Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. We discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.

神经形态计算从大脑获得灵感，以创建用于信息处理的节能硬件，能够执行高度复杂的任务。用标准电子设备构建的系统通过模仿大脑的分布式拓扑结构，从而获得了速度和能量上的收益。扩大此类系统并将其能源使用，速度和性能提高几个数量级需要硬件方面的革命。我们讨论了在算法中使用更多的物理学方法以及用于数据处理的纳米级材料如何对神经形态计算领域产生重大影响。我们使用电阻开关材料，光子学，自旋电子学和其他技术，回顾利用物理来增强人工神经网络的计算能力的惊人结果。