A Model for Emergent Chaotic Order in Small Neural Networks
Péter András · 2004
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.