In this work, we study minimum data rate tracking of a dynamical system under a neuromorphic event-based sensing paradigm. We begin by bridging the gap between continuous-time (CT) system dynamics and information theory's causal rate distortion theory. We motivate the use of non-singular source codes to quantify bitrates in event-based sampling schemes. This permits an analysis of minimum bitrate event-based tracking using tools already established in the control and information theory literature. We derive novel, nontrivial lower bounds to event-based sensing, and compare the lower bound with the performance of well-known schemes in the established literature.

翻译：本文研究在神经形态事件驱动感知范式下，动态系统的最小数据率跟踪问题。我们首先建立连续时间系统动态与信息理论中因果率失真理论之间的关联，论证在事件驱动采样方案中使用非奇异信源编码量化比特率的合理性。这使我们能够运用控制理论与信息理论文献中已有的工具，分析事件驱动跟踪的最小比特率。我们推导出事件驱动感知的新型非平凡下界，并将该下界与现有文献中经典方案的性能进行了比较。