On the capacity per synapse
- 7 September 1990
- journal article
- Published by IOP Publishing in Journal of Physics A: General Physics
- Vol. 23 (17) , L935-L938
- https://doi.org/10.1088/0305-4470/23/17/016
Abstract
The optimal storage capacity of a perceptron with a finite fraction of sign constrained weights, which are prescribed a priori, is examined. The storage capacity is calculated by considering the fractional volume of weights which can store a set of alpha N random patterns, where N is the size of the input. It is found that in the case where (1-s) N weights are sign constrained the capacity is (1+s)/2 alpha Gc(k), where alpha Gc(k) is the maximal storage capacity in Gardner's case and k is a stability parameter.Keywords
This publication has 6 references indexed in Scilit:
- Capacity of neural networks with discrete synaptic couplingsJournal of Physics A: General Physics, 1990
- The interaction space of neural networks with sign-constrained synapsesJournal of Physics A: General Physics, 1989
- Perceptron learning with sign-constrained weightsJournal of Physics A: General Physics, 1989
- Storage capacity of memory networks with binary couplingsJournal de Physique, 1989
- 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