A Mixed-Signal Spatio-Temporal Signal Classifier for On-Sensor Spike Sorting

Neuromorphic systems provide an alternative to conventional computing hardware, promising low-power operation suitable for sensory-processing and edge computing. In this paper, we present a mixed-signal processing system designed to provide on-sensor classification of signals obtained from multi-electrode array neural recordings. The designed circuits implement a real-time spike sorting algorithm, and operate on signals represented by asynchronous event streams. We combine analog circuits computation primitives (temporal surface generation, distance computation, winner-take-all) to implement a spatio-temporal clustering algorithm, classifying signals acquired by neighbouring electrodes. The prototype chip has been submitted for fabrication in a 180nm CMOS technology. The circuits are designed to fit, alongside signal conditioning and conversion circuits, in the area under the recording electrodes (below 80×80um per electrode). Circuit implementation details and simulation results are presented. The expected neural spike recognition rates of 75% in a single-layer network and 88% in a 2-layer network are comparable with a software implementation, while the system is designed to provide a low-power embedded real-time solution. This work provides a foundation towards the design of a large scale neuromorphic processing system, to be embedded in brain-machine interfaces.