Advances in Systems Science

Volume 240 of the series Advances in Intelligent Systems and Computing pp 295-304

Associative Learning Using Ising-Like Model

  • Jakub M. TomczakAffiliated withInstitute of Computer Science, Wrocław University of Technology Email author 

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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.


associative learning Ising model energy-based model Hebbian rule Oja’s rule Rescorla-Wagner model backward blocking reduced overshadowing