Organization of Receptive Fields in Networks with Hebbian Learning. The Connection between Synaptic and Phenomenological Models.

Abstract

In this paper we address the question of how does the lateral interaction affect the formation and organization of receptive fields in a network composed of interacting neurons with Hebbian type learning. We will show how to partially decouple the single cell effects from the network effects, and how some phenomenological models can be seen as approximations to these learning networks. We show that the interaction effects the structure of receptive fields. We also demonstrate how the organization of different receptive fields across the cortex is influenced by the interaction term, and that the type of singularities depend on the symmetries of the receptive fields.

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Document Details

Document Type
Technical Report
Publication Date
May 30, 1995
Accession Number
ADA295011

Entities

People

  • Harel Shouval
  • Leon Cooper

Organizations

  • Brown University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Brain
  • Classification
  • Complex Systems
  • Complex Variables
  • Dynamics
  • Eigenvalues
  • Equations
  • Excitation
  • Information Processing
  • Information Science
  • Information Systems
  • Inhibition
  • Iterations
  • Learning
  • Statistical Mechanics
  • Symmetry

Readers

  • Neural Network Machine Learning.
  • Neuroscience
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.