In this paper a binary associative network model with minimal number of connections is examined and its microscopic dynamics exactly studied. The knowledge of its time behavior allows us to determine a learning rule which realizes a one-step recalling associative memory. Its storage capacity is also analyzed with randomly distributed patterns and is proved to be O(log n) in the worst case, n being the number of neurons and connections, but to increase considerably when the patterns to be memorized are correlated. Spurious states are also investigated.

An Associative Memory Model with Minimum Connectivity

TAGLIAFERRI, Roberto
1992

Abstract

In this paper a binary associative network model with minimal number of connections is examined and its microscopic dynamics exactly studied. The knowledge of its time behavior allows us to determine a learning rule which realizes a one-step recalling associative memory. Its storage capacity is also analyzed with randomly distributed patterns and is proved to be O(log n) in the worst case, n being the number of neurons and connections, but to increase considerably when the patterns to be memorized are correlated. Spurious states are also investigated.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/3384477
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