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Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Cole Hurwitz · Kai Xu · Akash Srivastava · Alessio Buccino · Matthias Hennig

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #148

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.

Author Information

Cole Hurwitz (University of Edinburgh)
Kai Xu (University of Edinburgh)
Akash Srivastava (MIT–IBM Watson AI Lab)
Alessio Buccino (CINPLA, University of Oslo)
Matthias Hennig (University of Edinburgh)

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