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1 November 1992 Neural populations with multimodal threshold distributions that learn by selection
Madan M. Gupta, George K. Knopf, C. Yu
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Proceedings Volume 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods; (1992) https://doi.org/10.1117/12.131600
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
Abstract
Populations of cortical nerve cells that selectively learn from external stimuli are described in this paper. Numerous neural populations are interconnected within a spatially distributed neural activity field. Each population is assumed to possess a multi-modal distribution of neural thresholds which enable it to exhibit one or more state attractors for any given stimulus input. Each stable attractor represents a potential memory. The memory function of the field corresponds to the numerous attractors, or potential memories, generated after the removal of the external stimulus pattern. Massive numbers of attractors are inherent in the field of the onset of learning. The selective learning process involves enlarging the basin around the attractor selected by a given stimulus. A computer simulation involving three sets of stimuli is used to illustrate some of these notions.
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Madan M. Gupta, George K. Knopf, and C. Yu "Neural populations with multimodal threshold distributions that learn by selection", Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); https://doi.org/10.1117/12.131600
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KEYWORDS
Computer simulations
Nerve
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