Impact of Astrocytic Coverage of Synapses on the Short-Term Memory of a Computational Neuron-Astrocyte Network

Abstract

Working memory refers to the capability of the nervous system to selectively retain short-term memories in an active state. The long-standing viewpoint is that neurons play an indispensable role and working memory is encoded by synaptic plasticity. Furthermore, some recent studies have shown that calcium signaling assists the memory processes and the working memory might be affected by the astrocyte density. Over the last few decades, growing evidence has also revealed that astrocytes exhibit diverse coverage of synapses which are considered to participate in neuronal activities. However, very little effort has yet been made to attempt to shed light on the potential correlations between these observations. Hence, in this article, we leverage a computational neuron–astrocyte model to study the short-term memory performance subject to various astrocytic coverage and we demonstrate that the short-term memory is susceptible to this factor. Our model may also provide plausible hypotheses for the various sizes of calcium events as they are reckoned to be correlated with the astrocytic coverage.

Novel efficient reservoir computing methodologies for regular and irregular time series classification

“…Complex dynamical systems have demonstrated colossal potential in learning and computation in a wide spectrum of frameworks such as gene regulatory networks, cellular networks and artificial neural networks [12][13][14][15][16]. Among them, Reservoir Computing (RC) stands at the forefront of cuttingedge research in the field of machine learning and artificial intelligence, providing a promising approach to the challenges of processing complex temporal data [17][18][19].…”