Document Detail


Parallel and orthogonal stimulus in ultradiluted neural networks.
MedLine Citation:
PMID:  17155143     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Extending a model due to Derrida, Gardner, and Zippelius, we have studied the recognition ability of an extreme and asymmetrically diluted version of the Hopfield model for associative memory by including the effect of a stimulus in the dynamics of the system. We obtain exact results for the dynamic evolution of the average network superposition. The stimulus field was considered as proportional to the overlapping of the state of the system with a particular stimulated pattern. Two situations were analyzed, namely, the external stimulus acting on the initialization pattern (parallel stimulus) and the external stimulus acting on a pattern orthogonal to the initialization one (orthogonal stimulus). In both cases, we obtained the complete phase diagram in the parameter space composed of the stimulus field, thermal noise, and network capacity. Our results show that the system improves its recognition ability for parallel stimulus. For orthogonal stimulus two recognition phases emerge with the system locking at the initialization or stimulated pattern. We confront our analytical results with numerical simulations for the noiseless case T = 0.
Authors:
G A Sobral; V M Vieira; M L Lyra; C R da Silva
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Publication Detail:
Type:  Journal Article     Date:  2006-10-27
Journal Detail:
Title:  Physical review. E, Statistical, nonlinear, and soft matter physics     Volume:  74     ISSN:  1539-3755     ISO Abbreviation:  Phys Rev E Stat Nonlin Soft Matter Phys     Publication Date:  2006 Oct 
Date Detail:
Created Date:  2006-12-12     Completed Date:  2007-01-17     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101136452     Medline TA:  Phys Rev E Stat Nonlin Soft Matter Phys     Country:  United States    
Other Details:
Languages:  eng     Pagination:  046117     Citation Subset:  IM    
Affiliation:
Instituto de Física, Universidade Federal de Alagoas, 57072-970 Maceió-AL, Brazil.
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MeSH Terms
Descriptor/Qualifier:
Action Potentials / physiology*
Animals
Computer Simulation
Electric Stimulation*
Humans
Models, Neurological*
Nerve Net / physiology*
Synaptic Transmission / physiology*

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