A model for emergent complex order in small neural networks

Andras, Peter

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 complex 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
• Journal: Journal of integrative neuroscience
• Print ISSN: 0219-6352
• Electronic ISSN: 1757-448X
• 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/2808849