T Geszti and F Pazmandi 1987 J. Phys. A: Math. Gen. 20 L1299 doi:10.1088/0305-4470/20/18/014
T Geszti and F Pazmandi
Show affiliationsIn a bounded-synapses version of Hopfield's model (1984) for neural networks the quasienergy of a given memory, which is approximately equal to the depth of the corresponding energy well is calculated exactly by treating the change of a synaptic strength on learning as a random walk within bounds. Attractors corresponding to stored memories are found to be considerably flattened before serious retrieval errors arise. This allows dream sleep to be interpreted as random recall and relearning of fresh strong memories, in order to stack them on top of weak incidental memory imprints of a day.
07.05.Mh Neural networks, fuzzy logic, artificial intelligence
68T05 Learning and adaptive systems (See also 68Q32, 91E40)
92B20 Neural networks, artificial life and related topics (See also 68T05, 82C32, 94Cxx)
Issue 18 (21 December 1987)
T Geszti and F Pazmandi 1987 J. Phys. A: Math. Gen. 20 L1299
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