Literature DB >> 1016695

Complementary molecular models of learning and memory.

M Conrad.   

Abstract

The functional capabilities of the brain are formally characterizable interms of a finite system along with a memory space which it can manipulate. Two types of learning are possible: (1) modification-based learning, associated with alternate realizations of the finite system; (2) memory-based learning, associated with the assimilation, manipulation, and retrieval of memories. Constructive models which fulfill these conditions and which at the same time operate on the basis of molecular information processing principles have certain general features. We describe these features in terms of two interfaced submodels, the first for the finite system and the second for the memory space. The finite system may be realized by networks of neurons in which the specificity of enzyme molecules controls the nerve impulse. Such a realization is amenable to modification-based learning mediated by processes analogous to those of natural evolution and selective theories of antibody synthesis. The memory space is realizable by networks of neurons in which the conformation of dendritic receptor molecules controls the nerve impulse. In this case certain neurons firing in response to an external input undergo sensitization at the dendrites and in such a way that they are loadable and later callable by reference neurons, thereby allowing for reconstruction of manipulation of the firing pattern associated with this input. The overall construction makes a large number of biochemical, anatomical, physiological, and psychological predictions which are either testable or in good agreement with fact.

Mesh:

Year:  1976        PMID: 1016695     DOI: 10.1016/0303-2647(76)90015-0

Source DB:  PubMed          Journal:  Biosystems        ISSN: 0303-2647            Impact factor:   1.973


  2 in total

1.  Computational modeling of evolutionary learning processes in the brain.

Authors:  R R Kampfner; M Conrad
Journal:  Bull Math Biol       Date:  1983       Impact factor: 1.758

2.  The enzymatic neuron as a reaction-diffusion network of cyclic nucleotides.

Authors:  K G Kirby; M Conrad
Journal:  Bull Math Biol       Date:  1984       Impact factor: 1.758

  2 in total

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