A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head

Robotic vision introduces requirements for real-time processing of fast-varying, noisy information in a continuously changing environment. In a real-world environment, convenient assumptions, such as static camera systems and deep learning algorithms devouring high volumes of ideally slightly-varying data are hard to survive. Leveraging on recent studies on the neural connectome associated with eye movements, we designed a neuromorphic oculomotor controller and placed it at the heart of our in-house biomimetic robotic head prototype. The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas' connectivity, the SNN required no training to operate. A biologically-constrained Hebbian learning further improved the SNN performance in tracking a moving target. Here, we report the tracking performance of the robotic head and show that the robotic eye kinematics are similar to those reported in human eye studies. This work contributes to our ongoing effort to develop energy-efficient neuromorphic SNN and harness their emerging intelligence to control biomimetic robots with versatility and robustness.