小柯机器人

日本东京大学A. J. Genot研究组开发出酶学神经网络的非线性决策。2022年10月19日出版的《自然》杂志发表了这项成果。

研究人员引入了具有可调整权重和偏置的DNA编码的酶学神经元，并将其组装在多层架构中，对非线性可分离区域进行分类。研究人员首先利用神经元的敏锐决策边际来计算10比特上的各种多数函数。然后，研究人员将神经元组成一个两层网络，并在microRNA输入上合成一个参数化的矩形函数系列。最后，研究人员将神经和逻辑计算连接到一个混合回路中，该回路根据细胞大小的液滴中的决策树递归地分割一个浓度平面。这种计算能力和极端的微型化为查询和管理具有复杂内容的分子系统开辟了途径，如液体活检或DNA数据库。

据介绍，人工神经网络已经彻底改变了电子计算。同样，具有神经形态结构的分子网络可以使分子决策达到与基因调控网络相当的水平。非酶学网络原则上可以支持神经形态的架构，并且已经报道了开创性的原理证明。然而，泄漏（即不需要的物种释放），以及灵敏度、速度、准备和缺乏强大的非线性反应等问题，使分层的组成变得微妙，而且相当于多层神经网络的分子分类仍然难以实现（例如，将浓度空间划分为不能线性分离的区域）。

附：英文原文

Title: Nonlinear decision-making with enzymatic neural networks

Author: Okumura, S., Gines, G., Lobato-Dauzier, N., Baccouche, A., Deteix, R., Fujii, T., Rondelez, Y., Genot, A. J.

Issue&Volume: 2022-10-19

Abstract: Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks1,2. Non-enzymatic networks could in principle support neuromorphic architectures, and seminal proofs-of-principle have been reported3,4. However, leakages (that is, the unwanted release of species), as well as issues with sensitivity, speed, preparation and the lack of strong nonlinear responses, make the composition of layers delicate, and molecular classifications equivalent to a multilayer neural network remain elusive (for example, the partitioning of a concentration space into regions that cannot be linearly separated). Here we introduce DNA-encoded enzymatic neurons with tuneable weights and biases, and which are assembled in multilayer architectures to classify nonlinearly separable regions. We first leverage the sharp decision margin of a neuron to compute various majority functions on 10bits. We then compose neurons into a two-layer network and synthetize a parametric family of rectangular functions on a microRNA input. Finally, we connect neural and logical computations into a hybrid circuit that recursively partitions a concentration plane according to a decision tree in cell-sized droplets. This computational power and extreme miniaturization open avenues to query and manage molecular systems with complex contents, such as liquid biopsies or DNA databases.

DOI: 10.1038/s41586-022-05218-7

Source: https://www.nature.com/articles/s41586-022-05218-7

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html