Theoretical and Experimental Research Into Biological Mechanisms Underlying Learning and Memory

Abstract

We describe an extended model of backward propagation incorporating gain modification and compare the performance of the extended model with ordinary backward propagation. We also describe our work on a statistical model for feature extraction based on the BCM neural network model. The model is presented as an exploratory (PP) (Projection Pursuit) algorithm. The formulation, which is similar in nature to PP, is based on a minimization of a cost function over a set of parameters, yielding an optimal decision rule under some norm. A new projection index (cost function) was presented that favors directions possessing multi-modality, where the multi-modality is measured in terms of the separability property of the data. The synaptic modification equations, which perform the minimization of the cost function, turn out to be similar to the synaptic modification equations governing learning in BCM neurons. Keywords: Backward propagation, Statistical model.

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Document Details

Document Type
Technical Report
Publication Date
Apr 24, 1990
Accession Number
ADA223615

Entities

People

  • Leon Cooper

Organizations

  • Brown University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Differential Equations
  • Dimensionality Reduction
  • Equations
  • Extraction
  • Feature Extraction
  • Information Processing
  • Information Systems
  • Learning
  • Mathematical Analysis
  • Military Research
  • Neural Networks
  • Scientific Research
  • Stochastic Processes
  • Theorems
  • Time Intervals

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms