DeepAI AI Chat
Log In Sign Up

Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models

by   Dharani Punithan, et al.
Seoul National University

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


page 7

page 8

page 9

page 10


Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field

In this paper, we consider Bayesian image denoising based on a Gaussian ...

mrf2d: Markov random field image models in R

Markov random fields on two-dimensional lattices are behind many image a...

Learning of signaling networks: molecular mechanisms

Molecular processes of neuronal learning have been well-described. Howev...

Unsupervised learning of features and object boundaries from local prediction

A visual system has to learn both which features to extract from images ...

Fractional Denoising for 3D Molecular Pre-training

Coordinate denoising is a promising 3D molecular pre-training method, wh...

On the trade-off between complexity and correlation decay in structural learning algorithms

We consider the problem of learning the structure of Ising models (pairw...

Logical N-AND Gate on a Molecular Turing Machine

In Boolean algebra, it is known that the logical function that correspon...