Architectural Exploration of Neuromorphic Compression based Neural Sensing for Next-Gen Wireless implantable-BMI

Abstract

This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, nextgen wireless iBMI. The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio and spike information preservation. For the latter, we use metrics such as root-mean-square error and correlation coefficient between the original and recovered signal to assess the effect of neuromorphic compression on spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data compression ratio of 50 − 100 can be achieved, 5 − 18× more than prior work, by selective transmission of event pulses corresponding to neural spikes. A correlation coefficient of ≈ 0.9 and spike detection accuracy of over 90% for the worst-case analysis involving 10K-channel simulated recording and typical analysis using 100 or 384-channel real neural recordings. We also analyze the collision handling capability and scalability of the proposed pipeline.