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Oliver W Layton

Showing results (1-10 of 24) with videos related to

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Sensors (Basel, Switzerland)|December 28, 2021
ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion EstimationOliver W Layton
Neural Networks : the Official Journal of the International Neural Network Society|August 9, 2022
Estimating heading from optic flow: Comparing deep learning network and human performanceNatalie Maus, Oliver W Layton
Eneuro|October 13, 2021
Modeling Physiological Sources of Heading Bias from Optic FlowSinan Yumurtaci, Oliver W Layton
Vision Research|December 3, 2014
A neural model of border-ownership from kinetic occlusionOliver W Layton, Arash Yazdanbakhsh
The Journal of Neuroscience : the Official Journal of the Society for Neuroscience|November 9, 2019
Computational Mechanisms for Perceptual Stability using Disparity and Motion ParallaxOliver W Layton, Brett R Fajen
Journal of Vision|January 15, 2016
Sources of bias in the perception of heading in the presence of moving objects: Object-based and border-based discrepanciesOliver W Layton, Brett R Fajen
Journal of Neurophysiology|October 30, 2015
The temporal dynamics of heading perception in the presence of moving objectsOliver W Layton, Brett R Fajen
Journal of Vision|May 16, 2017
Possible role for recurrent interactions between expansion and contraction cells in MSTd during self-motion perception in dynamic environmentsOliver W Layton, Brett R Fajen
Frontiers in Computational Neuroscience|October 13, 2012
Recurrent competition explains temporal effects of attention in MSTdOliver W Layton, N Andrew Browning
The Journal of Neuroscience : the Official Journal of the Society for Neuroscience|August 5, 2016
A Neural Model of MST and MT Explains Perceived Object Motion during Self-MotionOliver W Layton, Brett R Fajen
Pageof 3

Showing results (1-10 of 24) with videos related to

Sort By:
Pageof 3
Sensors (Basel, Switzerland)|December 28, 2021
ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion EstimationOliver W Layton
Neural Networks : the Official Journal of the International Neural Network Society|August 9, 2022
Estimating heading from optic flow: Comparing deep learning network and human performanceNatalie Maus, Oliver W Layton
Eneuro|October 13, 2021
Modeling Physiological Sources of Heading Bias from Optic FlowSinan Yumurtaci, Oliver W Layton
Vision Research|December 3, 2014
A neural model of border-ownership from kinetic occlusionOliver W Layton, Arash Yazdanbakhsh
The Journal of Neuroscience : the Official Journal of the Society for Neuroscience|November 9, 2019
Computational Mechanisms for Perceptual Stability using Disparity and Motion ParallaxOliver W Layton, Brett R Fajen
Journal of Vision|January 15, 2016
Sources of bias in the perception of heading in the presence of moving objects: Object-based and border-based discrepanciesOliver W Layton, Brett R Fajen
Journal of Neurophysiology|October 30, 2015
The temporal dynamics of heading perception in the presence of moving objectsOliver W Layton, Brett R Fajen
Journal of Vision|May 16, 2017
Possible role for recurrent interactions between expansion and contraction cells in MSTd during self-motion perception in dynamic environmentsOliver W Layton, Brett R Fajen
Frontiers in Computational Neuroscience|October 13, 2012
Recurrent competition explains temporal effects of attention in MSTdOliver W Layton, N Andrew Browning
The Journal of Neuroscience : the Official Journal of the Society for Neuroscience|August 5, 2016
A Neural Model of MST and MT Explains Perceived Object Motion during Self-MotionOliver W Layton, Brett R Fajen
Pageof 3