Physical implementations of neural computation now extend far beyond silicon hardware, encompassing substrates such as memristive devices, photonic circuits, mechanical metamaterials, microfluidic networks, chemical reaction systems, and living neural tissue. By exploiting intrinsic physical processes such as charge transport, wave interference, elastic deformation, mass transport, and biochemical regulation, these substrates can realize neural inference and adaptation directly in matter. As silicon GPU-centered AI faces growing energy and data-movement constraints, physical neural computation is becoming increasingly relevant as a complementary path beyond conventional digital accelerators. This trend is driven in particular by pervasive intelligence, i.e., the deployment of on-device and edge AI across large numbers of resource-constrained systems. In such settings, co-locating computation with sensing and memory can reduce data shuttling and improve efficiency. Meanwhile, physical neural approaches have emerged across disparate disciplines, yet progress remains fragmented, with limited shared terminology and few principled ways to compare platforms. This survey unifies the field by mapping neural primitives to substrate-specific mechanisms, analyzing architectural and training paradigms, and identifying key engineering constraints including scalability, precision, programmability, and I/O interfacing overhead. To enable cross-domain comparison, we introduce a first-order benchmarking scheme based on standardized static and dynamic tasks and physically interpretable performance dimensions. We show that no single substrate dominates across the considered dimensions; instead, physical neural systems occupy complementary operating regimes, enabling applications ranging from ultrafast signal processing and in-memory inference to embodied control and in-sample biochemical decision making.

翻译：物理实现的神经计算如今已远超硅基硬件的范畴，涵盖忆阻器件、光子电路、力学超材料、微流控网络、化学反应系统及活体神经组织等多种基底。通过利用电荷输运、波干涉、弹性形变、质量输运和生化调控等固有物理过程，这些基底可直接在物质层面实现神经推理与自适应。随着以硅基GPU为核心的人工智能面临日益严峻的能耗与数据传输瓶颈，作为超越传统数字加速器的互补路径，物理神经计算正变得愈发重要。这一趋势尤其受到普适智能的驱动，即在大量资源受限系统中部署端侧及边缘人工智能。在此类场景中，将计算与感知及存储协同布局可减少数据搬运，提升能效。与此同时，物理神经方法已分散出现在不同学科领域，然而进展仍呈碎片化态势，缺乏统一的共享术语与为数不多的系统化跨平台比较方法。本综述通过将神经基元映射至特定基底机制、分析架构与训练范式、识别包括可扩展性、精度、可编程性及输入输出接口开销在内的关键工程约束，实现了该领域的统一。为促进跨领域比较，我们提出一种基于标准化静态与动态任务、以及物理可解释性能维度的初级基准方案。研究表明：没有任何单一基底能在所有考量维度上占优；相反，物理神经系统占据互补的工作区间，能够支撑从超快信号处理、存内推理，到具身控制与样本内生化决策等多种应用。