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Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays

View ORCID ProfileGonzalo E. Mena, Lauren E. Grosberg, Sasidhar Madugula, Paweł Hottowy, Alan Litke, John Cunningham, E.J. Chichilnisky, Liam Paninski
doi: https://doi.org/10.1101/089912
Gonzalo E. Mena
1Statistics Department, Columbia University, New York, NY, 10027, USA
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  • For correspondence: gem2131@columbia.edu
Lauren E. Grosberg
3Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, USA
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Sasidhar Madugula
3Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, USA
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Paweł Hottowy
4Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Krakow, Poland
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Alan Litke
5Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
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John Cunningham
1Statistics Department, Columbia University, New York, NY, 10027, USA
2Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University.
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E.J. Chichilnisky
3Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, USA
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Liam Paninski
1Statistics Department, Columbia University, New York, NY, 10027, USA
2Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University.
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Abstract

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

Author Summary Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these recordings is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is largely stymied by electrical stimulation artifacts across the array, which are typically larger than the signals of interest. We develop a novel computational framework to estimate and subtract away this contaminating artifact, enabling the large-scale analysis of responses of possibly hundreds of cells to tailored stimulation. Importantly, we suggest that this technology may also be helpful for the development of future high-resolution neural prosthetic devices (e.g., retinal prostheses).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 15, 2017.
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Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays
Gonzalo E. Mena, Lauren E. Grosberg, Sasidhar Madugula, Paweł Hottowy, Alan Litke, John Cunningham, E.J. Chichilnisky, Liam Paninski
bioRxiv 089912; doi: https://doi.org/10.1101/089912
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Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays
Gonzalo E. Mena, Lauren E. Grosberg, Sasidhar Madugula, Paweł Hottowy, Alan Litke, John Cunningham, E.J. Chichilnisky, Liam Paninski
bioRxiv 089912; doi: https://doi.org/10.1101/089912

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