An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

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 equiva...

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Detalles Bibliográficos
Autores principales: Cabessa, Jérémie, Villa, Alessandro E. P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984152/
https://www.ncbi.nlm.nih.gov/pubmed/24727866
http://dx.doi.org/10.1371/journal.pone.0094204

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984152/
https://www.ncbi.nlm.nih.gov/pubmed/24727866
http://dx.doi.org/10.1371/journal.pone.0094204

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