Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting

TL;DR

This paper introduces a Taylor polynomial-based approximation unit for spike waveform processing, significantly reducing hardware complexity while maintaining high shape fidelity for unsupervised spike sorting.

Contribution

A novel approximation unit using Taylor polynomial derivatives is proposed, enabling 3X waveform compression and up to 8.7X reduction in hardware cost without compromising sorting accuracy.

Findings

3X waveform compression preserves spike shape.

Performance remains consistent across noise levels.

Hardware cost is significantly reduced by up to 8.7X.

Abstract

This paper presents a novel approximation unit added to the conventional spike processing chain which provides an appreciable reduction of complexity of the high-hardware cost feature extractors. The use of the Taylor polynomial is proposed and modelled employing its cascaded derivatives to non-uniformly capture the essential samples in each spike for reliable feature extraction and sorting. Inclusion of the approximation unit can provide 3X compression (i.e. from 66 to 22 samples) to the spike waveforms while preserving their shapes. Detailed spike waveform sequences based on in-vivo measurements have been generated using a customized neural simulator for performance assessment of the approximation unit tested on six published feature extractors. For noise levels {\sigma}_N between 0.05 and 0.3 and groups of 3 spikes in each channel, all the feature extractors provide almost same sorting performance before and after approximation. The overall implementation cost when including the approximation unit and feature extraction shows a large reduction (i.e. up to 8.7X) in the hardware costly and more accurate feature extractors, offering a substantial improvement in feature extraction design.