Embedding Fields: Underlying Philosophy, Mathematics, and Applications to Psychology, Physiology, and Anatomy

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.