Department of Mathematics, National Taiwan Normal University, 88 Section 4, Ting Chou Road, Taipei 11677, Taiwan
Copyright © 2011 Mau-Hsiang Shih and Feng-Sheng Tsai. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A major puzzle in neural networks is understanding the information
encoding principles that implement the functions of the brain systems.
Population coding in neurons and plastic changes in synapses are two important
subjects in attempts to explore such principles. This forms the basis
of modern theory of neuroscience concerning self-organization and associative
memory. Here we wish to suggest an information storage scheme based on
the dynamics of evolutionary neural networks, essentially reflecting the meta-complication
of the dynamical changes of neurons as well as plastic changes
of synapses. The information storage scheme may lead to the development of
a complete description of all the equilibrium states (fixed points) of Hopfield
networks, a space-filling network that weaves the intricate structure of Hamming
star-convexity, and a plasticity regime that encodes information based
on algorithmic Hebbian synaptic plasticity.