TY - GEN
T1 - Computational model of cortical neuronal receptive fields for self-motion perception
AU - Yu, Chen Ping
AU - Duffy, Charles
AU - Page, William
AU - Gaborski, Roger
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77953839040&partnerID=8YFLogxK
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U2 - 10.1109/AIPR.2009.5466295
DO - 10.1109/AIPR.2009.5466295
M3 - Conference contribution
AN - SCOPUS:77953839040
SN - 9781424451463
T3 - Proceedings - Applied Imagery Pattern Recognition Workshop
BT - Applied Imagery Pattern Recognition 2009
T2 - 38th Applied Imagery Pattern Recognition Workshop: Vision: Humans, Animals, and Machines, AIPRW 2009
Y2 - 14 October 2009 through 16 October 2009
ER -