Dynamics in the Reduced Mean-Field Model of Neuron–Glial Interaction

Sergey M. Olenin, Tatiana A. Levanova () and Sergey V. Stasenko ()
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Sergey M. Olenin: Control Theory Department, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
Tatiana A. Levanova: Control Theory Department, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
Sergey V. Stasenko: Control Theory Department, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia

Mathematics, 2023, vol. 11, issue 9, 1-11

Abstract: The goal of this study is to propose a new reduced phenomenological model that describes the mean-field dynamics arising from neuron–glial interaction, taking into account short-term synaptic plasticity and recurrent connections in the presence of astrocytic modulation of the synaptic connection. Using computer simulation and numerical methods of nonlinear dynamics, it is shown that the proposed model reproduces a rich set of patterns of population activity, including spiking, bursting and chaotic temporal patterns. These patterns can coexist for specific regions in the parameter space of the model. The main focus of this study was on bifurcation mechanisms that lead to the occurrence of the described types of mean-field dynamics. The proposed phenomenological model can be used to reproduce various patterns of population activity of neurons in a wide range of studies of dynamic memory and information processing. One of the possible applications of such research is the development of new effective methods for the treatment of neurological diseases associated with neuron–glial interactions.

Keywords: neuron; astrocyte; tripartite synapse; spiking dynamics; bursting dynamics; chaos (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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