Hebbian learning and self-association in nonlinear neural networks

Author

Palmieri, Francesco

Author_Institution

Connecticut Univ., CT, USA

Volume

2

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

1258

Abstract

A self-organizing feature map, based on a Hebbian paradigm, is proposed as a universal adaptive memory. The learning paradigm, can be applied to arbitrary network topologies containing the standard sigmoidal nonlinearities at their nodes. The system generalizes the linear principal components by mapping the input space into a set of orthogonal nonlinear projections. Only localized learning rules are necessary for the adaptation. The size of the system is related to the desired accuracy and to the density of the examples

Keywords

Hebbian learning; self-organising feature maps; Hebbian learning; arbitrary network topologies; learning paradigm; linear principal components; localized learning rules; nonlinear neural networks; orthogonal nonlinear projections; self-association; self-organizing feature map; sigmoidal nonlinearities; universal adaptive memory; Adaptive filters; Adaptive signal processing; Artificial neural networks; Biological neural networks; Biomedical signal processing; Hebbian theory; Intelligent networks; Network topology; Neural networks; Signal analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374365

Filename

374365