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.

中文翻译：

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使用成对马尔可夫随机场模型在分子联想记忆中进行模式去噪

我们使用 Pairwise Markov Random Field (PMRF) 模型提出了一种用于模式学习、存储和去噪的计算机分子关联记忆模型。我们基于 PMRF 的分子联想记忆模型从暴露的例子中提取局部分布的特征，学习并将模式存储在分子联想记忆中，并通过基于 DNA 计算的操作对给定的噪声模式进行降噪。因此，我们的计算分子模型展示了人类记忆的内容寻址功能。我们的分子模拟结果表明，学习模式和去噪模式之间的平均均方误差很低 (< 0.014)，高达 30% 的噪声。