Modeling structural plasticity in the barn owl auditory localization system with a spike-time dependent hebbian learning rule

Shreesh P. Mysore, Steven R. Quartz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Auditory localization behavior in barn owls is mediated by the integration of topographically encoded visual and auditory space maps. In juvenile owls, disruption of the audio-visual map alignment by exposure to spectacles that laterally shift the visual input results in behavioral adaptation over the course of several weeks. It has been reported in literature that this adaptation is produced by architectural plasticity in the neural circuits encoding the space maps. It is known that this plasticity is guided by visual input in a topographic manner, and that the error signal is embedded in the firing dynamics of neurons in the inferior colliculus. In this work, we use leaky integrateand-fire neurons to model the key elements in the auditory localization circuit of barn owls. We demonstrate that a Hebbian spike-time dependent learning rule, coupled with an activity-dependent mechanism that promotes growth, can account for the essentials of circuit-level plasticity associated with prism experience. We point out the importance of inhibition in both the normal functioning of this circuit, and prism-induced plasticity, and comment on potential mechanisms for activity-induced growth.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages2766-2771
Number of pages6
DOIs
StatePublished - 2005
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume5

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
Country/TerritoryCanada
CityMontreal, QC
Period7/31/058/4/05

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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