An attractor-based complexity measurement for Boolean recurrent neural networks.

ABSTRACT: We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamica

SUBMITTER: Cabessa J 

PROVIDER: S-EPMC3984152 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature