Ising Model based Molecular Associative Memory for Pattern Recall

We combine statistical-mechanical approach for probabilistic image processing with DNA based biomolecular operations to construct associative memory for pattern recall. The statistical properties of the patterns are learned from the exposed examples, stored in memory and are recalled when presented with partial queries. The results show that our proposed memory model retrieves patterns with high recall accuracy.