Source:http://linkedlifedata.com/resource/pubmed/id/12689732
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
2-3
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| pubmed:dateCreated |
2003-4-11
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| pubmed:abstractText |
What genotypic features explain the evolvability of organisms that have to accomplish many different tasks? The genotype of behaviorally complex organisms may be more likely to encode modular neural architectures because neural modules dedicated to distinct tasks avoid neural interference, i.e. the arrival of conflicting messages for changing the value of connection weights during learning. However, if the connection weights for the various modules are genetically inherited, this raises the problem of genetic linkage: favorable mutations may fall on one portion of the genotype encoding one neural module and unfavorable mutations on another portion encoding another module. We show that this can prevent the genotype from reaching an adaptive optimum. This effect is different from other linkage effects described in the literature and we argue that it represents a new class of genetic constraints. Using simulations we show that sexual reproduction can alleviate the problem of genetic linkage by recombining separate modules all of which incorporate either favorable or unfavorable mutations. We speculate that this effect may contribute to the taxonomic prevalence of sexual reproduction among higher organisms. In addition to sexual recombination, the problem of genetic linkage for behaviorally complex organisms may be mitigated by entrusting evolution with the task of finding appropriate modular architectures and learning with the task of finding the appropriate connection weights for these architectures.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:month |
May
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| pubmed:issn |
0303-2647
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
69
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
245-62
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| pubmed:dateRevised |
2010-11-18
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| pubmed:meshHeading |
pubmed-meshheading:12689732-Adaptation, Physiological,
pubmed-meshheading:12689732-Algorithms,
pubmed-meshheading:12689732-Animals,
pubmed-meshheading:12689732-Biological Evolution,
pubmed-meshheading:12689732-Computer Simulation,
pubmed-meshheading:12689732-Genetic Linkage,
pubmed-meshheading:12689732-Genetic Variation,
pubmed-meshheading:12689732-Genotype,
pubmed-meshheading:12689732-Humans,
pubmed-meshheading:12689732-Models, Genetic,
pubmed-meshheading:12689732-Motor Activity,
pubmed-meshheading:12689732-Mutation,
pubmed-meshheading:12689732-Neural Networks (Computer),
pubmed-meshheading:12689732-Population Dynamics,
pubmed-meshheading:12689732-Psychomotor Performance,
pubmed-meshheading:12689732-Recombination, Genetic,
pubmed-meshheading:12689732-Selection, Genetic,
pubmed-meshheading:12689732-Sexual Behavior,
pubmed-meshheading:12689732-Task Performance and Analysis
|
| pubmed:year |
2003
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| pubmed:articleTitle |
What does it take to evolve behaviorally complex organisms?
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| pubmed:affiliation |
Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy. rcalabretta@ip.rm.cnr.it
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| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't,
Evaluation Studies
|