Neurons and astrocytes interaction in neuronal network: A game-theoretic approach

• Published in: Journal of theoretical biology 2019-06, Vol.470, p.76-89
• Main Authors: Zareh, Masoumeh, Manshaei, Mohammad Hossein, Adibi, Mehdi, Montazeri, Mohammad Ali
• Format: Article
• Language: English
• Subjects: Astrocyte; Bayesian game; Chemical synapse; Dynamic game; Morris-Lecar model; Neural network; Neuron
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2019.02.024

•We model neuron and astrocytes interactions under normal and abnormal conditions using dynamic and Bayesian games.•We analyze the functionality of normal and abnormal neurons, using Bayesian game.•We investigate the existence of Nash equilibrium in our proposed models.•Our model explains how neurons treat together in various conditions.•Using our proposed model, we investigate the functionality of neurons and astrocytes in epileptic patients. A neuron is the fundamental unit of the nervous system and the brain, crucial for transducing information in form of trains of electrical pulses known as action potentials. The connection between neurons is through synapses, enabling communication between neurons. This communication link is one of the key elements in processing of information from a neuron to another neuron. The strength of the synapses may vary over time, a phenomenon known as synaptic plasticity. This is the process by which it is believed memory and learning is governed. Recent studies revealed environmental factors affect the strength of synapses, and the way neurons communicate to each other. This poses the question as to what extent the pre- and post- synaptic neurons sense the environmental changes, and in turn adjust their synaptic link. Here, we model the behavior of an interconnected neuronal network in various environmental conditions as a multi-agent system in a game theoretic framework. We focus on a CA1 lattice subfield as an example plastic neuronal network. Our analysis revealed the neuronal network converges to different equilibria depending on the environmental changes. The model well-predicts the behavior of the network compared to a well-known theoretical model of individual neurons.