Associative learning using ising-like model

Jakub M. Tomczak*

*Corresponding author for this work

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


In this paper, a new computational model of associative learning is proposed, which is based on the Ising model. Application of the stochastic gradient descent algorithm to the proposed model yields an on-line learning rule. Next, it is shown that the obtained new learning rule generalizes two well-known learning rules, i.e., the Hebbian rule and the Oja’s rule. Later, the fashion of incorporating the cognitive account into the obtained associative learning rule is proposed. At the end of the paper, experiments were carried out for testing the backward blocking and the reduced overshadowing and blocking phenomena. The obtained results are discussed and conclusions are drawn.

Original languageEnglish
Title of host publicationAdvances in Systems Science - Proceedings of the International Conference on Systems Science, ICSS 2013
EditorsJerzy Świątek, Adam Grzech, Paweł Świątek, Jakub M. Tomczak, Jerzy Świątek
PublisherSpringer Verlag
Number of pages10
ISBN (Electronic)9783319018560
Publication statusPublished - 1 Jan 2014
Externally publishedYes
EventInternational Conference on Systems Science, ICSS 2013 - Wroclaw, Poland
Duration: 10 Sep 201312 Sep 2013

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


ConferenceInternational Conference on Systems Science, ICSS 2013


  • Associative learning
  • Backward blocking
  • Energy-based model
  • Hebbian rule
  • Ising model
  • Oja’s rule
  • Reduced overshadowing
  • Rescorla-Wagner model


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