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bioRxiv · 10.1101/101030

Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction

Jun, J. J.·Mitelut, C.·Lai, C.·Gratiy, S.·Anastassiou, C.·Harris, T. D.
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Abstract

Electrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating covariance-based features and unsupervised clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes up to 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.

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Jun, J. J., Mitelut, C., Lai, C., Gratiy, S., Anastassiou, C., Harris, T. D.. 2017-01-19. Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction. https://doi.org/10.1101/101030

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