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：生物神经系统为更快、更廉价及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系统，同时优化硬件及其运行算法。将计算过程迁移至物理基质可带来显著的效率提升，但目前我们尚缺乏指导高效实现的理论框架。本文提出了一种基于时空感受野的神经形态系统典型计算模型，该模型在空间域采用仿射高斯核，在时间域采用漏积分器和漏积分-发放模型。该理论可证明对空间仿射变换与时间尺度变换具有协变性，且与哺乳动物大脑的视觉处理机制高度相似。我们将这些时空感受野作为事件驱动视觉任务的先验信息，结果表明这能改善脉冲神经网络的训练效果——该问题在事件驱动视觉领域历来被视为难点。本研究整合了尺度空间理论与计算神经科学的成果，为神经形态系统中时空信号处理奠定了具有理论依据的路径。我们的成果可直接应用于信号处理与事件驱动视觉领域，并可拓展至空间与时间维度的其他处理任务，如记忆与控制。