still open to debate (Savtchouk and Volterra, 2018), but there is accumulating evidence demonstrating the involvement of astrocytes in local synaptic plasticity and the coordination of network activity (Durkee and Araque, 2019), and as a result in information processing and memory encoding (Santello et al, 2019). Experimental evidence shows that astrocyte pathology in the medium prefrontal cortex (PFC) impairs WM and learning functions (Lima et al, 2014), increasing astrocyte density enhances short-term memory performance (Luca et al, 2020), and recognition memory performance and disruption of WM depend on gliotransmitter release from astrocytes in the hippocampus (Han et al, 2012; Robin et al, 2018) Despite these numerous experimental insights of the contribution of astrocytes to synaptic modulations in neuronal signaling, the possible role of astrocytes in information processing and learning is still a subject of discussion (Kanakov et al, 2019; Kastanenka et al, 2019)
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