Long-Term Synaptic Plasticity and Learning in Neuronal Networks.

Abstract

The purpose of this project was to understand the mechanisms by which use-dependent changes in synaptic transmission can encode information into neural networks. Our working hypothesis that guided this effort was that long-term synaptic potentiation (LTP) is an excellent candidate mechanism for information storage in the nervous system. The project was organized around three interrelated efforts. First, new experimental and theoretical techniques for analyzing synaptic function were developed. Second, these and conventional methods were used to understand the biophysical and molecular mechanisms responsible for lTP in several different tissues. Third, the relationship between LTP and several formal information encoding schemes was demonstrated. These included synaptic analogs to classical conditioning, Hebb's postulate, and a modified version of Klopf's postulate. The results add confidence to our working hypothesis; they provide new insights into our understanding of synaptic plasticity; and they will enable the definitive tests of some leading theories.

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Document Details

Document Type
Technical Report
Publication Date
Jul 14, 1986
Accession Number
ADA173170

Entities

People

  • Thomas H. Brown

Tags

Communities of Interest

  • Advanced Electronics
  • Human Systems

DTIC Thesaurus Topics

  • Brain
  • Central Nervous System
  • Computer Programs
  • Computer Simulations
  • Computers
  • Education
  • Hippocampus
  • Nerve Impulses
  • Nerves
  • Nervous System
  • Neural Networks
  • Neurons
  • Notation
  • Plastic Properties
  • Scientific Research
  • Simulations
  • Symbols

Readers

  • Auditory Neuroscience/Auditory Physiology.
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Systems Analysis and Design

Technology Areas

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