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Titolo:
Self-organization of topographic mixture networks using attentional feedback
Autore:
Williamson, JR;
Indirizzi:
Boston Univ, Dept Cognit & Neural Syst, Boston, MA 02115 USA Boston Univ Boston MA USA 02115 ognit & Neural Syst, Boston, MA 02115 USA Boston Univ, Ctr Adapt Syst, Boston, MA 02115 USA Boston Univ Boston MA USA 02115 niv, Ctr Adapt Syst, Boston, MA 02115 USA
Titolo Testata:
NEURAL COMPUTATION
fascicolo: 3, volume: 13, anno: 2001,
pagine: 563 - 593
SICI:
0899-7667(200103)13:3<563:SOTMNU>2.0.ZU;2-3
Fonte:
ISI
Lingua:
ENG
Soggetto:
STRIATE CORTEX; NEURAL-NETWORK; CORTICAL MAPS; CLASSIFICATION; MODEL;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Engineering, Computing & Technology
Citazioni:
20
Recensione:
Indirizzi per estratti:
Indirizzo: Williamson, JR Boston Univ, Dept Cognit & Neural Syst, Boston, MA 02115 USA Boston Univ Boston MA USA 02115 Syst, Boston, MA 02115 USA
Citazione:
J.R. Williamson, "Self-organization of topographic mixture networks using attentional feedback", NEURAL COMP, 13(3), 2001, pp. 563-593

Abstract

This article proposes a neural network model of supervised learning that employs biologically motivated constraints of using local, on-line, constructive learning. The model possesses two novel learning mechanisms. The firstis a network for learning topographic mixtures. The network's internal category nodes are the mixture components, which learn to encode smooth distributions in the input space by taking advantage of topography in the input feature maps. The second mechanism is an attentional biasing feedback circuit. When the network makes an incorrect output prediction, this feedback circuit modulates the learning rates of the category nodes, by amounts based on the sharpness of their tuning, in order to improve the network's prediction accuracy. The network is evaluated on several standard classification benchmarks and shown to perform well in comparison to other classifiers.

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Documento generato il 25/01/20 alle ore 15:54:16