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Titolo:
The temporal correlation hypothesis for self-organizing feature maps
Autore:
Chen, YN; Reggia, JA;
Indirizzi:
Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA Univ Maryland College Pk MD USA 20742 Comp Sci, College Pk, MD 20742 USA MicroStrategy Inc, Vienna, VA 22182 USA MicroStrategy Inc Vienna VA USA 22182 oStrategy Inc, Vienna, VA 22182 USA Univ Maryland, Inst Adv Comp Study, College Pk, MD 20742 USA Univ Maryland College Pk MD USA 20742 omp Study, College Pk, MD 20742 USA
Titolo Testata:
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
fascicolo: 7, volume: 31, anno: 2000,
pagine: 911 - 921
SICI:
0020-7721(200007)31:7<911:TTCHFS>2.0.ZU;2-U
Fonte:
ISI
Lingua:
ENG
Soggetto:
CONVERGENCE PROPERTIES; NEURAL NETWORKS; MOTOR CONTROL; ROBOT ARM; COORDINATION; DYNAMICS; MODEL;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
24
Recensione:
Indirizzi per estratti:
Indirizzo: Reggia, JA Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA Univ Maryland College Pk MD USA 20742 College Pk, MD 20742 USA
Citazione:
Y.N. Chen e J.A. Reggia, "The temporal correlation hypothesis for self-organizing feature maps", INT J SYST, 31(7), 2000, pp. 911-921

Abstract

Feature maps, in which one or more aspects of the environment are systematically represented over the surface of the cerebral cortex, are of ten found in primary sensory and motor cortical regions of the vertebrate brain. They have inspired a great deal of computational modelling, and this has provided evidence that such maps are emergent properties of the interactions ofnumerous cortical neurons and their adaptive, nonlinear connections. In this paper, we address the issue of how multiple feature maps that coexist inthe same region of cerebral cortex align with each other. We hypothesize that such alignment is governed by temporal correlations : features in one map that are temporally correlated with those in another come to occupy the same spatial locations over time. To examine the feasibility of this hypothesis and to establish some of its detailed implications, we initially studied a computational model of primary sensorimotor cortex. Coexisting sensoryand motor maps formed and generally aligned in a fashion consistent with the temporal correlation hypothesis. We summarize these results, and then mathematically analyse a simplified model of self-organization during unsupervised learning. We show that the properties observed computationally are quite general : that temporally correlated inputs become spatially correlated(i.e. aligned), while input patterns that are temporally anti-correlated tend to result in mutually exclusive (i.e. unaligned) spatial distributions. This work provides a framework in which to interpret and understand futureexperimental studies of map relationships.

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Documento generato il 05/04/20 alle ore 06:38:18