Analysis and Synthesis of Adaptive Neural Elements and Assemblies

Abstract

The objectives of this research are to analyze the properties of identified neurons and neural circuits that exhibit nonassociative and associative plasticity and to examine the role of neuronal plasticity in learning. During the period between August 1, 1988 and July 31 1989, progress has been made in four areas. First, a model of the biophysical processes within sensory neurons that contribute to nonassociative and associative learning was developed. Second, a model of biophysical and cellular processes underlying rhythmic bursting patterns of activity in neuron R15 was developed. Third, a real-time model of associative learning was incorporated into small neural networks, which include facilitory and inhibitory interneurons, and the ability of these networks to stimulate higher-order features of classical conditioning was examined. Fourth, a model which stimulates aspects of classical conditioning was incorporated into a small neural network, and the ability of this neural network to simulate features of operant conditioning was examined. Keywords: Aplysia; Learning; Memory; Information storage; Artificial intelligence.

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Document Details

Document Type
Technical Report
Publication Date
Dec 19, 1989
Accession Number
ADA218335

Entities

People

  • John H. Byrne

Organizations

  • McGovern Medical School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cells
  • Circuits
  • Generators
  • Mathematical Models
  • Medical Personnel
  • Membrane Potentials
  • Models
  • Modulation
  • Motor Neurons
  • Network Architecture
  • Network Simulation
  • Networks
  • Neural Networks
  • Neurons
  • Simulations
  • Students

Fields of Study

  • Biology

Readers

  • Neural Network Machine Learning.
  • Neuroscience

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Biotechnology