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Hamming Star-Convexity Packing in Information Storage

Abstract

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.

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Correspondence to Feng-Sheng Tsai.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Shih, M., Tsai, F. Hamming Star-Convexity Packing in Information Storage. Fixed Point Theory Appl 2011, 615274 (2011). https://doi.org/10.1155/2011/615274

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Keywords

  • Neural Network
  • Equilibrium State
  • Synaptic Plasticity
  • Dynamical Change
  • Differential Geometry