Accession Number : AD0756919

Title :   A Review of the Theory of Embedding Fields and Its Application to Neural Networks.

Descriptive Note : Final technical rept.,

Corporate Author : DAYTON UNIV OHIO RESEARCH INST

Personal Author(s) : Levy,Donald M.

Report Date : DEC 1972

Pagination or Media Count : 39

Abstract : The mathematical theory of embedding fields is reviewed from the point of view of its applicability to the study of neural networks. Emphasis is placed on those networks which can discriminate, learn, remember and reproduce complicated spatial-temporal patterns. Of particular interest is the realization of such networks using electronic models of neural elements termed syncoders. The theory of embedding fields allows an examination of networks composed of neuron-like elements and the demonstration that these elements can be arranged in a variety of ways to accomplish pattern discrimination, learning and remembering. Networks which are analyzed include those for learning both stationary and time-varying patterns as well as for pattern discrimination. Syncoders are determined to possess many of the attributes indicated by the theory to be required for the synthesis of networks which exhibit learning and memory capability. One characteristic which is found to be lacking and is a suggested area of future consideration, is the capability to establish an adequate dynamic associational strength at each cell. (Author)

Descriptors :   (*ARTIFICIAL INTELLIGENCE, LEARNING MACHINES), (*NERVE CELLS, LEARNING), PATTERN RECOGNITION, MATHEMATICAL MODELS, CENTRAL NERVOUS SYSTEM

Subject Categories : Bionics

Distribution Statement : APPROVED FOR PUBLIC RELEASE