Learning how to select an action: A computational model

Berat Denizdurduran*, Neslihan Serap Sengor

*Corresponding author for this work

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

5 Citations (Scopus)


Neurophysiological experimental results suggest that basal ganglia plays crucial role in action selection while dopamine modifies this process. There are computational models based on these experimental results for action selection. This work focuses on modification of action selection by dopamine release. In the model, a dynamical system is considered for action selection and modification of action selection process is realized by reinforcement learning. The ability of the proposed dynamical system is investigated by bifurcation analysis. Based on the results of this bifurcation analysis, the effect of reinforcement learning on action selection is discussed. The model is implemented on a mobile robot and a foraging task is realized where an exploration in an unfamiliar environment with training in the world is accomplished. Thus, this work fulfills its aim of showing the efficiency of brain-inspired computational models in controlling intelligent agents.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Number of pages8
EditionPART 1
Publication statusPublished - 2012
Event22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
Duration: 11 Sept 201214 Sept 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7552 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Artificial Neural Networks, ICANN 2012


  • Action Selection
  • Basal Ganglia Circuits
  • Bifurcation Analysis
  • Cognitive Robotics
  • Reinforcement Learning


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