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Browsing by Subject "MT"

OpenBU

Browsing by Subject "MT"

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  • Browning, Andrew N.; Grossberg, Stephen; Mingolla, Ennio (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2008-12)
    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 ...
  • Grossbergy, Stephen; Rudd, Michael E. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-04)
    This article describes further evidence for a new neural network theory of biological motion perception. The theory clarifies why parallel streams Vl --> V2, Vl --> MT, and Vl --> V2 --> MT exist for static form and motion ...
  • Berzhanskaya, J.; Grossberg, S.; Mingolla, E. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2007-01)
    How do visual form and motion processes cooperate to compute object motion when each process separately is insufficient? Consider, for example, a deer moving behind a bush. Here the partially occluded fragments of motion ...
  • Grossberg, Stephen; Mingolla, Ennio; Viswanathan, Lavanya (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-02)
    A neural model is developed of how motion integration and segmentation processes, both within and across apertures, compute global motion percepts. Figure-ground properties, such as occlusion, influence which motion signals ...
  • Grossberg, Stephen; Mingolla, Ennio (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1992-05)
    A neural network model of global motion segmentation by visual cortex is described. Called the Motion Boundary Contour System (BCS), the model clarifies how ambiguous local movements on a complex moving shape are actively ...
  • Grossberg, Stephen; Srihasam, Krishna; Bullock, Daniel (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2007-11)
    How does the brain use eye movements to track objects that move in unpredictable directions and speeds? Saccadic eye movements rapidly foveate peripheral visual or auditory targets and smooth pursuit eye movements keep the ...
  • Browing, Andrew N.; Grossberg, Stephen; Mingolla, Ennio (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2008-12)
    Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a ...
  • Elder, David M.; Grossberg, Stephen; Mingolla, Ennio (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2007-04)
    A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality ...
  • Elder, David M.; Grossberg, Stephen; Minogolla, Ennio (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2009-04)
    A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality ...
  • Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2007-09)
    When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds ...
  • Grossberg, Stephen; Pilly, Praveen K. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2008-02)
    How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A ...