Universality in the space of interactions for network models
Open Access
- 21 June 1989
- journal article
- Published by IOP Publishing in Journal of Physics A: General Physics
- Vol. 22 (12) , 2031-2038
- https://doi.org/10.1088/0305-4470/22/12/008
Abstract
By modifying the measure used to sum over coupling matrices, the authors generalise Gardner's (1988) calculation of the fractional interaction-space volume and storage capacity of neural network models. They also compute the local field distribution for the network. The generalised measure allows one to consider networks with a wide variety of properties away from saturation, but they find that the original results for saturated networks are universal for all well behaved measures. Other universality classes including those containing Hebb matrices and pseudo-inverse matrices are obtained by considering singular measures.Keywords
This publication has 12 references indexed in Scilit:
- Statistical mechanics of neural networks near saturationPublished by Elsevier ,2004
- The roles of stability and symmetry in the dynamics of neural networksJournal of Physics A: General Physics, 1988
- Optimal storage properties of neural network modelsJournal of Physics A: General Physics, 1988
- The space of interactions in neural network modelsJournal of Physics A: General Physics, 1988
- Domains of attraction in neural networksJournal de Physique, 1988
- Maximum Storage Capacity in Neural NetworksEurophysics Letters, 1987
- Associative recall of memory without errorsPhysical Review A, 1987
- Information storage and retrieval in spin-glass like neural networksJournal de Physique Lettres, 1985
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982
- A theory for the acquisition and loss of neuron specificity in visual cortexBiological Cybernetics, 1979