Introduction to the theory of neural computation
Introduction to the theory of neural computation
The consolidation of learning during sleep: comparing the pseudorehearsal and unlearning accounts
Neural Networks - Special issue on organisation of computation in brain-like systems
On the problem of spurious patterns in neural associative memory models
IEEE Transactions on Neural Networks
Sequential learning in neural networks: A review and a discussion of pseudorehearsal based methods
Intelligent Data Analysis
Stable-yet-switchable (SyS) attractor networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Hopfield/constraint satisfaction type networks can be used to learn (autoassociate) patterns. Random inputs to the network will sometimes converge on states which are learned patterns, and sometimes converge on states which are unlearned/spurious. It would be useful for many reasons to be able to tell whether or not a given state was learned or spurious. In this paper we present a robust and general method, based on 'energy profiles', which allows us to make this distinction. We briefly describe related research, and note links with the study of recall, recognition and familiarity in the psychological literature.