MetaSort: An Accelerated Approach for Non-uniform Compression and Few-shot Classification of Neural Spike Waveforms

TL;DR

MetaSort is a novel algorithm that combines adaptive compression and few-shot classification of neural spike waveforms using meta-transfer learning, demonstrating promising results on in-vivo data and enabling potential ultra-low-power on-chip applications.

Contribution

It introduces a combined approach for spike sorting and compression using adaptive algorithms and meta-transfer learning, which is a novel integration in this domain.

Findings

MetaSort achieves high-fidelity spike shape approximation.

It demonstrates robust and discriminative classification performance.

The method shows promise for ultra-low-power, on-chip spike analysis.

Abstract

Many previous works in spike sorting study spike classification and compression independently. In this paper, a novel algorithm is proposed called MetaSort to address these two problems. To deal with compression, a novel adaptive level crossing algorithm is proposed to approximate spike shapes with high fidelity. Meanwhile, the latent feature representation is used to handle the classification problem. Besides, to guarantee MetaSort is robust and discriminative, the geometric information of data is exploited simultaneously in the proposed framework by meta-transfer learning. Empirical experiments with in-vivo spike data demonstrate that MetaSort delivers promising performance, highlighting its potential and motivating continued development toward an ultra-low-power, on-chip implementation.