Modeling the Neural Substrates of Associative Learning and Memory: A Computational Approach

We develop a computational model of the neural substrates of elementary associative learning, using the neural circuits known to govern classical conditioning of the gill-withdrawal response of Aplysia. Building upon the theoretical efforts of Hawkins and Kandel (1984), we use this model to demonstrate that several higher order features of classical conditioning could be elaborations of the known cellular mechanisms for simple associative learning. Indeed, the current circuit model robustly exhibits many of the basic phenomena of classical conditioning. The model, however, requires a further assumption (regarding the form of the acquisition function) to predict asymptotic blocking and contingency learning. In addition, if extinction is mediated by the nonassociative mechanism of habituation-rather than the associative process postulated by Rescorla and Wagner (1972)-then we argue that additional mechanisms must be specified to resolve a conflict between acquisition and maintenance of learned associations. We suggest several possible extensions to the circuit model at both the cellular and molecular levels that are consistent with the known Aplysia physiology and that could, in principle, generate classical conditioning behavior.