A model for emergent complex order in small neural networks.
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.
Andras
Peter
P
Claremont Tower, School of Computing Science, University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK. peter.andras@ncl.ac.uk
D000818:Animals; D004594:Electrophysiology; D006801:Humans; D009433:Neural Inhibition; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology