Embedding fields: Underlying philosophy, mathematics, and applications to psychology, physiology, and anatomy

Author(s): Grossberg, S. |

Year: 1971

Citation: Journal of Cybernetics, 1, 28-50

Abstract: This article reviews results on a learning theory that can be derived from simple psychological postulates and given a suggestive neurophysiological, anatomical, and biochemical interpretation. The neural networks described can discriminate, learn, simultaneously remember, and perform individually upon demand any number of space-time patterns of essentially arbitrary complexity. A general theorem expressing this fact is stated in the language of nonlinear functional-differential systems. Applications of the theory to various empirical problems are mentioned; e.g., serial learning, stimulus sampling, lateral inhibition, energy-entropy dependence, reaction time, transmitter production and release, spatiotemporal masking, operant and respondant conditioning, influences of under- or over-arousal on learning.

Topics: Biological Learning, Mathematical Foundations of Neural Networks, Applications: Other, Models: Other,

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Cross References


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