Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

@article{Hurwitz2019ScalableSS,
  title={Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference},
  author={C. Hurwitz and K. Xu and A. Srivastava and A. P. Buccino and M. Hennig},
  journal={bioRxiv},
  year={2019}
}
  • C. Hurwitz, K. Xu, +2 authors M. Hennig
  • Published 2019
  • Computer Science, Mathematics, Biology
  • bioRxiv
  • 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… CONTINUE READING

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