College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.
Phys Chem Chem Phys. 2024 Apr 17;26(15):11854-11866. doi: 10.1039/d3cp02811a.
With the advent of the post-Moore's Law era, the development of traditional silicon-based computers has reached its limit, and there is an urgent need to develop new computing technologies to meet the needs of science, technology, and daily life. Due to its super-strong parallel computing capability and outstanding data storage capacity, DNA computing has become an important branch and hot research topic of new computer technology. DNA enzyme-free hybridization reaction technology is widely used in DNA computing, showing excellent performance in computing power and information processing. Studies have shown that DNA molecules not only have the computing function of electronic devices, but also exhibit certain human brain-like functions. In the field of artificial intelligence, activation functions play an important role as they enable artificial intelligence systems to fit and predict non-linear and complex variable relationships. Due to the difficulty of implementing activation functions in DNA computing, DNA circuits cannot easily achieve all the functions of artificial intelligence. DNA circuits need to rely on electronic computers to complete the training and learning process. Based on the parallel computing characteristics of DNA computing and the kinetic features of DNA molecule displacement reactions, this paper proposes a new activation function. This activation function can not only be easily implemented by DNA enzyme-free hybridization reaction reactions, but also has good nesting properties in DNA circuits, and can be cascaded with other DNA reactions to form a complete DNA circuit. This paper not only provides the mathematical analysis of the proposed activation function, but also provides a detailed analysis of its kinetic features. The activation function is then nested into a nonlinear neural network for DNA computing. This system is capable of fitting and predicting a certain nonlinear function.
随着后摩尔定律时代的到来，传统硅基计算机的发展已经达到极限，因此迫切需要开发新的计算技术来满足科学、技术和日常生活的需求。由于其超强的并行计算能力和出色的数据存储能力，DNA 计算已成为新计算机技术的一个重要分支和热门研究课题。DNA 无酶杂交反应技术广泛应用于 DNA 计算中，在计算能力和信息处理方面表现出优异的性能。研究表明，DNA 分子不仅具有电子设备的计算功能，而且还表现出某些类似人脑的功能。在人工智能领域，激活函数作为人工智能系统拟合和预测非线性和复杂变量关系的重要工具，发挥着重要作用。由于在 DNA 计算中实现激活函数具有一定的难度，因此 DNA 电路无法轻易实现人工智能的所有功能。DNA 电路需要依赖电子计算机来完成训练和学习过程。本文基于 DNA 计算的并行计算特点和 DNA 分子位移反应的动力学特征，提出了一种新的激活函数。该激活函数不仅可以通过 DNA 无酶杂交反应轻松实现，而且在 DNA 电路中具有良好的嵌套特性，可以与其他 DNA 反应级联，形成完整的 DNA 电路。本文不仅提供了所提出的激活函数的数学分析，还详细分析了其动力学特征。然后，将激活函数嵌套到用于 DNA 计算的非线性神经网络中。该系统能够拟合和预测某些非线性函数。
