Document Detail


The effect of molecular inhibition on evolutionary learning: studies in the hypernetwork architecture.
MedLine Citation:
PMID:  12595117     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The hypernetwork architecture is a biologically inspired learning model based on abstract molecules and molecular interactions that exhibits functional and organizational correlation with biological systems. Hypernetwork organisms were trained, by molecular evolution, to solve N-input parity tasks. We found that learning improves when molecules exhibit inhibitory sites, allowing molecular inhibition and opening the possibility of forming negative feedback regulatory pathways. Optimal learning is achieved when at least 20% of the molecules in each cell have inhibitory sites. Intra-cellular as well as inter-cellular molecular inhibitions play an important role in the information processing of hypernetwork organisms, by maintaining a balance of the molecular cascade reactions. Similar mechanisms inside neurons are considered important for memory.
Authors:
Jose L Segovia-Juarez; Silvano Colombano
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Bio Systems     Volume:  68     ISSN:  0303-2647     ISO Abbreviation:  BioSystems     Publication Date:    2003 Feb-Mar
Date Detail:
Created Date:  2003-02-21     Completed Date:  2003-11-18     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0430773     Medline TA:  Biosystems     Country:  Ireland    
Other Details:
Languages:  eng     Pagination:  187-98     Citation Subset:  IM    
Copyright Information:
Copyright 2002 Elsevier Science Ireland Ltd.
Affiliation:
Department of Computer Science, Biocomputing Laboratory, Wayne State University, 431 State Hall, Detroit, MI 48202, USA. jls@cs.wyane.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Evolution*
Learning
Models, Biological*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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