Abstract
Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears if no other stimuli are fed to the system. 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 demonstrate 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 store relevant information for seconds and 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 synchronously, e.g., when an information pattern is loaded. For illustration, we took grayscale photographs of people’s faces where the shades of gray correspond to the level of applied current which stimulates 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.
Highlights
Understanding principles of information processing in the brain remains one of the primary challenges of neuroscience [1, 2]
We propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears if no other stimuli are fed to the system
To evaluate the robustness to noise of the proposed neuron–astrocyte network model, we investigated the dependence of the quality of model retrieval on the noise level in the test image
Summary
Understanding principles of information processing in the brain remains one of the primary challenges of neuroscience [1, 2]. Working memory is believed to be ‘‘encoded’’ by changes in the strengths of synaptic connections, e.g., synaptic plasticity [4, 11, 12] These changes determine, which particular neuronal clusters or signal transmission pathways that encode the information should be memorized. Several studies discuss the role of Neural Computing and Applications astrocytes in the perception of sensory stimuli [21,22,23,24], spatio-temporal coordination of neural network signaling [25,26,27,28,29,30], information processing, and cognitive functions [31,32,33]. Astrocytes act as the third part of the so-called tripartite synapses [37, 38]
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