Associative Memory Biological and Mathematical Aspects.

Abstract

A tutorial is presented encompassing both biological and mathematical aspects of associative memory for pattern processing. A systems viewpoint is adopted whereby biological associative memory is viewed as a system of adaptive filters, with the free parameters of the filter corresponding to the strengths of the biological neural connections. Certainly such viewpoint is not intended to accurately depict the true mechanisms underlying the extraordinary capabilities of biological associative memory-fast pattern recognition and apparently infinite memory capacity. For such mechanisms will unlikely be discovered in the absence of tools allowing the observance of collective behavior over systems of neurons. However, the viewpoint does serve to integrate both mathematics and biology on a general level. Of most significance is perhaps the systematic treatment of mathematical associative memory. In the adaptive filter framework, associative memory is described and compared to traditional statistical techniques. Also, new insight into the generalization capability of associative memory is expressed. Conditions are presented to ensure both correct memory recall and significant generalization. Keywords: Optical processing; Artificial intelligence.

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Document Details

Document Type
Technical Report
Publication Date
Dec 29, 1987
Accession Number
ADA189315

Entities

People

  • Mitchell Eggers

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Adaptive Filters
  • Algorithms
  • Brain
  • Computational Science
  • Computers
  • Content Addressable Memory
  • Filters
  • Information Processing
  • Neural Networks
  • Parallel Computing
  • Parallel Processing
  • Pattern Recognition
  • Processing Equipment
  • Recognition
  • Signal Processing
  • Transfer Functions

Fields of Study

  • Biology

Readers

  • Parallel and Distributed Computing.
  • Theoretical Analysis.

Technology Areas

  • AI & ML