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A Model for Emergent Chaotic Order in Small Neural Networks

Andras, Peter

Authors

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Prof Peter Andras P.Andras@napier.ac.uk
Dean of School of Computing Engineering and the Built Environment



Abstract

A new neural network model is introduced in this paper. The aim of the proposed Sierpinski neural networks is to provide a simple and biologically plausible neural network architecture that produces emergent complex spatio-temporal patterns through the activity of the output neurons of the network and is able to perform computational tasks. Such networks may play an important role in the analysis and understanding of complex dynamic activity observed at various levels of biological neural systems. The proposed Sierpinski neural networks are described in detail and their functioning is analyzed. We discuss about emerging neural activity patterns and their interpretations, neuro-computation with such emerging activity patterns, and also possible implications for computational neuroscience.

Citation

Andras, P. (2003). A Model for Emergent Chaotic Order in Small Neural Networks. Journal of Integrative Neuroscience, 2(1), 55-69. https://doi.org/10.1142/S0219635203000172

Journal Article Type Article
Publication Date 2003
Deposit Date Nov 8, 2021
Print ISSN 0219-6352
Publisher World Scientific Publishing
Peer Reviewed Peer Reviewed
Volume 2
Issue 1
Pages 55-69
DOI https://doi.org/10.1142/S0219635203000172
Keywords Complex emergent behavior, dynamic patterns, neural network model, Sierpinski triangle, small neural network, spatio-temporal patterns
Public URL http://researchrepository.napier.ac.uk/Output/2809385