Extracellular recordings using modern, dense probes provide detailed footprints of action potentials (spikes) from thousands of neurons simultaneously. Inferring the activity of single neurons from these recordings, however, is a complex blind source separation problem, complicated both by the high intrinsic data dimensionality and large data volume. Despite these complications, dense probes can allow for the estimation of a spike's source location, a powerful feature for determining the firing neuron's position and identity in the recording. Here we present a novel, generative model for inferring the source of individual spikes given observed electrical traces . To allow for scalable , efficient inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on  biophysically realistic simulated datasets, showing that our method outperforms heuristic localization methods such as center of mass and can improve spike sorting performance significantly. We further apply our model to real data to show that it is an effective, interpretable tool for analyzing large-scale extracellular recordings .
Determining the positions of neurons in an extracellular recording is useful for investigating the 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 .
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Extracellular recordings using modern, dense probes provide detailed footprints of action potentials (spikes) from thousands of neurons simultaneously. Inferring the activity of single neurons from these recordings, however, is a complex blind source separation problem, complicated both by the high intrinsic data dimensionality and large data volume. Despite these complications, dense probes can allow for
Determining the positions of neurons in an extracellular recording is useful for investigating the functional properties of
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estimation of a spike's source location, a powerful feature for determining the firing neuron's position and identity in the recording. Here
underlying neural circuitry. In this work,
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novel, generative model for inferring
Bayesian modelling approach for localizing
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given observed electrical traces
on high-density, microelectrode arrays
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, efficient
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both
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datasets, showing that our method outperforms
and real extracellular datasets, demonstrating that it is more accurate than and can improve spike sorting performance over
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and can improve spike sorting performance significantly. We further apply our model to real data to show that it is an effective, interpretable tool for analyzing large-scale extracellular recordings