Contrastive Hebbian Learning in the Continuous Hopfield Model

This pape.r shows that contrastive Hebbian, the algorithm used in mean field learning, can be applied to any continuous Hopfield model. This implies that non-logistic activation functions as well as self connections are allowed. Contrary to previous approaches, the learning algorithm is derived without considering it a mean field approximation to Boltzmann machine learning. The paper includes a discussion of the conditions under which the function that contrastive Hebbian mini~ mizes can be considered a proper error function, and an analysis of five different training regimes. An appendix provides complete demonstrations and specific instructions on how to implement contrastive Hebbian learning in interactive activation and competition models (a convenient version of the continuous Hopfield model).