A computational framework for understanding decision making through integration of basic learning rules

Maxim Bazhenov, Ramon Huerta, Brian Smith

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Nonassociative and associative learning rules simultaneously modify neural circuits. However, it remains unclear how these forms of plasticity interact to produce conditioned responses. Here we integrate nonassociative and associative conditioning within a uniform model of olfactory learning in the honeybee. Honeybees show a fairly abrupt increase in response after a number of conditioning trials. The occurrence of this abrupt change takes many more trials after exposure to nonassociative trials than just using associative conditioning. We found that the interaction of unsupervised and supervised learning rules is critical for explaining latent inhibition phenomenon. Associative conditioning combined with the mutual inhibition between the output neurons produces an abrupt increase in performance despite smooth changes of the synaptic weights. The results show that an integrated set of learning rules implemented using fan-out connectivities together with neural inhibition can explain the broad range of experimental data on learning behaviors.

Original languageEnglish (US)
Pages (from-to)5686-5697
Number of pages12
JournalJournal of Neuroscience
Volume33
Issue number13
DOIs
StatePublished - Mar 27 2013

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Decision Making
Learning
Neural Inhibition
Neurons
Weights and Measures
Conditioning (Psychology)
Inhibition (Psychology)

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

A computational framework for understanding decision making through integration of basic learning rules. / Bazhenov, Maxim; Huerta, Ramon; Smith, Brian.

In: Journal of Neuroscience, Vol. 33, No. 13, 27.03.2013, p. 5686-5697.

Research output: Contribution to journalArticle

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