A Neuroprosthetic SoC Featuring Rapid Local Sensory Feedback with On-Chip Unsupervised Multi-Class Spike Sorting

Abstract

This letter presents a 65 nm neuroprosthetic system-on-chip (SoC) featuring a rapid feedback pathway from motor recording to sensory stimulation. To enable low-latency sensory mapping modulated by individual-neuron firing rates, the SoC integrates a mixed-signal unsupervised spike-sorting module with stimulation-artifact rejection, a programmable sliding-window firing-rate calculator, and a closed-loop controller for adapting stimulation parameters. The spike-sorting module combines event-driven analog feature extraction with Manhattan-distance-based digital clustering while consuming 1.6 $μ$W/channel. On the Quiroga Difficult-1 benchmark, it achieves 94.58% three-class accuracy, with only a 0.4% reduction when stimulation artifacts are introduced at 20% of the spike count. The closed-loop controller supports polynomial and logarithmic mapping functions and incorporates Mitchell's approximation for power saving. The SoC is fully characterized on the bench and in vivo.