Writing to the Hopfield Memory via Training a Recurrent Network

HAN BAO, Richong Zhang, Yongyi Mao, Jinpeng Huai

Abstract:

We consider the problem of writing on a Hopfield network.
We cast the problem as a supervised learning problem by observing a
simple link between the update equations of Hopfield network and recurrent
neural networks. We compare the new writing protocol to existing
ones and experimentally verify its effectiveness. Our method not only has
a better ability of noise recovery, but also has a bigger capacity compared
to the other existing writing protocols.