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

2026-03-08

AI Summary

This work proposes MetaSort, a novel algorithm that unifies neural spike compression and classification within a single framework—addressing a key limitation of existing approaches that treat these tasks separately and thus struggle to balance efficiency and performance. MetaSort achieves high-fidelity non-uniform compression through adaptive level-crossing sampling and constructs a geometry-aware latent representation by integrating meta-transfer learning with the intrinsic geometric structure of the data, enabling effective few-shot classification. Evaluated on in vivo neural spike recordings, the method significantly improves both compression ratio and classification accuracy, offering an efficient and practical solution for ultra-low-power on-chip neural signal processing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• spike sorting
• non-uniform compression
• few-shot classification
• neural spike waveforms

Innovation

• adaptive level crossing
• latent feature representation
• meta-transfer learning
• few-shot classification
• non-uniform compression