Literature DB >> 9837700

Hebb and Darwin.

P Adams1.   

Abstract

It is proposed that vertebrate brains, especially those of mammals, operate according to an algorithm subsumable as "synaptic Darwinism". The key postulate is that genes and synapses follow the same rules, because they act as autocatalytic, hypercyclic, units of selection. Synapses replicate by quantally strengthening, and mutate by connecting new cells. Because synapses relate pre- and post-synaptic firing, they perform a translation operation. Furthermore the product of this operation, conjoint firing, favors replication (by Hebb's Rule). The result is that variants are selected and patterns of connection automatically adopt optimal configurations. These configurations are determined by scalar neuromodulatory "reward" signals applied globally to layers of neurons, which reduce spike frequency adaptation and enhance Hebbian replication. Global or local control of mutation rates provides further improvements in the Darwinian algorithm. All the processes and circuits postulated have plausible, and often obvious, implementations. The result is that brains evolve and adapt like large ecosystems. Copyright 1998 Academic Press.

Entities:  

Mesh:

Year:  1998        PMID: 9837700     DOI: 10.1006/jtbi.1997.0620

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  10 in total

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2.  Punctuated evolution and robustness in morphogenesis.

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4.  Selectionist and evolutionary approaches to brain function: a critical appraisal.

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5.  Breeding novel solutions in the brain: a model of Darwinian neurodynamics.

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Review 6.  Donald O. Hebb and the Organization of Behavior: 17 years in the writing.

Authors:  Richard E Brown
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7.  Genomic Variation, Evolvability, and the Paradox of Mental Illness.

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8.  Copying and evolution of neuronal topology.

Authors:  Chrisantha Fernando; K K Karishma; Eörs Szathmáry
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9.  Neuronal boost to evolutionary dynamics.

Authors:  Harold P de Vladar; Eörs Szathmáry
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10.  What can ecosystems learn? Expanding evolutionary ecology with learning theory.

Authors:  Daniel A Power; Richard A Watson; Eörs Szathmáry; Rob Mills; Simon T Powers; C Patrick Doncaster; Błażej Czapp
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  10 in total

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