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:在生物学最尖端的计算机、大脑和神经网络的启发下,神经网络构成了对计算原理的深刻改造。 显著的是,在生命细胞内的信息处理分子系统中,比如信号传输级联和基因监管网络中,也会出现类似的高维、高度相互联系的计算结构。 神经形态集体模式可能会在其他物理和化学过程中被更广泛地发现,甚至那些表面上发挥非信息处理作用,如蛋白合成、新陈代谢或结构自组作用的系统? 我们在这里检查多构件结构自我组装期间的核化,显示高维的集中模式可以被区分和分类,其方式与神经网络计算类似。 具体地说,我们设计了一套917个DNA图案,可以以三种替代方式自我合成。 竞争性集合模式可能更为广泛,取决于三个结构内高浓缩质结构内高集成的共位结构(例如蛋白合成、新陈代谢或结构自体)的共同定位。 这个系统在可以将一组18个灰度的多面30平方图像分为三类。 实验性, 度的高度的集中度模式和原子结构将一个精度的精度的精度模式分类化的精度模式分类可以被区分地分类化, 。 和精确的精准的精准的精准的精度 和原子的精度的精度的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精准的精确的精细的精细的精细的精细的精准的精准的精准的精准的精准的精准的精准的精确的精准的精准的