Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

Journal: Journal of neuroscience methods
PMID: 27317497

Abstract

BACKGROUND: Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons.

Authors

  • Christian Leibig
    Neurochip Research Group, Natural and Medical Sciences Institute, Reutlingen, Germany; International Max Planck Research School of Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany; Bernstein Center for Computational Neuroscience Munich and Department of Biology II, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany; ZEISS Vision Science Lab, Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany. Electronic address: christian.leibig@uni-tuebingen.de.
  • Thomas Wachtler
    Bernstein Center for Computational Neuroscience Munich and Department of Biology II, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
  • Günther Zeck
    Neurochip Research Group, Natural and Medical Sciences Institute, Reutlingen, Germany.

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