Entropy bifurcation of neural networks on Cayley trees
Abstract
It has been demonstrated that excitable media with a tree structure performed better than other network topologies, it is natural to consider neural networks defined on Cayley trees. The investigation of a symbolic space called tree-shift of finite type is important when it comes to the discussion of the equilibrium solutions of neural networks on Cayley trees. Entropy is a frequently used invariant for measuring the complexity of a system, and constant entropy for an open set of coupling weights between neurons means that the specific network is stable. This paper gives a complete characterization for entropy spectrum of neural networks on Cayley trees and reveals whether the entropy bifurcates when the coupling weights change.
Keywords
Cite
@article{arxiv.1706.09283,
title = {Entropy bifurcation of neural networks on Cayley trees},
author = {Jung-Chao Ban and Chih-Hung Chang and Nai-Zhu Huang},
journal= {arXiv preprint arXiv:1706.09283},
year = {2018}
}
Comments
arXiv admin note: text overlap with arXiv:1701.05113