Deep Spike Decoder (DSD)
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.
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