Modified Modulated Hebb-Oja learning rule: A method for biologically plausible principal component analysis

Marko Jankovic, Pablo Martinez, Zhe Chen, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This paper presents Modified Modulated Hebb-Oja (MHO) method that performs principal component analysis. Method is based on implementation of Time-Oriented Hierarchical Method applied on recently proposed principal subspace analysis rule called Modulated Hebb-Oja learning rule. Comparing to some other well-known methods for principal component analysis, the proposed method has one feature that could be seen as desirable from the biological point of view - synaptic efficacy learning rule does not need the explicit information about the value of the other efficacies to make individual efficacy modification. Simplicity of the "neural circuits" that perform global computations and a fact that their number does not depend on the number of input and output neurons, could be seen as good features of the proposed method. The number of necessary global calculation circuit is one. Some similarity to a part of the frog retinal circuit will be suggested, too.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Pages527-536
Number of pages10
EditionPART 1
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 13 Nov 200716 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4984 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Neural Information Processing, ICONIP 2007
Country/TerritoryJapan
CityKitakyushu
Period13/11/0716/11/07

Keywords

  • Local learning rules
  • Principal component analysis
  • Stiefel submanifold
  • Time oriented hierarchy

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