Integration of Neurobiological and Computational Analyses of the Neural Network Essentials for Conditioned Taste Aversion

Abstract

The general goal of this project was to determine the neural basis of learning and memory, that is, how the brain stores and retrieves memory. The special form of learning which was the focus of this project was conditioned taste aversions, learned aversions to the taste of food or fluid when consumption of that substance is followed by illness. Studies were made of the illness pathway and illness-taste integration pathway. Conditions under which endogenous substances act as illness-inducing agents were determined, techniques to study neural substrates for those substances as well as exogenous toxins were developed and evidence refuting hypotheses regarding the role of particular brain areas as substrates for illness-integration was obtained. Endogenous factors that modulate the acquisition and extinction of conditioned taste aversions were also identified. Variations in endogenous hormone levels, availability of water, and age alter the facility with which an aversion is learned and unlearned. Finally, a neural model encompassing the illness pathway, the taste pathway, the behavioral pathways, and the modulatory pathways was developed.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 29, 1991
Accession Number
ADA235164

Entities

People

  • Kathleen C. Chambers

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Brain
  • Brain Stem
  • Endocrine Glands
  • Hormones
  • Human Behavior
  • Military Research
  • Neural Networks
  • Neurosciences
  • New York
  • Physiology
  • Psychology
  • Psychophysiology
  • Substrates
  • Vagus Nerve
  • Water Deprivation

Fields of Study

  • Psychology

Readers

  • Breast cancer cell signaling and growth regulation.
  • Materials Science
  • Neurodegenerative Parkinson's Disease and Rickettsial Disease handbook, including the data level of dopamine, BC, neurons, and PD.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks