Journal ArticleDOI
Sensibility of neural networks
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TLDR
A susceptibility-like quantity which characterises the sensibility of neural models can be calculated without the replicas in the Hopfield model and may be useful in the investigation of highly nonlinear learning rules.Abstract:
The author introduces and investigates a susceptibility-like quantity which characterises the sensibility of neural models. It can be calculated without the replicas in the Hopfield model and may be useful in the investigation of highly nonlinear learning rules.read more
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
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Spin-glass models of neural networks.
TL;DR: Two dynamical models, proposed by Hopfield and Little to account for the collective behavior of neural networks, are analyzed and it is shown that the long-time behavior of the two models is identical, for all temperatures below a transition temperature ${T}_{c}$.
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Storing infinite numbers of patterns in a spin-glass model of neural networks.
TL;DR: The Hopfield model for a neural network is studied in the limit when the number of stored patterns increases with the size of the network, as $p=\ensuremath{\alpha}N$, and it is shown that, despite its spin-glass features, the model exhibits associative memory.
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‘Unlearning’ has a stabilizing effect in collective memories
TL;DR: Although the model was not motivated by higher nervous function, the system displays behaviours which are strikingly parallel to those needed for the hypothesized role of ‘unlearning’ in rapid eye movement (REM) sleep.
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Networks of Formal Neurons and Memory Palimpsests
TL;DR: A general formulation allows for an exploration of some basic issues in learning theory and two learning schemes are constructed, which avoid the overloading deterioration and keep learning and forgetting, with a stationary capacity.