MEMORY RETRIEVAL IN OPTIMAL SUBSPACES

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Abstract
A simple dynamical scheme for Attractor Neural Networks with non-monotonic three state effective neurons is discussed. For the unsupervised Hebb learning rule, we give some basic numerical results which are interpreted in terms of a combinatorial task realized by the dynamical process (dynamical selection of optimal subspaces). An analytical estimate of optimal performance is given by resorting to two different simplified versions of the model. We show that replica symmetry breaking is required since the replica symmetric solutions are unstable.

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