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Spike sorting for large, dense electrode arrays

Nature Neuroscience volume 19, pages 634–641 (2016)Cite this article

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Abstract

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%.

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Figure 1: High-count silicon probe recording.
Figure 2: Local spike-detection algorithm.
Figure 3: Evaluation of spike detection performance.
Figure 4: Evaluation of automatic clustering performance.
Figure 5: The wizard for computer-guided manual correction.
Figure 6: Screenshot of the KlustaViewa graphical user interface.
Figure 7: Consistency of manual curation across operators.

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References

  1. Buzsáki, G. Large-scale recording of neuronal ensembles. Nat. Neurosci. 7, 446–451 (2004).

    PubMed  Google Scholar 

  2. Wise, K.D. & Najafi, K. Microfabrication techniques for integrated sensors and microsystems. Science 254, 1335–1342 (1991).

    CAS  PubMed  Google Scholar 

  3. Csicsvari, J. et al. Massively parallel recording of unit and local field potentials with silicon-based electrodes. J. Neurophysiol. 90, 1314–1323 (2003).

    PubMed  Google Scholar 

  4. McNaughton, B.L., O'Keefe, J. & Barnes, C.A. The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J. Neurosci. Methods 8, 391–397 (1983).

    CAS  PubMed  Google Scholar 

  5. Gray, C.M., Maldonado, P.E., Wilson, M. & McNaughton, B. Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex. J. Neurosci. Methods 63, 43–54 (1995).

    CAS  PubMed  Google Scholar 

  6. Wilson, M.A. & McNaughton, B.L. Dynamics of the hippocampal ensemble code for space. Science 261, 1055–1058 (1993).

    CAS  PubMed  Google Scholar 

  7. Recce, M. & O'Keefe, J. The tetrode: a new technique for multi-unit extracellular recording. Soc. Neurosci. Abstr. 15, 1250 (1989).

    Google Scholar 

  8. Harris, K.D., Henze, D.A., Csicsvari, J., Hirase, H. & Buzsáki, G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84, 401–414 (2000).

    CAS  PubMed  Google Scholar 

  9. Henze, D.A. et al. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84, 390–400 (2000).

    CAS  PubMed  Google Scholar 

  10. Gold, C., Henze, D.A., Koch, C. & Buzsáki, G. On the origin of the extracellular action potential waveform: A modeling study. J. Neurophysiol. 95, 3113–3128 (2006).

    CAS  PubMed  Google Scholar 

  11. Einevoll, G.T., Franke, F., Hagen, E., Pouzat, C. & Harris, K.D. Towards reliable spike-train recordings from thousands of neurons with multielectrodes. Curr. Opin. Neurobiol. 22, 11–17 (2012).

    CAS  PubMed  Google Scholar 

  12. Lewicki, M.S. A review of methods for spike sorting: the detection and classification of neural action potentials. Network 9, R53–R78 (1998).

    CAS  PubMed  Google Scholar 

  13. Hazan, L., Zugaro, M. & Buzsáki, G. Klusters, NeuroScope, NDManager: a free software suite for neurophysiological data processing and visualization. J. Neurosci. Methods 155, 207–216 (2006).

    PubMed  Google Scholar 

  14. Briggman, K.L., Helmstaedter, M. & Denk, W. Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011).

    CAS  PubMed  Google Scholar 

  15. Berényi, A. et al. Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals. J. Neurophysiol. 111, 1132–1149 (2014).

    PubMed  Google Scholar 

  16. Du, J., Blanche, T.J., Harrison, R.R., Lester, H.A. & Masmanidis, S.C. Multiplexed, high density electrophysiology with nanofabricated neural probes. PLoS One 6, e26204 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Bouveyron, C. & Brunet-Saumard, C. Model-based clustering of high-dimensional data: a review. Comput. Stat. Data Anal. 71, 52–78 (2014).

    Google Scholar 

  18. Ekanadham, C., Tranchina, D. & Simoncelli, E.P. A unified framework and method for automatic neural spike identification. J. Neurosci. Methods 222, 47–55 (2014).

    PubMed  Google Scholar 

  19. Carlson, D.E. et al. Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling. IEEE Trans. Biomed. Eng. 61, 41–54 (2014).

    PubMed  Google Scholar 

  20. Calabrese, A. & Paninski, L. Kalman filter mixture model for spike sorting of non-stationary data. J. Neurosci. Methods 196, 159–169 (2011).

    PubMed  Google Scholar 

  21. Franke, F., Natora, M., Boucsein, C., Munk, M.H. & Obermayer, K. An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes. J. Comput. Neurosci. 29, 127–148 (2010).

    PubMed  Google Scholar 

  22. Quiroga, R.Q., Nadasdy, Z. & Ben-Shaul, Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16, 1661–1687 (2004).

    Article  PubMed  Google Scholar 

  23. Swindale, N.V. & Spacek, M.A. Spike sorting for polytrodes: a divide and conquer approach. Front. Syst. Neurosci. 8, 6 (2014).

    PubMed  PubMed Central  Google Scholar 

  24. Swindale, N.V. & Spacek, M.A. Spike detection methods for polytrodes and high density microelectrode arrays. J. Comput. Neurosci. 38, 249–261 (2015).

    PubMed  Google Scholar 

  25. Buzsáki, G. & Kandel, A. Somadendritic backpropagation of action potentials in cortical pyramidal cells of the awake rat. J. Neurophysiol. 79, 1587–1591 (1998).

    PubMed  Google Scholar 

  26. Logothetis, N.K., Kayser, C. & Oeltermann, A. In vivo measurement of cortical impedance spectrum in monkeys: implications for signal propagation. Neuron 55, 809–823 (2007).

    CAS  PubMed  Google Scholar 

  27. Harris, K.D., Hirase, H., Leinekugel, X., Henze, D.A. & Buzsáki, G. Temporal interaction between single spikes and complex spike bursts in hippocampal pyramidal cells. Neuron 32, 141–149 (2001).

    CAS  PubMed  Google Scholar 

  28. Quirk, M.C., Blum, K.I. & Wilson, M.A. Experience-dependent changes in extracellular spike amplitude may reflect regulation of dendritic action potential back-propagation in rat hippocampal pyramidal cells. J. Neurosci. 21, 240–248 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Quirk, M.C. & Wilson, M.A. Interaction between spike waveform classification and temporal sequence detection. J. Neurosci. Methods 94, 41–52 (1999).

    CAS  PubMed  Google Scholar 

  30. Kadir, S.N., Goodman, D.F. & Harris, K.D. High-dimensional cluster analysis with the masked EM algorithm. Neural Comput. 26, 2379–2394 (2014).

    PubMed  PubMed Central  Google Scholar 

  31. Fowlkes, E.B. & Mallows, C.L. A method for comparing 2 hierarchical clusterings. J. Am. Stat. Assoc. 78, 553–569 (1983).

    Google Scholar 

  32. Schmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K. & Redish, A.D. Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience 131, 1–11 (2005).

    CAS  PubMed  Google Scholar 

  33. Hill, D.N., Mehta, S.B. & Kleinfeld, D. Quality metrics to accompany spike sorting of extracellular signals. J. Neurosci. 31, 8699–8705 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Owens, J.D. et al. GPU computing. Proc. IEEE 96, 879–899 (2008).

    Google Scholar 

  35. Freeman, J. et al. Mapping brain activity at scale with cluster computing. Nat. Methods 11, 941–950 (2014).

    CAS  PubMed  Google Scholar 

  36. Comaniciu, D. & Meer, P. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002).

    Google Scholar 

  37. Rodriguez, A. & Laio, A. Machine learning. Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014).

    CAS  PubMed  Google Scholar 

  38. Marre, O. et al. Mapping a complete neural population in the retina. J. Neurosci. 32, 14859–14873 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Pillow, J.W., Shlens, J., Chichilnisky, E.J. & Simoncelli, E.P. A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings. PLoS One 8, e62123 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Saleem, A.B., Ayaz, A., Jeffery, K.J., Harris, K.D. & Carandini, M. Integration of visual motion and locomotion in mouse visual cortex. Nat. Neurosci. 16, 1864–1869 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Ayaz, A., Saleem, A.B., Schölvinck, M.L. & Carandini, M. Locomotion controls spatial integration in mouse visual cortex. Curr. Biol. 23, 890–894 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Ecker, A.S. et al. State dependence of noise correlations in macaque primary visual cortex. Neuron 82, 235–248 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Ecker, A.S. et al. Decorrelated neuronal firing in cortical microcircuits. Science 327, 584–587 (2010).

    CAS  PubMed  Google Scholar 

  44. Zeater, N., Cheong, S.K., Solomon, S.G., Dreher, B. & Martin, P.R. Binocular visual responses in the primate lateral geniculate nucleus. Curr. Biol. 25, 3190–3195 (2015).

    CAS  PubMed  Google Scholar 

  45. The HDF Group. Hierarchical Data Format, version 5. http://www.hdfgroup.org/HDF5/ (2014).

  46. Rossant, C. & Harris, K.D. Hardware-accelerated interactive data visualization for neuroscience in Python. Front. Neuroinform. 7, 36 (2013).

    PubMed  PubMed Central  Google Scholar 

  47. Shreiner, D., Sellers, G., Kessenich, J.M., Licea-Kane, B. & Khronos OpenGL ARB Working Group. OpenGL Programming Guide: The Official Guide to Learning OpenGL, version 4.3. 8th edn. (Addison Wesley, 2013).

  48. Swayne, D.F., Cook, D. & Buja, A. XGobi: interactive dynamic data visualization in the X Window System. J. Comput. Graph. Stat. 7, 113–130 (1998).

    Google Scholar 

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Acknowledgements

We thank the 200+ members of the klustaviewas@groups.google.com mailing list for their feedback, bug reports and suggestions. This work was supported by EPSRC (K015141, I005102, K.D.H.) and the Wellcome Trust (95668, 95669, 100154, K.D.H., M.C.). M.C. is supported by the GlaxoSmithKline/Fight for Sight chair in Visual Neuroscience.

Author information

Author notes

  1. Cyrille Rossant and Shabnam N Kadir: These authors contributed equally to this work.

Authors and Affiliations

  1. UCL Institute of Neurology, London, UK

    Cyrille Rossant, Shabnam N Kadir, Maximilian L D Hunter & Kenneth D Harris

  2. Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK

    Cyrille Rossant, Shabnam N Kadir, Maximilian L D Hunter & Kenneth D Harris

  3. Department of Electrical and Electronic Engineering, Imperial College, London, UK

    Dan F M Goodman

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California, USA

    John Schulman

  5. UCL Institute of Ophthalmology, London, UK

    Aman B Saleem & Matteo Carandini

  6. NYU Neuroscience Institute, Langone Medical Center, New York University, New York, New York, USA

    Andres Grosmark, Mariano Belluscio & György Buzsáki

  7. Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA

    George H Denfield, Alexander S Ecker & Andreas S Tolias

  8. Department of Experimental Psychology, UCL Institute of Behavioural Neuroscience, London, UK

    Samuel Solomon

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Contributions

C.R., D.F.M.G., S.N.K. and J.S. wrote SpikeDetekt. K.D.H., S.N.K. and D.F.M.G. designed the masked EM algorithm and wrote KlustaKwik. C.R. and M.L.D.H. wrote KlustaViewa. C.R. wrote Galry. S.N.K. analyzed a lgorithm performance. Rat data were recorded by A.G., M.B. and G.B. Mouse data were recorded by A.B.S. and M.C. Marmoset data were recorded by S.S. The procedure for non-chronic laminar recordings with NeuroNexus Vector probes in awake, behaving macaques was developed by G.H.D., A.S.E. and A.S.T., who also collected the data. K.D.H., S.N.K. and C.R. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Kenneth D Harris.

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The authors declare no competing financial interests.

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Supplementary Figures 1–17 and Supplementary Table 1 (PDF 4198 kb)

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Rossant, C., Kadir, S., Goodman, D. et al. Spike sorting for large, dense electrode arrays. Nat Neurosci 19, 634–641 (2016). https://doi.org/10.1038/nn.4268

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