论文标题
在物理系统中没有神经元学习
Learning without neurons in physical systems
论文作者
论文摘要
传统上，学习是在生物或计算系统中研究的。学习框架解决硬性逆问题的力量为开发“物理学习”提供了一个吸引人的案例，即物理系统在没有计算设计的情况下独自采用理想的属性。最近，人们意识到，大量的物理系统可以通过本地学习规则进行物理学习，自主对其参数进行自主调整，以响应观察到的使用示例。我们回顾了新兴的体育学习领域的最新工作，描述了从分子自组装到流动网络和机械材料等领域的理论和实验进步。物理学习机器比计算机设计的机器具有多种实用的优势，特别是通过不需要准确的系统模型，以及它们自主适合随着时间的变化需求的能力。作为理论构造，物理学习机器对物理约束如何修改抽象学习理论具有新的观点。
Learning is traditionally studied in biological or computational systems. The power of learning frameworks in solving hard inverse-problems provides an appealing case for the development of `physical learning' in which physical systems adopt desirable properties on their own without computational design. It was recently realized that large classes of physical systems can physically learn through local learning rules, autonomously adapting their parameters in response to observed examples of use. We review recent work in the emerging field of physical learning, describing theoretical and experimental advances in areas ranging from molecular self-assembly to flow networks and mechanical materials. Physical learning machines provide multiple practical advantages over computer designed ones, in particular by not requiring an accurate model of the system, and their ability to autonomously adapt to changing needs over time. As theoretical constructs, physical learning machines afford a novel perspective on how physical constraints modify abstract learning theory.
