Feature discovery by competitive learning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Filter Selection Model for Generating Visual Motion Signals
Advances in Neural Information Processing Systems 5, [NIPS Conference]
International Journal of Computer Vision
Optical Snow and the Aperture Problem
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Disambiguating Visual Motion Through Contextual Feedback Modulation
Neural Computation
Simultaneous measurement of steering performance and perceived heading on a curving path
ACM Transactions on Applied Perception (TAP)
A Neural Model of Smooth Pursuit Control and Motion Perception by Cortical Area MST
Journal of Cognitive Neuroscience
Target selection by the frontal cortex during coordinated saccadic and smooth pursuit eye movements
Journal of Cognitive Neuroscience
A neuromorphic model of spatial lookahead planning
Neural Networks
A neural circuit for robust time-to-contact estimation based on primate mst
Neural Computation
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Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT^-/MSTv and MT^+/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model's retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT^+ interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT^- interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.