Information-theoretic interpretation of tuning curves for multiple motion directions

Wentao Huang, Xin Huang, Kechen Zhang

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

Abstract

We have developed an efficient information-maximization method for computing the optimal shapes of tuning curves of sensory neurons by optimizing the parameters of the underlying feedforward network model. When applied to the problem of population coding of visual motion with multiple directions, our method yields several types of tuning curves with both symmetric and asymmetric shapes that resemble what have been found in the visual cortex. Our result suggests that the diversity or heterogeneity of tuning curve shapes as observed in neurophysiological experiment might actually constitute an optimal population representation of visual motions with multiple components.

Original languageEnglish (US)
Title of host publication2017 51st Annual Conference on Information Sciences and Systems, CISS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509047802
DOIs
StatePublished - May 10 2017
Event51st Annual Conference on Information Sciences and Systems, CISS 2017 - Baltimore, United States
Duration: Mar 22 2017Mar 24 2017

Publication series

Name2017 51st Annual Conference on Information Sciences and Systems, CISS 2017

Other

Other51st Annual Conference on Information Sciences and Systems, CISS 2017
CountryUnited States
CityBaltimore
Period3/22/173/24/17

Keywords

  • Area MT
  • Bidirectional visual motion
  • Fisher information
  • Shannon mutual information
  • Side bias
  • Unsupervised learning

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems and Management
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Huang, W., Huang, X., & Zhang, K. (2017). Information-theoretic interpretation of tuning curves for multiple motion directions. In 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017 [7926142] (2017 51st Annual Conference on Information Sciences and Systems, CISS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2017.7926142