Scalable Event-by-event Processing of Neuromorphic Sensory Signals Using Deep State-Space Models

This work presents a scalable method for modeling irregular event-stream data from neuromorphic sensors, addressing the key challenges of long-range dependencies, asynchronous processing, and parallelization.