Concept of stochastic memory & data retrieval using artificial neural networks increasing memory capacity and data security by data overlapping

This paper presents the concept of a physical memory whose state is dependent on a stochastic variable. The stochastic parameter used is temperature. This gives way to efficient space utilization by overlapping data patches upon existing data and overcoming the upper limit of storage space, i.e. more storage data with less hardware and more data security. Furthermore, the paper goes on to present retrieval solutions, for such overlapped data patch structures, using Deep Belief Networks made up of layers of. Restricted Boltzmann machines (RBM), along with mapping with a Bidirectional Associative Memory (BAM).