论文标题
使用成对马尔可夫随机场模型在分子缔合记忆中的模式deoing
Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models
论文作者
论文摘要
我们提出了一个用于模式学习，存储和使用成对马尔可夫随机场（PMRF）模型的硅分子关联记忆模型。我们的基于PMRF的分子关联记忆模型从裸露的示例中提取本地分布的特征，学习并将模式存储在分子关联存储器中，并通过基于DNA计算的操作来定位给定的嘈杂模式。因此，我们的计算分子模型证明了人类记忆的内容 - 地理性的功能。我们的分子模拟结果表明，所学习的模式和去胶模式之间的平均平方误差较低（<0.014），最高30％的噪声。
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.
