Neuron Learning to Network Organization.

Abstract

Progress has been recently made in constructing neural networks that can organize themselves to produce distributed memories. These networks, as well as the proposed procedures by which they modify themselves with experience, are consistent with known neurophysiology as well as with what information may be available at synaptic junctions. The modification assumptions on which these ideas are based have consequences that may be testable in visual cortex. Applied to visual cortex, we assume that between lateral-geniculate and visual cortical cells there exist labile synapses that modify themselves in a fashion consistent with the assumptions above. Giving the environment an appropriate form, we obtain orientation tuning curves and ocular dominance comparable to what is observed in normally reared adult cats or monkeys. Simulations with binocular input and various types of normal or altered environments show good agreement with the relevant experimental data. Experiments are suggested that could test our theory further. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 20, 1983
Accession Number
ADA136338

Entities

People

  • L. N. Cooper

Organizations

  • Brown University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Brain
  • Central Nervous System
  • Computational Science
  • Computers
  • Differential Equations
  • Information Processing
  • Nervous System
  • Network Science
  • Neurons
  • Plastic Properties
  • Probabilistic Models
  • Random Variables
  • Statistical Analysis
  • Stochastic Processes
  • Synapses
  • Theorems

Fields of Study

  • Biology

Readers

  • Molecular and Cellular Biochemistry
  • Theoretical Analysis.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks