On the spatial entropy and patterns of two-dimensional cellular neural networks

Song-Sun Lin*, Tzi Sheng Yang

*Corresponding author for this work

Research output: Contribution to journalArticle

13 Scopus citations


This work investigates binary pattern formations of two-dimensional standard cellular neural networks (CNN) as well as the complexity of the binary patterns. The complexity is measured by the exponential growth rate in which the patterns grow as the size of the lattice increases, i.e. spatial entropy. We propose an algorithm to generate the patterns in the finite lattice for general two-dimensional CNN. For the simplest two-dimensional template, the parameter space is split up into finitely many regions which give rise to different binary patterns. Qualitatively, the global patterns are classified for each region. Quantitatively, the upper bound of the spatial entropy is estimated by computing the number of patterns in the finite lattice, and the lower bound is given by observing a maximal set of patterns of a suitable size which can be adjacent to each other.

Original languageEnglish
Pages (from-to)115-128
Number of pages14
JournalInternational Journal of Bifurcation and Chaos in Applied Sciences and Engineering
Issue number1
StatePublished - 1 Jan 2002

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