High Capacity Neural Networks for Familiarity Discrimination

R. Bogacz, M. W. Brown, C. Giraud-Carrier, High Capacity Neural Networks for Familiarity Discrimination. Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN99), pp. 773–778. September 1999. PDF, 64 Kbytes.


This paper presents two new novelty discrimination models for uncorrelated patterns based on neural modelling. The first model uses a single neuron with Hebbian learning and works well when the number of memorised patterns is less than 0.046N (N, the number of inputs). The second model is based on checking the value of the energy function of a Hopfield network. By sacrificing the ability to extract patterns - not needed for familiarity detection - an amazing jump from the normal capacity for retrieval of 0.145N to a capacity for novelty discrimination of 0.023N2 is achieved. In addition, both models give some insight on the effect of deja vu, since there is always a very small probability of detecting novel patterns as familiar.

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