Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models
This addresses pattern storage and retrieval for molecular computing, but appears incremental as it applies existing PMRF methods to a new domain.
The paper tackled the problem of pattern denoising in molecular associative memory by proposing a Pairwise Markov Random Field model, achieving low mean squared error (< 0.014) for up to 30% noise.
We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model. Our PMRF-based molecular associative memory model extracts locally distributed features from the exposed examples, learns and stores the patterns in the molecular associative memory and denoises the given noisy patterns via DNA computation based operations. Thus, our computational molecular model demonstrates the functionalities of content-addressability of human memory. Our molecular simulation results show that the averaged mean squared error between the learned and denoised patterns are low (< 0.014) up to 30% of noise.