Spatial complexity in multi-layer cellular neural networks
Jung‐Chao Ban, Chih-Hung Chang, Song-Sun Lin, Yin-Heng Lin · 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) · 2010
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.