serval:BIB_2CD19842304A
An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks
10.1371/journal.pone.0094204
000336736200044
24727866
Cabessa
J.
author
Villa
A.E.P.
author
article
2014-04-11
PLoS ONE
1932-6203
journal
9
4
e94204
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 -automata, and then translating the most refined classification of -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 dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.
General Biochemistry, Genetics and Molecular Biology
General Agricultural and Biological Sciences
General Medicine
eng
60_published
true
peer-reviewed
University of Lausanne
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