Mathematical Models of Dynamic Behavior of Individual Neural Networks of Central Nervous System

We present mathematical models that describe individual neural networks of the Central Nervous System. Three cases are examined, varying in each case the values of the refractory period and the synaptic delay of a neuron. In the case where both the refractory period and the synaptic delay are bigger than one, we split the population of neurons into sub-groups with their own distinct synaptic delay. It is shown that the proposed approach describes the neural activity of the network efficiently, especially in the case mentioned above. Various examples with different network parameters are presented to investigate the network’s behavior.

Authors and Affiliations

1. Pattern Recognition Laboratory, Dept. of Computer Engineering & Informatics, University of Patras, 26500, Patras, Greece

   Dimitra-Despoina Pagania, Adam Adamopoulos & Spiridon D. Likothanassis

2. Dept. of Medicine, Democritus University of Thrace, Alexandroupolis, Greece

   Adam Adamopoulos

Editors and Affiliations

1. Democritus University of Thrace, 68200 N., Orestiada, Greece

   Lazaros Iliadis

2. London Metropolitan University, N7 8DB, London, UK

   Chrisina Jayne

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