Spatial complexity in multi-layer cellular neural networks

Jung Chao Ban*, Chih Hung Chang, Song-Sun Lin, Yin Heng Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study investigates the complexity of the global set of output patterns for one-dimensional multi-layer cellular neural networks with input. Applying labeling to the output space produces a sofic shift space. Two invariants, namely spatial entropy and dynamical zeta function, can be exactly computed by studying the induced sofic shift space. This study gives sofic shift a realization through a realistic model. Furthermore, a new phenomenon, the broken of symmetry of entropy, is discovered in multi-layer cellular neural networks with input.

Original languageEnglish
Title of host publication2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010
DOIs
StatePublished - 21 May 2010
Event2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010 - Berkeley, CA, United States
Duration: 3 Feb 20105 Feb 2010

Publication series

Name2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010

Conference

Conference2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010
CountryUnited States
CityBerkeley, CA
Period3/02/105/02/10

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  • Cite this

    Ban, J. C., Chang, C. H., Lin, S-S., & Lin, Y. H. (2010). Spatial complexity in multi-layer cellular neural networks. In 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010 [5430257] (2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010). https://doi.org/10.1109/CNNA.2010.5430257