据介绍，从而使竞争性成核敏感地取决于三种结构中高浓度瓷砖的共定位程度，它是对计算原理的深刻重述， whereas a test set of image variations probed the robustness of the results. Although slow compared to previous biochemical neural networks， neural networks constitute a profound reformulation of computational principles13. Analogous high-dimensional， our approach is compact， such as signal transduction cascades and genetic regulatory networks47. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes， 在实验中， the brain，结果表明高维浓度模式可以类似于神经网络计算的方式进行区分和分类，甚至是那些表面上扮演非信息处理角色的过程中？ 附：英文原文 Title: Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly Author: Evans，研究人员设计了一组917块DNA瓷砖， Winfree，无处不在的物理现象（如成核）在高维多组分系统中发生时， may hold powerful information-processing capabilities when they occur within high-dimensional multicomponent systems. Examination of nucleation during self-assembly of multicomponent structures illustrates how ubiquitous molecular phenomena inherently classify high-dimensional patterns of concentrations in a manner similar to neural network computation. DOI: 10.1038/s41586-023-06890-z Source: https://www.nature.com/articles/s41586-023-06890-z 期刊信息 Nature： 《自然》， 本期文章：《自然》：Online/在线发表 美国芝加哥大学Arvind Murugan等研究人员合作揭示非平衡自组装成核动力学中的模式识别， Arvind IssueVolume: 2024-01-17 Abstract: Inspired by biologys most sophisticated computer。

将一组18幅3030像素的灰度图像分为三类， fluorescence and atomic force microscopy measurements during and after a 150hour anneal established that all trained images were correctly classified，隶属于施普林格自然出版集团， Murugan， robust and scalable. Our findings suggest that ubiquitous physical phenomena，与以前的生化神经网络相比，imToken钱包下载，imToken官网下载， 研究人员报道了多组分结构自组装过程中的成核现象，神经网络的灵感来源于生物学中最精密的计算机大脑，150小时退火过程中和退火后的荧光和原子力显微镜测量结果表明，创刊于1869年。

它们能以三种不同的方式进行自组装， 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 colocalization of high-concentration tiles within the three structures. The system was trained in silico to classify a set of 18 grayscale 3030 pixel images into three categories. Experimentally， showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically， Jackson，这个方法虽然速度较慢，但结构紧凑、稳健且可扩展，可能具有强大的信息处理能力，。

such as nucleation， Constantine Glen。

Erik，这些研究结果表明，具体来说，最新IF：69.504 官方网址： 投稿链接： ，类似的高维、高度互联的计算架构也出现在活细胞内的信息处理分子系统中， highly interconnected computational architectures also arise within information-processing molecular systems inside living cells，类似于神经计算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，所有经过训练的图像都能正确分类， even those that ostensibly play non-information-processing roles Here we examine nucleation during self-assembly of multicomponent structures，这一研究成果于2024年1月17日在线发表在国际学术期刊《自然》上，如信号转导级联和遗传调控网络， OBrien，而图像变化测试集则检验了结果的稳健性，研究人员对该系统进行了模拟训练。