Elementary units characterized by a threshold-linear (graded) response have been argued to model single neurons in auto-associative networks more realistically than binary units. The different way local activity is constrained in the two representations is shown here to have important consequences for the spin-glass-like properties of otherwise equivalent systems. In particular, in contrast with their binary counterparts, the threshold-linear Sherrington-Kirkpatrick model is stable with respect to replica symmetry-breaking (RSB), while threshold-linear fully connected neural networks with covariance learning are RSB unstable only in a very restricted region of their phase diagram. Whether or not spin-glass effects dominate attractor dynamics is suggested to affect considerably, among other things, the ability of auto-associative memories to encode new information.

Are spin-glass effects relevant to understanding realistic auto-associative networks? / Treves, Alessandro. - In: JOURNAL OF PHYSICS. A, MATHEMATICAL AND GENERAL. - ISSN 0305-4470. - 24:11(1991), pp. 2645-2654. [10.1088/0305-4470/24/11/029]

Are spin-glass effects relevant to understanding realistic auto-associative networks?

Treves, Alessandro
1991

Abstract

Elementary units characterized by a threshold-linear (graded) response have been argued to model single neurons in auto-associative networks more realistically than binary units. The different way local activity is constrained in the two representations is shown here to have important consequences for the spin-glass-like properties of otherwise equivalent systems. In particular, in contrast with their binary counterparts, the threshold-linear Sherrington-Kirkpatrick model is stable with respect to replica symmetry-breaking (RSB), while threshold-linear fully connected neural networks with covariance learning are RSB unstable only in a very restricted region of their phase diagram. Whether or not spin-glass effects dominate attractor dynamics is suggested to affect considerably, among other things, the ability of auto-associative memories to encode new information.
24
11
2645
2654
Treves, Alessandro
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11767/85752
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