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arXiv · 2611.03722

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

Bowen Liu·Yuhan Hou·Yu Huang·Jianxiong Xu·Hao You·Ashley Hung·Naize Yang·Junyu Ma·Laura Kondrataviciute·Suneil K. Kalia·Andrew G. Richardson·Roman Genov·Xilin Liu
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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.

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Bowen Liu, Yuhan Hou, Yu Huang, Jianxiong Xu, Hao You, Ashley Hung, Naize Yang, Junyu Ma, Laura Kondrataviciute, Suneil K. Kalia, Andrew G. Richardson, Roman Genov, Xilin Liu. 2026-11-01. A Neuroprosthetic SoC Featuring Rapid Local Sensory Feedback with On-Chip Unsupervised Multi-Class Spike Sorting. https://doi.org/10.1109/lssc.2026.3746977

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