生物学与机器学习中的回路设计 I：随机网络与降维
Circuit design in biology and machine learning. I. Random networks and dimensional reduction
AI总结：
本文通过剖析随机连接网络、降维与趋势预测两个经典机器学习模型，探讨生物回路设计的演化机制基础，提出生物学问题的机器学习对应物可为生物回路设计研究提供假设。
摘要：
生物回路是一种神经级联或生化级联，接收输入并产生输出。在生命演化史上，生物回路是如何学会应对环境挑战的？答案无疑契合杜布赞斯基的名言：“若无演化之光，生物学的一切都毫无意义。”但这句名言并未提及自然选择试错学习所依赖的机制基础，而这正是我们必须理解的内容。设计生物回路的学习过程究竟如何运作？通过研究塑造这些回路的过程，我们能对生物回路的结构与功能获得多少洞见？\n由于生命的回路往往需要解决与机器学习面临的相同问题，例如环境追踪、稳态控制、降维或分类，我们可以先研究机器学习如何设计计算回路来解决问题。随后我们可以追问：这些计算回路能为理解生物回路的设计提供多少洞见？在用于解决问题的特定回路设计上，生物学与计算机存在多大差异？\n本文逐步剖析两个经典机器学习模型，为分析关于生物回路设计的广泛问题奠定基础。其中一项洞见是随机连接网络的惊人能力。另一项洞见是嵌入在生物回路内部的环境内部模型的核心作用，这一点通过一个降维与趋势预测模型得到了阐释。总体而言，生物学中的许多挑战在机器学习中都有对应问题，这为研究生物回路的设计方式提供了相关假设。
英文摘要：
A biological circuit is a neural or biochemical cascade, taking inputs and producing outputs. How have biological circuits learned to solve environmental challenges over the history of life? The answer certainly follows Dobzhansky's famous quote that ``nothing in biology makes sense except in the light of evolution.'' But that quote leaves out the mechanistic basis by which natural selection's trial-and-error learning happens, which is exactly what we have to understand. How does the learning process that designs biological circuits actually work? How much insight can we gain about the form and function of biological circuits by studying the processes that have made those circuits? Because life's circuits must often solve the same problems as those faced by machine learning, such as environmental tracking, homeostatic control, dimensional reduction, or classification, we can begin by considering how machine learning designs computational circuits to solve problems. We can then ask: How much insight do those computational circuits provide about the design of biological circuits? How much does biology differ from computers in the particular circuit designs that it uses to solve problems? This article steps through two classic machine learning models to set the foundation for analyzing broad questions about the design of biological circuits. One insight is the surprising power of randomly connected networks. Another is the central role of internal models of the environment embedded within biological circuits, illustrated by a model of dimensional reduction and trend prediction. Overall, many challenges in biology have machine learning analogs, suggesting hypotheses about how biology's circuits are designed.
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