Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals

TL;DR

This paper introduces a deep metric learning approach with a novel loss function for improved self-correcting source separation of neural spiking signals, effectively handling noisy labels and enhancing decoding accuracy.

Contribution

It proposes a new loss function that maintains intra-class variance, enabling robust source separation and label cleaning in noisy neurophysiological data.

Findings

Successfully recovered original spike timestamps in noisy conditions

Enhanced robustness of source separation in high-noise scenarios

Improved neural decoding accuracy using imperfect labels

Abstract

Neurophysiological time series, such as electromyographic signal and intracortical recordings, are typically composed of many individual spiking sources, the recovery of which can give fundamental insights into the biological system of interest or provide neural information for man-machine interfaces. For this reason, source separation algorithms have become an increasingly important tool in neuroscience and neuroengineering. However, in noisy or highly multivariate recordings these decomposition techniques often make a large number of errors, which degrades human-machine interfacing applications and often requires costly post-hoc manual cleaning of the output label set of spike timestamps. To address both the need for automated post-hoc cleaning and robust separation filters we propose a methodology based on deep metric learning, using a novel loss function which maintains intra-class variance, creating a rich embedding space suitable for both label cleaning and the discovery of new activations. We then validate this method with an artificially corrupted label set based on source-separated high-density surface electromyography recordings, recovering the original timestamps even in extreme degrees of feature and class-dependent label noise. This approach enables a neural network to learn to accurately decode neurophysiological time series using any imperfect method of labelling the signal.