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A neurocomputational amygdala model of auditory fear conditioning: A hybrid system approach
Knowledge Technology Group, University of Hamburg, Department of Computer Science, Hamburg, Germany.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
Knowledge Technology Group, University of Hamburg, Department of Computer Science, Hamburg, Germany.
2012 (English)In: Proceedings of the International Joint Conference on Neural Networks (IJCNN) / [ed] Hussein Abbass, New York: IEEE conference proceedings, 2012, 214-221 p.Conference paper, (Refereed)
Abstract [en]

In this work, we present a neurocomputational model for auditory-cue fear acquisition. Computational fear conditioning has experienced a growing interest over the last few years, on the one hand, because it is a robust and quick learning paradigm that can contribute to the development of more versitile robots, and on the other hand, because it can help in the understanding of fear conditioning and dysfunctions in animals. Fear learning involves sensory and motor aspects [1] and it is essential for adaptive self-protective systems. We argue that a deeper study of the mechanisms underlying fear circuits in the brain will contribute not only to the development of safer robots but eventually also to a better conceptual understanding of neural fear processing in general. Towards the development of a robotic adaptive self-protective system, we have designed a neural model of fear conditioning based on LeDoux's dual-route hypothesis of fear [2] and also dopamine modulated Pavlovian conditioning [3]. Our hybrid approach is capable of learning the temporal relationship between auditory sensory cues and an aversive or appetitive stimulus. The model was tested as a neural network simulation but it was designed to be used with minor modifications on a robotic platform.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2012. 214-221 p.
Series
IEEE International Conference on Neural Networks (ICNN), ISSN 1098-7576
National Category
Computer and Information Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-6592DOI: 10.1109/IJCNN.2012.6252392ISI: 000309341300030Scopus ID: 2-s2.0-84865098665ISBN: 978-1-4673-1488-6 ISBN: 978-1-4673-1490-9 ISBN: 978-1-4673-1489-3 ISBN: 1-4673-1489-7 OAI: oai:DiVA.org:his-6592DiVA: diva2:564032
Conference
2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, QLD, Australia, 10 June 2012 through 15 June 2012
Available from: 2012-11-01 Created: 2012-11-01 Last updated: 2015-12-29Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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More languages
Output format
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