Proceedings of the Annual Meeting of the Cognitive Science Society

In connectionist networks, newly-learned information can completely destroy previouslylearned information unless the network is continually retrained on the old information. This behavior, known as catastrophic forgetting, is unacceptable both for practical purposes and as a model of mind. This paper advances the claim that catastrophic forgetting is a direct consequence of the overlap of the system's distributed representations and can be reduced by reducing this overlap. A simple algorithm is presented that allows a standard feedforward backpropagation network to develop semidistributed representations, thereby significantly reducing the problem of catastrophic forgetting.