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A Model for Emergent Chaotic Order in Small Neural Networks
 

Summary: A Model for Emergent Chaotic Order in Small
Neural Networks
Peter Andras
School of Computing Science
University of Newcastle
Newcastle upon Tyne, NE1 7RU, UK
Abstract
A new neural network model is introduced in this paper. The aim of the pro-
posed Sierpinski neural networks is to provide a simple and biologically plausi-
ble neural network architecture that produces emergent complex spatio-temporal
patterns through the activity of the output neurons of the network. Such net-
works can play an important role in the analysis and understanding of complex
dynamic activity observed at various levels of biological neural systems. The pro-
posed Sierpinski neural networks are described in detail and their functioning is
analysed mathematicaly to show that they indeed produce Sierpinski triangles as
the spatio-temporal activity patterns of their output neurons. The paper briefly
discusses generalizations of the proposed neural networks, aspects of their biolog-
ically plausible realization, and their implication to the understanding of the role
of biological neural chaos.
Keywords: bio-plausible model, chaos, complex behavior, dynamic patterns,

  

Source: Andras, Peter - School of Computing Science, University of Newcastle upon Tyne

 

Collections: Computer Technologies and Information Sciences