Computational model of cortical neuronal receptive fields for self-motion perception

Chen Ping Yu, Charles Duffy, William Page, Roger Gaborski

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

Abstract

Biologically inspired approaches are an alternative to conventional engineering approaches when developing complex algorithms for intelligent systems. In this paper, we present a novel approach to the computational modeling of primate cortical neurons in the dorsal medial superior temporal area (MSTd). Our approach is based-on a spatially distributed mixture of Gaussians, where MST's primary function is detecting self-motion from optic flow stimulus. Each biological neuron was modeled using a genetic algorithm to determine the parameters of the mixture of Gaussians, resulting in firing rate responses that accurately match the observed responses of the corresponding biological neurons. We also present the possibility of applying the trained models to machine vision as part of a simple dorsal stream processing model for self-motion detection, which has applications to motion analysis and unmanned vehicle navigation.

Original languageEnglish (US)
Title of host publicationApplied Imagery Pattern Recognition 2009
Subtitle of host publicationVision: Humans, Animals, and Machines, AIPR 2009
DOIs
StatePublished - Dec 1 2009
Event38th Applied Imagery Pattern Recognition Workshop: Vision: Humans, Animals, and Machines, AIPRW 2009 - Washington, DC, United States
Duration: Oct 14 2009Oct 16 2009

Publication series

NameProceedings - Applied Imagery Pattern Recognition Workshop
ISSN (Print)1550-5219

Conference

Conference38th Applied Imagery Pattern Recognition Workshop: Vision: Humans, Animals, and Machines, AIPRW 2009
CountryUnited States
CityWashington, DC
Period10/14/0910/16/09

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Yu, C. P., Duffy, C., Page, W., & Gaborski, R. (2009). Computational model of cortical neuronal receptive fields for self-motion perception. In Applied Imagery Pattern Recognition 2009: Vision: Humans, Animals, and Machines, AIPR 2009 [5466295] (Proceedings - Applied Imagery Pattern Recognition Workshop). https://doi.org/10.1109/AIPR.2009.5466295