Document Detail

Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity.
MedLine Citation:
PMID:  18928369     Owner:  NLM     Status:  MEDLINE    
Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.
Takuma Tanaka; Takeshi Kaneko; Toshio Aoyagi
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Neural computation     Volume:  21     ISSN:  0899-7667     ISO Abbreviation:  Neural Comput     Publication Date:  2009 Apr 
Date Detail:
Created Date:  2009-03-23     Completed Date:  2009-05-11     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9426182     Medline TA:  Neural Comput     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1038-67     Citation Subset:  IM    
Department of Morphological Brain Science, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan.
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MeSH Terms
Central Nervous System / cytology,  physiology
Models, Neurological*
Neuronal Plasticity / physiology
Neurons / physiology*

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