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 language | English (US) |
|---|---|
| Title of host publication | Applied Imagery Pattern Recognition 2009 |
| Subtitle of host publication | Vision: Humans, Animals, and Machines, AIPR 2009 |
| DOIs | |
| State | Published - Dec 1 2009 |
| Event | 38th Applied Imagery Pattern Recognition Workshop: Vision: Humans, Animals, and Machines, AIPRW 2009 - Washington, DC, United States Duration: Oct 14 2009 → Oct 16 2009 |
Publication series
| Name | Proceedings - Applied Imagery Pattern Recognition Workshop |
|---|---|
| ISSN (Print) | 1550-5219 |
Conference
| Conference | 38th Applied Imagery Pattern Recognition Workshop: Vision: Humans, Animals, and Machines, AIPRW 2009 |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 10/14/09 → 10/16/09 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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Computational model of cortical neuronal receptive fields for self-motion perception. / Yu, Chen Ping; Duffy, Charles; Page, William; Gaborski, Roger.
Applied Imagery Pattern Recognition 2009: Vision: Humans, Animals, and Machines, AIPR 2009. 2009. 5466295 (Proceedings - Applied Imagery Pattern Recognition Workshop).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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
UR - http://www.scopus.com/inward/citedby.url?scp=77953839040&partnerID=8YFLogxK
U2 - 10.1109/AIPR.2009.5466295
DO - 10.1109/AIPR.2009.5466295
M3 - Conference contribution
SN - 9781424451463
T3 - Proceedings - Applied Imagery Pattern Recognition Workshop
BT - Applied Imagery Pattern Recognition 2009
ER -