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