Accession Number : ADA188378
Title : A Neuronal Model of Classical Conditioning.
Descriptive Note : Technical rept. Oct 79-Sep 87,
Corporate Author : AIR FORCE WRIGHT AERONAUTICAL LABS WRIGHT-PATTERSON AFB OH
Personal Author(s) : Klopf, A H
PDF Url : ADA188378
Report Date : Oct 1987
Pagination or Media Count : 159
Abstract : The neuronal model of classical conditioning is proposed to yield a model more in accordance with animal learning phenomena. Instead of correlating pre- and postsynaptic levels of activity, changes in pre- and postsynaptic levels of activity should be correlated to determine the changes in synaptic efficacy that represent learning. Instead of correlating approximately simultaneous pre and postsynaptic signals earlier changes in presynaptic signals should be correlated with later changes in postsynaptic signals. A change in the efficacy of a synapse should be proportional to the current efficacy of the synapse, accounting for the initial positive acceleration in the s-shaped acquisition curves observed in animal learning. The resulting model, termed a drive reinforcement model of single neuron function, suggest that nervous system activity can be understood in terms of two classes of neuronal signals: Drives that are defined to be signal levels and reinforcers that are defined to be changes in signal levels. Defining drives and reinforcers in this way, in conjunction with the neuronal model is an extension of the neurobiological theory of learning. It is shown that the proposed neuronal model predicts the basic categories of classical conditioning phenomena including delay and trace conditioning, conditioned and unconditioned stimulus duration ad amplitude effects, partial reinforcement effects, interstimulus interval effects including simultaneous conditioning, second-order conditioning, conditioned inhibition, extinction, reacquisition effects, backward conditioning, blocking, overshadowing, compound conditioning, and discriminative stimulus effects.
Descriptors : *LEARNING, *NERVE CELLS, *ARTIFICIAL INTELLIGENCE, *MATHEMATICAL MODELS, ACCELERATION, AMPLITUDE, ANIMALS, EXTINCTION, INHIBITION, INTERVALS, MODELS, NERVOUS SYSTEM, SIGNALS, STIMULI, SYNAPSE, SYNCHRONISM, CONDITIONING(LEARNING), NEURAL NETS, NERVE TRANSMISSION
Subject Categories : Cybernetics
Anatomy and Physiology
Distribution Statement : APPROVED FOR PUBLIC RELEASE