从感知到智能：神经形态电路与系统的原理
Sensing to Intelligence: Principles for Neuromorphic Circuits and Systems
- Indian Institute of Science（印度科学学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出神经形态工程应由计算原理而非生物相似性定义，并从感知、计算、记忆等角度总结设计原则，强调物理状态嵌入计算。.
AI中文摘要：
神经形态工程始于这样一个理念：系统本身的物理行为可用于计算，其灵感来源于神经系统在时间中感知、适应和演化的方式。该领域此后已远远超出其早期的模拟电路范畴，扩展到包括基于事件的传感器、脉冲处理器、新兴存储器件、混合信号系统以及大规模神经加速器。然而，随着这种扩展，“神经形态”的含义变得越来越宽泛。在本展望文章中，我们认为神经形态工程既不应由与生物组分的相似性来定义，也不应由任何特定的器件、信号表示或基底来定义。其潜力在于识别生物系统中的计算原理，并将其转化为人工机器的组织与动力学。当所引用的生物或物理原理在信息如何表示、状态如何演化或完整系统实现何种能力方面起到因果性的、与设计相关的角色时，该系统才是真正的神经形态。我们跨感知、集体计算、记忆、学习与交互来阐述这一观点。适应、非线性动力学、吸引子结构、变异性和闭环行动展示了计算如何嵌入机器演化的物理状态中。从这些例子中，我们识别出一组神经形态系统的设计原理。随着神经形态工程迈向更广泛的部署，这一观点将核心问题从机器与神经系统有多相似，转变为当它们的原理被理解并有意识地转化为新机器时，何种计算成为可能。
英文摘要：
Neuromorphic engineering began with the idea that the physical behavior of a system could itself be used for computation, taking inspiration from the way nervous systems sense, adapt, and evolve in time. The field has since expanded far beyond its early analog circuits to include event-based sensors, spiking processors, emerging memory devices, mixed-signal systems, and large-scale neural accelerators. With this expansion, however, the meaning of neuromorphic has become increasingly broad. In this Perspective, we argue that neuromorphic engineering should be defined neither by resemblance to biological components nor by any particular device, signal representation, or substrate. Its potential lies in identifying computational principles in biological systems and translating them into the organization and dynamics of artificial machines. A system is truly neuromorphic when the invoked biological or physical principle plays a causal, design-relevant role in how information is represented, how state evolves, or what capability the complete system achieves. We develop this view across sensing, collective computation, memory, learning, and interaction. Adaptation, nonlinear dynamics, attractor structure, variability, and closed-loop action illustrate how computation can be embedded in a machine's evolving physical state. From these examples, we identify a set of design principles for neuromorphic systems. As neuromorphic engineering moves toward wider deployment, this perspective shifts the central question from how closely machines resemble nervous systems to what computation becomes possible when their principles are understood and deliberately translated into new machines.
