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Determining the positions of neurons in an extracellular recording is usefulfor investigating functional properties of the underlying neural circuitry. Inthis work, we present a Bayesian modelling approach for localizing the sourceof individual spikes on high-density, microelectrode arrays. To allow forscalable inference, we implement our model as a variational autoencoder andperform amortized variational inference. We evaluate our method on bothbiophysically realistic simulated and real extracellular datasets,demonstrating that it is more accurate than and can improve spike sortingperformance over heuristic localization methods such as center of mass.

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference