Document Detail


Critical branching neural networks.
MedLine Citation:
PMID:  23356781     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical branching and, in doing so, simulates observed scaling laws as pervasive to neural and behavioral activity. These scaling laws are related to neural and cognitive functions, in that critical branching is shown to yield spiking activity with maximal memory and encoding capacities when analyzed using reservoir computing techniques. The model is also shown to account for findings of pervasive 1/f scaling in speech and cued response behaviors that are difficult to explain by isolable causes. Issues and questions raised by the model and its results are discussed from the perspectives of physics, neuroscience, computer and information sciences, and psychological and cognitive sciences. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
Authors:
Christopher T Kello
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Psychological review     Volume:  120     ISSN:  1939-1471     ISO Abbreviation:  Psychol Rev     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-01-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0376476     Medline TA:  Psychol Rev     Country:  United States    
Other Details:
Languages:  eng     Pagination:  230-54     Citation Subset:  IM    
Affiliation:
Cognitive and Information Sciences, School of Social Sciences, Humanities, and Arts, University of California.
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