Biological nervous systems constitute important sources of inspiration towards computers that are faster, cheaper, and more energy efficient. Neuromorphic disciplines view the brain as a coevolved system, simultaneously optimizing the hardware and the algorithms running on it. There are clear efficiency gains when bringing the computations into a physical substrate, but we presently lack theories to guide efficient implementations. Here, we present a principled computational model for neuromorphic systems in terms of spatio-temporal receptive fields, based on affine Gaussian kernels over space and leaky-integrator and leaky integrate-and-fire models over time. Our theory is provably covariant to spatial affine and temporal scaling transformations, and with close similarities to the visual processing in mammalian brains. We use these spatio-temporal receptive fields as a prior in an event-based vision task, and show that this improves the training of spiking networks, which otherwise is known as problematic for event-based vision. This work combines efforts within scale-space theory and computational neuroscience to identify theoretically well-founded ways to process spatio-temporal signals in neuromorphic systems. Our contributions are immediately relevant for signal processing and event-based vision, and can be extended to other processing tasks over space and time, such as memory and control.

翻译：生物神经系统为构建更快、更廉价、更节能的计算机提供了重要灵感来源。神经形态学科将大脑视为协同进化的系统，同时优化硬件及其上运行的算法。将计算迁移至物理基底可显著提升能效，但目前我们仍缺乏指导高效实现的理论。本文基于空间上的仿射高斯核与时间上的漏积分器及漏积分点火模型，提出了一套以时空感受野为核心的神经形态系统理论计算模型。该理论在空间仿射变换与时间尺度变换下具有可证明的协变性，并与哺乳动物大脑的视觉处理过程高度相似。我们将这些时空感受野作为事件驱动视觉任务的先验知识，实验表明该方法能有效改善脉冲神经网络的训练——这一过程在事件驱动视觉中素来存在困难。本工作融合了尺度空间理论与计算神经科学，为神经形态系统中时空信号的处理建立了理论基础。我们的研究成果对信号处理与事件驱动视觉具有直接应用价值，并可拓展至记忆、控制等其他时空处理任务。