Name
Abstract | ||
|---|---|---|
The focus of the paper is the estimation of the maximum number of states that can be made stable in higher-order extensions of neural network models. Each higher-order neuron in a network of n elements is modeled as a polynomial threshold element of degree d. It is shown that regardless of the manner of operation, or the algorithm used, the storage capacity of the higher-order network is of the order of one bit per interaction weight. In particular, the maximal (algorithm independent) storage capacity realizable in a recurrent network of n higher-order neurons of degree d is of the order of ndd!. A generalization of a spectral algorithm for information storage is introduced and arguments adducing near optimal capacity for the algorithm are presented. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1016/0885-064X(91)90040-5 | Journal of Complexity |
DocType | Volume | Issue |
Journal | 7 | 3 |
ISSN | Citations | PageRank |
0885-064X | 15 | 2.60 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Santosh S. Venkatesh | 1 | 381 | 71.80 |
| Pierre Baldi | 2 | 4626 | 502.51 |