Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Modeling the Behavioral Substrates of Associate Learning and Memory: Adaptive Neuronal Models

Chuen-Chien Lee

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-89-495
February 1989

http://www.eecs.berkeley.edu/Pubs/TechRpts/1989/CSD-89-495.pdf

Three adaptive neuronal models based on neural analogs of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Further, each model can find the most nonredundant and earliest predictor of reinforcement. The behaviors of our models account for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well our models fit empirical data from various animal learning paradigms.


BibTeX citation:

@techreport{Lee:CSD-89-495,
    Author = {Lee, Chuen-Chien},
    Title = {Modeling the Behavioral Substrates of Associate Learning and Memory: Adaptive Neuronal Models},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {1989},
    Month = {Feb},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/1989/6167.html},
    Number = {UCB/CSD-89-495},
    Abstract = {Three adaptive neuronal models based on neural analogs of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Further, each model can find the most nonredundant and earliest predictor of reinforcement. The behaviors of our models account for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well our models fit empirical data from various animal learning paradigms.}
}

EndNote citation:

%0 Report
%A Lee, Chuen-Chien
%T Modeling the Behavioral Substrates of Associate Learning and Memory: Adaptive Neuronal Models
%I EECS Department, University of California, Berkeley
%D 1989
%@ UCB/CSD-89-495
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/1989/6167.html
%F Lee:CSD-89-495