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Robotic vision introduces requirements for real-time processing offast-varying, noisy information in a continuously changing environment. In areal-world environment, convenient assumptions, such as static camera systemsand deep learning algorithms devouring high volumes of ideally slightly-varyingdata are hard to survive. Leveraging on recent studies on the neural connectomeassociated with eye movements, we designed a neuromorphic oculomotor controllerand 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 andprocessed by a spiking neural network (SNN), and (2) by mimicking theassociated brain areas' topology, the SNN is biologically interpretable andrequires no training to operate. Here, we report the robot's target trackingability, demonstrate that its eye kinematics are similar to those reported inhuman eye studies and show that a biologically-constrained learning, althoughnot required for the SNN's function, can be used to further refine itsperformance. This work aligns with our ongoing effort to developenergy-efficient neuromorphic SNNs and harness their emerging intelligence tocontrol biomimetic robots with versatility and robustness.

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