Inspired by biology's most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Remarkably, analogous high-dimensional, highly-interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks. Might neuromorphic collective modes be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles such as protein synthesis, metabolism, or structural self-assembly? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically, we design a set of 917 DNA tiles that can self-assemble in three alternative ways such that competitive nucleation depends sensitively on the extent of co-localization of high-concentration tiles within the three structures. The system was trained in-silico to classify a set of 18 grayscale 30 x 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy monitoring during and after a 150-hour anneal established that all trained images were correctly classified, while a test set of image variations probed the robustness of the results. While slow compared to prior biochemical neural networks, our approach is surprisingly compact, robust, and scalable. This success suggests that ubiquitous physical phenomena, such as nucleation, may hold powerful information processing capabilities when scaled up as high-dimensional multicomponent systems.

翻译：受生物学中最复杂的计算机——大脑的启发，神经网络构成了计算原理的深刻重塑。值得注意的是，类似的高维、高度互联的计算架构也出现在活细胞内信息处理的分子系统中，例如信号转导级联和基因调控网络。类神经形态的集体模式是否可能更广泛地存在于其他物理和化学过程中，即便是那些表面上不起信息处理作用的过程，如蛋白质合成、代谢或结构自组装？在此，我们研究了多组分结构自组装过程中的成核现象，表明浓度的高维模式可以以类似于神经网络计算的方式进行判别和分类。具体而言，我们设计了一组917个DNA瓦片，它们可以按三种替代方式自组装，使得竞争性成核高度依赖于三个结构中高浓度瓦片的共定位程度。该系统通过计算机训练，将18幅30×30像素的灰度图像分类为三个类别。实验上，在150小时退火过程中及之后进行的荧光和原子力显微镜监测证实，所有训练图像均被正确分类，同时一组图像变体的测试集检验了结果的鲁棒性。虽然与先前的生化神经网络相比速度较慢，但我们的方法出奇地紧凑、鲁棒且可扩展。这一成功表明，当作为高维多组分系统规模化时，诸如成核等普遍存在的物理现象可能蕴藏着强大的信息处理能力。