Theoretical and Experimental Analysis of the Neural Bases for Learning and Memory

Abstract

Our ONR supported empirical work during the past five years has focused on identification of the essential i.e., the necessary and sufficient, neural memory trace circuitry and the essential memory traces for associative learning and memory of discrete behavioral responses learned to deal with aversive events, using eyelid conditioning (in the rabbit) as the primary animal model but other discrete responses as well. In-so-far as localization of the memory trace itself is concerned, we developed several lines of evidence arguing strongly that it is not in the motor nuclei that generate the behavioral response or in the UR reflex pathways, most notably that lesions and pharmacological manipulations can selectively abolish the CR with no effect on the UR. Utilizing an auditory signal detection paradigm we developed strong evidence that with an auditory CS the memory trace is not stored in the CS pathway, at the least the main-line primary auditory relay nuclei. Our evidence to date demonstrates that the cerebellum is necessary for the learning and memory of eyelid closure and other discrete behavioral responses. The conditioned eyelid closure response is a very precisely timed movement--over the entire effective CS-US onset interval where learning occurs, from about 100 msec to over 1 sec., the learned response develops such that the eyelid closure is maximal at the time of onset of the US. It is also a very precisely timed 'skilled' movement, perhaps the most elementary form of learned skilled movement. Our results strongly support the general spirit of earlier computational theories of the role of the cerebellum in motor learning.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 1988
Accession Number
ADA203375

Entities

People

  • Herbert Solomon
  • Richard F. Thompson

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Auditory Signals
  • Brain
  • Brain Stem
  • Cells
  • Cerebellum
  • Climbing
  • Computer Science
  • Data Analysis
  • Information Science
  • Learning
  • Linear Accelerators
  • Neurons
  • New York
  • Signal Detection
  • Statistics
  • Training
  • Two Dimensional

Fields of Study

  • Biology
  • Psychology

Readers

  • Neural Network Machine Learning.
  • Theoretical Analysis.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.