A Fully Automated Approach to Spike Sorting.

Autor:	Chung JE; Neuroscience Graduate Program, Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California San Francisco, CA 94158, USA., Magland JF; Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA., Barnett AH; Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA; Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA., Tolosa VM; Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA., Tooker AC; Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA., Lee KY; Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA., Shah KG; Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA., Felix SH; Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA., Frank LM; Neuroscience Graduate Program, Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California San Francisco, CA 94158, USA; Howard Hughes Medical Institute. Electronic address: loren@phy.ucsf.edu., Greengard LF; Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA; Courant Institute, NYU, New York, NY 10012, USA.
Jazyk:	angličtina
Zdroj:	Neuron [Neuron] 2017 Sep 13; Vol. 95 (6), pp. 1381-1394.e6.
DOI:	10.1016/j.neuron.2017.08.030
Abstrakt:	Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. (Copyright © 2017 Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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