| Genetic algorithm applied to hierarchically coupled associative memories. | |
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MedLine Citation:
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PMID: 20020348 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Inspired by the theory of neuronal group selection (TNGS), we have carried out an analysis of the capacity of convergence of a multi-level associative memory based on coupled generalized-brain-state-in-a-box (GBSB) networks through evolutionary computation. The TNGS establishes that a memory process can be described as being organized functionally in hierarchical levels where higher levels coordinate sets of functions of lower levels. According to this theory, the most basic units in the cortical area of the brain are called neuronal groups or first-level blocks of memories and the higher-level memories are formed through selective strengthening or weakening of the synapses amongst the neuronal groups. In order to analyse this effect, we propose that the higher levels should emerge through a learning mechanism as correlations of lower level memories. According to this proposal, this paper describes a method of acquiring the inter-group synapses based on a genetic algorithm. Thus the results show that genetic algorithms are feasible as they allow the emergence of complex behaviours which could be potentially excluded in other learning process. |
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Authors:
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Rog?rio Martins Gomes; Ant?nio P?dua Braga; Henrique E Borges |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Advances in experimental medicine and biology Volume: 657 ISSN: 0065-2598 ISO Abbreviation: Adv. Exp. Med. Biol. Publication Date: 2010 |
Date Detail:
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Created Date: 2009-12-18 Completed Date: 2010-04-08 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0121103 Medline TA: Adv Exp Med Biol Country: United States |
Other Details:
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Languages: eng Pagination: 187-99 Citation Subset: IM |
Affiliation:
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CEFET-MG, Av. Amazonas 7675, Belo Horizonte, MG, CEP 30510-000, Brazil. rogerio@lsi.cefetmg.br |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Association Learning / physiology* Computer Simulation Humans Memory / physiology* Models, Neurological* Neural Networks (Computer) |
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