Spatial evolution of Hindmarsh-Rose neural network with time delays

Michał Łepek , Piotr Fronczak


Spatial relations between neurons in the network with time delays play a crucial role in determining dynamics of the system. During the development of the nervous system, different types of neurons group together to enable specific functions of the network. Right spatial distances, thus right time delays between cells are crucial for an appropriate functioning of the system. To model the process of neural migration, we proposed simple but effective model of network spatial evolution based on Hindmarsh-Rose neurons and Metropolis-Hastings Monte Carlo algorithm. Under the specific assumptions and using appropriate parameters of the neural evolution, the network can converge to the desirable state giving the opportunity of achieving large variety of spectra. We show that there is a specific range of network size in space which allows it to generate assumed output. A network or generally speaking a system with time delays (corresponding to the arrangement in the physical space) of specific output properties has a specific spatial dimension that allows it to function properly.
Author Michał Łepek (FP / PCSD)
Michał Łepek,,
- Physics of Complex Systems Divison
, Piotr Fronczak (FP / PCSD)
Piotr Fronczak,,
- Physics of Complex Systems Divison
Journal seriesNonlinear Dynamics, ISSN 0924-090X, e-ISSN 1573-269X, (A 40 pkt)
Issue year2018
Publication size in sheets0.5
Keywords in EnglishNeural network; Spatial evolution; Hindmarsh-Rose neuron; Time delays; Monte Carlo algorithm
ASJC Classification2208 Electrical and Electronic Engineering; 2604 Applied Mathematics; 2210 Mechanical Engineering; 2212 Ocean Engineering; 2202 Aerospace Engineering; 2207 Control and Systems Engineering
Languageen angielski
Score (nominal)45
ScoreMinisterial score = 40.0, 19-11-2018, ArticleFromJournal
Ministerial score (2013-2016) = 45.0, 19-11-2018, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.523; WoS Impact Factor: 2017 = 4.339 (2) - 2017=3.906 (5)
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