Why Do We Need Yet Another Controller? Traditional controllers are remarkably efficient when the target system is stable and well-predicted. However, reality is rarely that ideal. When the environment changes, sensor statistics shift in real-time, and unexpected disturbances occur, the adaptability of classical controllers quickly reaches its limits. Our core question was not, "Can we beat another static benchmark?" Instead, we asked: **"Can a biologically interpretable, very small network maintain online adaptation and solve real-world control problems on edge hardware?"** This problem awareness originated from the harshest industrial environments, and the key to the solution was found in biological control systems (e.g., smooth pursuit of the eye, saccades, antagonistic motor groups, and delayed reward). The **Spiking Liquid Neural Network (SLNN)** is not an architecture where biology was forcefully fitted after starting with a mathematical theorem. It is an architecture that proves how honestly and efficiently biological control motifs—intuitively understood by clinicians—can be implemented in an edge environment (Rust-based) without excessive offline learning.
Lee et al. (Sun,) studied this question.