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.
Cite
@article{arxiv.2309.06504,
title = {Minimum Bitrate Neuromorphic Encoding for Continuous-Time Gauss-Markov Processes},
author = {Travis Cuvelier and Ronald Ogden and Takashi Tanaka},
journal= {arXiv preprint arXiv:2309.06504},
year = {2023}
}