Hippocampal Modulation of Recognition, Conditioning, Timing, and Space: Why So Many Functions? [PDF]
, 1993 Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-24877); Office of Naval Research (N00014-91-J ...
Grossberg, Stephen
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A Neural Circuit Model for Prospective Control of Interceptive Reaching [PDF]
, 2001 Two prospective controllers of hand movements in catching -- both based on required velocity control -- were simulated. Under certain conditions, this required velocity controlled to overshoots of the future interception point.
Beck, Peter +3 more
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A Canonical Laminar Neocortical Circuit Whose Bottom-Up, Horizontal, and Top-Down Pathways Control Attention, Learning, and Prediction. [PDF]
Front Syst Neurosci, 2021 Grossberg S.
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Temporal Dynamics of Binocular Display Processing with Corticogeniculate Interactions [PDF]
, 1995 A neural model of binocular vision is developed to simulate psychophysical and neurobiological data concerning the dynamics of binocular disparity processing.
Grossberg, Stephen, Gruenwald, Alexander
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Discrete analogue of impulsive recurrent neural networks with both discrete and finite distributive asynchronous time-varying delays. [PDF]
Cogn Neurodyn, 2022 Jia S, Chen Y.
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Cortical Learning of Recognition Categories: A Resolution of the Exemplar Vs. Prototype Debate [PDF]
, 2009 Do humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex?
Amis, Gregory P. +3 more
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Brain Categorization: Learning, Attention, and Consciousness [PDF]
, 2005 How do humans and animals learn to recognize objects and events? Two classical views are that exemplars or prototypes are learned. A hybrid view is that a mixture, called rule-plus-exceptions, is learned. None of these models learn their categories.
Carpenter, Gail +2 more
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ARTEX: A Self-Organizing Architecture for Classifying Image Regions [PDF]
, 1996 A self-organizing architect is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties ...
Grossberg, Stephen, Williamson, James R.
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Multiplicative processing in the modeling of cognitive activities in large neural networks. [PDF]
Biophys Rev, 2023 Valle-Lisboa JC, Pomi A, Mizraji E.
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Artificial neural networks: a practical review of applications involving fractional calculus. [PDF]
Eur Phys J Spec Top, 2022 Viera-Martin E +4 more
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