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Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2020-03-30 , DOI: arxiv-2003.13720
Marko Vasic and Cameron Chalk and Sarfraz Khurshid and David Soloveichik

Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields. A key remaining challenge lies in developing programming paradigms for molecular computation that are well-aligned with the underlying chemical hardware and do not attempt to shoehorn ill-fitting electronics paradigms. We discover a surprisingly tight connection between a popular class of neural networks (binary-weight ReLU aka BinaryConnect) and a class of coupled chemical reactions that are absolutely robust to reaction rates. The robustness of rate-independent chemical computation makes it a promising target for bioengineering implementation. We show how a BinaryConnect neural network trained in silico using well-founded deep learning optimization techniques, can be compiled to an equivalent chemical reaction network, providing a novel molecular programming paradigm. We illustrate such translation on the paradigmatic IRIS and MNIST datasets. Toward intended applications of chemical computation, we further use our method to generate a chemical reaction network that can discriminate between different virus types based on gene expression levels. Our work sets the stage for rich knowledge transfer between neural network and molecular programming communities.

中文翻译:

深度分子编程:二元权重 ReLU 神经网络的自然实现

在与传统电子学不兼容的分子环境中嵌入计算有望在合成生物学、医学、纳米制造和其他领域产生广泛的影响。剩下的一个关键挑战在于为分子计算开发编程范式,该范式与基础化学硬件完全一致,并且不会试图硬塞不合适的电子范式。我们发现一类流行的神经网络(二元权重 ReLU 又名 BinaryConnect)和一类对反应速率绝对稳健的耦合化学反应之间有着惊人的紧密联系。与速率无关的化学计算的稳健性使其成为生物工程实施的有希望的目标。我们展示了如何使用成熟的深度学习优化技术在计算机上训练的 BinaryConnect 神经网络可以编译为等效的化学反应网络,从而提供一种新的分子编程范式。我们在典型的 IRIS 和 MNIST 数据集上说明了这种转换。对于化学计算的预期应用,我们进一步使用我们的方法来生成化学反应网络,该网络可以根据基因表达水平区分不同的病毒类型。我们的工作为神经网络和分子编程社区之间的丰富知识转移奠定了基础。对于化学计算的预期应用,我们进一步使用我们的方法来生成化学反应网络,该网络可以根据基因表达水平区分不同的病毒类型。我们的工作为神经网络和分子编程社区之间的丰富知识转移奠定了基础。对于化学计算的预期应用,我们进一步使用我们的方法来生成化学反应网络,该网络可以根据基因表达水平区分不同的病毒类型。我们的工作为神经网络和分子编程社区之间的丰富知识转移奠定了基础。
更新日期:2020-07-01
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