Open AccessSpin glass model of learning by selection.
Author(s) -
G. Toulouse,
Stanislas Dehaene,
JeanPierre Changeux
Publication year - 1986
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.83.6.1695
Subject(s) - categorization , selection (genetic algorithm) , pruning , computer science , spin glass , artificial intelligence , tree (set theory) , statistical mechanics , sign (mathematics) , function (biology) , machine learning , limit (mathematics) , theoretical computer science , statistical physics , mathematics , physics , biology , quantum mechanics , mathematical analysis , evolutionary biology , agronomy
A model of learning by selection is described at the level of neuronal networks. It is formally related to statistical mechanics with the aim to describe memory storage during development and in the adult. Networks with symmetric interactions have been shown to function as content-addressable memories, but the present approach differs from previous instructive models. Four biologically relevant aspects are treated--initial state before learning, synaptic sign changes, hierarchical categorization of stored patterns, and synaptic learning rule. Several of the hypotheses are tested numerically. Starting from the limit case of random connections (spin glass), selection is viewed as pruning of a complex tree of states generated with maximal parsimony of genetic information.