An Adaptive Contrastive Learning Model for Spike Sorting

TL;DR

This paper introduces an adaptive contrastive learning framework for spike sorting in brain-computer interfaces, improving accuracy and efficiency by leveraging mutual information maximization and binary classification simplification.

Contribution

It proposes a novel adaptive contrastive learning model based on mutual information for spike sorting, with enhancements to handle overlapping spikes and improve performance.

Findings

Improved spike sorting accuracy

Reduced runtime compared to traditional methods

Effective handling of overlapping spikes

Abstract

Brain-computer interfaces (BCIs), is ways for electronic devices to communicate directly with the brain. For most medical-type brain-computer interface tasks, the activity of multiple units of neurons or local field potentials is sufficient for decoding. But for BCIs used in neuroscience research, it is important to separate out the activity of individual neurons. With the development of large-scale silicon technology and the increasing number of probe channels, artificially interpreting and labeling spikes is becoming increasingly impractical. In this paper, we propose a novel modeling framework: Adaptive Contrastive Learning Model that learns representations from spikes through contrastive learning based on the maximizing mutual information loss function as a theoretical basis. Based on the fact that data with similar features share the same labels whether they are multi-classified or binary-classified. With this theoretical support, we simplify the multi-classification problem into multiple binary-classification, improving both the accuracy and the runtime efficiency. Moreover, we also introduce a series of enhancements for the spikes, while solving the problem that the classification effect is affected because of the overlapping spikes.