An Associative Neural Network Model of Classical Conditioning
نویسندگان
چکیده
In this paper we present a new associative model of classical conditioning based on a neural network. The new model is compared with a number of other well-known models of classical conditioning. The experiments that are used to evaluate the new model are commonly used and they represent the set of tasks that a model of classical conditioning needs to address in order to be successful. The new neural network based model is composed of a number of interconnected Bayesian confidence propagating neural networks (BCPNNs). The BCPNN implements Hebbian learning. This new BCPNN based model sorts under the category of associative models. A key concept of this model is to make a closer tie between the output and the underlying neural activity. The BCPNN model does not use delaylines as many other models of conditioning. The output from the BCPNN model fit the results of classical conditioning experiments.
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