| The effect of molecular inhibition on evolutionary learning: studies in the hypernetwork architecture. | |
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MedLine Citation:
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PMID: 12595117 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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Jose L Segovia-Juarez; Silvano Colombano |
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Publication Detail:
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Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
Journal Detail:
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Title: Bio Systems Volume: 68 ISSN: 0303-2647 ISO Abbreviation: BioSystems Publication Date: 2003 Feb-Mar |
Date Detail:
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Created Date: 2003-02-21 Completed Date: 2003-11-18 Revised Date: 2006-11-15 |
Medline Journal Info:
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Nlm Unique ID: 0430773 Medline TA: Biosystems Country: Ireland |
Other Details:
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Languages: eng Pagination: 187-98 Citation Subset: IM |
Copyright Information:
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Copyright 2002 Elsevier Science Ireland Ltd. |
Affiliation:
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Department of Computer Science, Biocomputing Laboratory, Wayne State University, 431 State Hall, Detroit, MI 48202, USA. jls@cs.wyane.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Evolution* Learning Models, Biological* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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