- Original Pdf: pdf
- Keywords: self-supervised, deep learning, spike sorting, EMG, sEMG, autoencoder, inductive bias
- TL;DR: We built an unsupervised spike sorting algorithm using deep learning with biophysics baked in.
- Abstract: Spike-sorting is of central importance for neuroscience research. We introducea novel spike-sorting method comprising a deep autoencoder trained end-to-endwith a biophysical generative model, biophysically motivated priors, and a self-supervised loss function to training a deep autoencoder. The encoder infers the ac-tion potential event times for each source, while the decoder parameters representeach source’s spatiotemporal response waveform. We evaluate this approach inthe context of real and synthetic multi-channel surface electromyography (sEMG)data, a noisy superposition of motor unit action potentials (MUAPs). Relative toan established spike-sorting method, this autoencoder-based approach shows su-perior recovery of source waveforms and event times. Moreover, the biophysicalnature of the loss functions facilitates interpretability and hyperparameter tuning.Overall, these results demonstrate the efficacy and motivate further developmentof self-supervised spike sorting techniques.