Corrective memory by a symmetric sparsely encoded network
Yoram Baram · IEEE Transactions on Information Theory · 1994
A neural network that retrieves stored binary vectors, when probed by possibly corrupted versions of them, is presented. It employs sparse ternary internal coding and autocorrelation (Hebbian) storage. It is symmetrically structured and, consequently, can be folded into a feedback configuration. Bounds on the network parameters are derived from probabilistic considerations. It is shown that when the input dimension is n, the proportional activation radius is /spl rho/ and the network size is 2/sup /spl nu/n/ with /spl nu/>1-h/sub 2/(/spl rho/), the equilibrium capacity is at least 2/sup /spl alpha/n//8n/spl rho/(1-/spl rho/) for any /spl alpha/>