Neuronal Basis of Learning.

Abstract

Our studies on the relatively simple nervous systems of the molluscs Pleurobranchaea and Aplysia have inquired into the neuronal basis of integrated behavior, the effect of learning on such integration, identification of neurons involved in the learned behavior, and small network modeling of learning. Significant findings are: (1) Neurocircuits establishing whole-animal behavior functionally emerge or self-organize within pools of coactive neurons; i.e., functional neurocircuits arise moreso from nonlinear dynamical properties than from static (switchboard) anatomical ones. 2) Pharmacologic antagonists of cholinergic muscarinic receptors enhance one-trial Pavlovian conditioning. The specificity of this effect on associative processes provides an important inroad for experiments aiming to identify neurons involved in learning, and also to understand how learning biases self-organization. 3) In support of a goal-seeking theory of learning, conditioning of small nerve networks shows that cellular changes relating to an analog of learning involve postsynaptic processes in addition to presynaptic ones. These findings have broad implications in neurosciences and artificial intelligence. Keywords: Associative learning, Parallel processing, Self-organization, Pavlovian conditioning, Muscarinic receptors, Cholinergic; Identified neurons, Distributed function, Nerve nets.

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Document Details

Document Type
Technical Report
Publication Date
Mar 03, 1986
Accession Number
ADA167079

Entities

People

  • George J. Moitsos

Organizations

  • Oregon State University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Animal Behavior
  • Animals
  • Artificial Intelligence
  • Cells
  • Central Nervous System
  • Experimental Design
  • Learning
  • Medical Personnel
  • Nerve Net
  • Nerves
  • Nervous System
  • Neurons
  • New York
  • Oceanography
  • Parallel Computing
  • Parallel Processing
  • Self Organizing Systems

Fields of Study

  • Biology
  • Psychology

Readers

  • Neural Network Machine Learning.
  • Neuroscience

Technology Areas

  • AI & ML