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Large-scale recording of neuronal ensembles

Nature Neuroscience volume 7, pages 446–451 (2004)Cite this article

Abstract

How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron–electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.

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Figure 1: Unit isolation quality varies as a function of distance from the electrode.

Debbie Maizels

Figure 2: High-density recording of unit activity in the somatosensory cortex of the rat.
Figure 3: Functional topography within the recorded population in the somatosensory cortex of the rat.
Figure 4: Behavior and network-dependent variability of spike amplitude and waveform is the most important source of unit classification errors.
Figure 5: Coordination of assembly patterns in the hippocampus.

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References

  1. Bialek, W., Rieke, F., de Ruyter van Steveninck, R.R. & Warland, D. Reading a neural code. Science 252, 1854–1857 (1991).

    Article  CAS  Google Scholar 

  2. Laurent, G. A systems perspective on early olfactory coding. Science 286, 723–728 (1999).

    Article  CAS  PubMed Central  Google Scholar 

  3. Engel, A.K., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704–716 (2001).

    Article  CAS  Google Scholar 

  4. Shadlen, M.N. & Newsome, W.T. The variable discharge of cortical neurons: implications for connectivity computation, and information coding. J. Neurosci. 18, 3870–3896 (1998).

    Article  CAS  PubMed Central  Google Scholar 

  5. Harris, K.D., Csicsvari, J., Hirase, H., Dragoi, G. & Buzsáki, G. Organization of cell assemblies in the hippocampus. Nature 424, 552–556 (2003).

    Article  CAS  PubMed Central  Google Scholar 

  6. Georgopoulos, A.P., Lurito, J.T., Petrides, M., Schwartz, A.B. & Massey, J.T. Mental rotation of the neuronal population vector. Science 243, 234–236 (1989).

    Article  CAS  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Douglas, R.J. & Martin, K.A. Opening the grey box. Trends Neurosci. 14, 286–293 (1991).

    Article  CAS  PubMed Central  Google Scholar 

  9. Hubel, D.H. Tungsten microelectrodes for recording single units. Science 125, 549–550 (1957).

    Article  CAS  PubMed Central  Google Scholar 

  10. Carmena, J.M. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, 193–208 (2004).

    Google Scholar 

  11. Donoghue, J.P. Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci. 5 (Suppl.), 1085–1088 (2002).

    Article  CAS  Google Scholar 

  12. Rousche, P.J. & Normann, R.A. Chronic recording capability of the Utah intracortical electrode array in cat sensory cortex. J. Neurosci. Methods 82, 1–15 (1998).

    Article  CAS  PubMed Central  Google Scholar 

  13. Hampson, R.E., Simeral, J.D. & Deadwyler, S.A. Distribution of spatial and nonspatial information in dorsal hippocampus. Nature 402, 610–614 (1999).

    Article  CAS  PubMed Central  Google Scholar 

  14. Hoffman, K.L. & McNaughton, B.L. Coordinated reactivation of distributed memory traces in primate neocortex. Science 297, 2070–2073 (2002).

    Article  CAS  PubMed Central  Google Scholar 

  15. Chapin, J.K. Using multi-neuron population recordings for neural prosthetics. Nat. Neurosci. 7, 452–455 (2004).

    Article  CAS  PubMed Central  Google Scholar 

  16. Churchland, P.S. & Sejnowski, T.J. The Computational Brain (MIT Press, Cambridge, 1992).

    Google Scholar 

  17. 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).

    Article  CAS  Google Scholar 

  18. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  20. Jog, M.S. et al. Tetrode technology: advances in implantable hardware, neuroimaging, and data analysis techniques. J. Neurosci. Methods 117, 141–152 (2002).

    Article  CAS  PubMed Central  Google Scholar 

  21. Holmgren, C., Harkany, T., Svennenfors, B. & Zilberter, Y. Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J. Physiol. 551, 139–153 (2003).

    Article  CAS  PubMed Central  Google Scholar 

  22. Csicsvari, J., Hirase, H., Czurko, A., Mamiya, A. & Buzsáki, G. Oscillatory coupling of hippocampal pyramidal cells and interneurons in the behaving rat. J. Neurosci. 19, 274–287 (1999).

    Article  CAS  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed Central  Google Scholar 

  24. Norlin, P., Kindlundh, M., Mouroux, A., Yoshida, K. & Hofmann, U.G. A 32-site neuronal probe fabricated by DRIE of SOI substrates. J. Micromech. Microeng. 12, 414–419 (2002).

    Article  CAS  Google Scholar 

  25. Wise, K.D. Micromachined Interfaces to the cellular world. Sensors Materials 10, 385–395 (1998).

    CAS  Google Scholar 

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

    Article  PubMed Central  Google Scholar 

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

    Article  PubMed Central  Google Scholar 

  28. Barthó, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. (in press).

  29. Llinás, R.R. The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242, 1654–1664 (1988).

    Article  PubMed Central  Google Scholar 

  30. Stuart, G., Spruston, N., Sakmann, B. & Hausser, M. Action potential initiation and backpropagation in neurons of the mammalian CNS. Trends Neurosci. 20, 125–131 (1997).

    Article  CAS  Google Scholar 

  31. Holt, G.R. & Koch, C. Electrical interactions via the extracellular potential near cell bodies. J. Comput. Neurosci. 6, 169–184 (1999).

    Article  CAS  PubMed Central  Google Scholar 

  32. 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).

    Article  CAS  Google Scholar 

  33. Buzsáki, G., Penttonen, M., Nadasdy, Z. & Bragin, A. Pattern and inhibition-dependent invasion of pyramidal cell dendrites by fast spikes in the hippocampus in vivo. Proc. Natl. Acad. Sci. USA 93, 9921–9925 (1996).

    Article  PubMed Central  Google Scholar 

  34. 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).

    Article  CAS  PubMed Central  Google Scholar 

  35. 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).

    Article  CAS  PubMed Central  Google Scholar 

  36. Takahashi, S., Anzai, Y. & Sakurai, Y. Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes. J. Neurophysiol. 89, 2245–2258 (2003).

    Article  PubMed Central  Google Scholar 

  37. Fee, M.S., Mitra, P.P. & Kleinfeld, D. Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability. J. Neurosci. Methods 69, 175–188 (1996).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed Central  Google Scholar 

  39. Klausberger, T. et al. Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo. Nature 421, 844–848 (2003).

    Article  CAS  PubMed Central  Google Scholar 

  40. Swadlow, H.A. Fast-spike interneurons and feedforward inhibition in awake sensory neocortex. Cereb. Cortex 13, 25–32 (2003).

    Article  PubMed Central  Google Scholar 

  41. Csicsvari, J., Hirase, H., Czurko, A. & Buzsáki, G. Reliability and state dependence of pyramidal cell-interneuron synapses in the hippocampus: an ensemble approach in the behaving rat. Neuron 21, 179–189 (1998).

    Article  CAS  PubMed Central  Google Scholar 

  42. Somogyi, P., Tamas, G., Lujan, R. & Buhl, E.H. Salient features of synaptic organisation in the cerebral cortex. Brain Res. Brain Res. Rev. 26, 113–135 (1998).

    Article  CAS  PubMed Central  Google Scholar 

  43. Monyer, H. & Markram, H. Interneuron diversity series: molecular and genetic tools to study GABAergic interneuron diversity and function. Trends Neurosci. 27, 90–97 (2004).

    Article  CAS  PubMed Central  Google Scholar 

  44. Kawaguchi, Y. & Kubota, Y. Correlation of physiological subgroupings of nonpyramidal cells with parvalbumin- and calbindinD28k-immunoreactive neurons in layer V of rat frontal cortex. J. Neurophysiol. 70, 387–396 (1993).

    Article  CAS  PubMed Central  Google Scholar 

  45. Buzsáki, G., Traub, R.D. & Pedley, T. The cellular synaptic generation of EEG. in Current Practice of Clinical Encephalography Edn. 3 (eds. Ebersole, J.S. & Pedley, T.A.) 1–11 (Lippincott Williams & Wilkins, Philadelphia, 2003).

    Google Scholar 

  46. Deadwyler, S.A. & Hampson, R.E. Ensemble activity and behavior: what's the code? Science 270, 1316–1318, 1995.

    Article  CAS  Google Scholar 

  47. Eichenbaum, H. & Davis, J.L. Neuronal Ensembles: Strategies for Recording and Coding (Wiley-Liss, New York, 1988).

    Google Scholar 

  48. Buzsáki, G., Horváth, Z., Urioste, R., Hetke, J. & Wise, K. High-frequency network oscillation in the hippocampus. Science 256, 1025–1027 (1992).

    Article  PubMed Central  Google Scholar 

  49. Nádasdy, Z., Hirase, H., Czurkó, A., Csicsvari, J. & Buzsáki, G. Replay and time compression of recurring spike sequences in the hippocampus. J. Neurosci. 19, 9497–9507 (1999).

    Article  PubMed Central  Google Scholar 

  50. Louie, K. & Wilson, M.A. Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep. Neuron 29, 145–156 (2001).

    Article  CAS  PubMed Central  Google Scholar 

  51. Lee, A.K. & Wilson, M.A. Memory of sequential experience in the hippocampus during slow wave sleep. Neuron 36, 1183–1194 (2002).

    Article  CAS  PubMed Central  Google Scholar 

  52. Hebb, D.O. The Organization of Behavior: A Neuropsychological Theory (John Wiley & Sons, New York, 1949).

    Google Scholar 

  53. Gray, C.M., Konig, P., Engel, A.K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989).

    Article  CAS  PubMed Central  Google Scholar 

  54. deCharms, R.C. & Zador, A. Neural representation and the cortical code. Annu. Rev. Neurosci. 23, 613–647 (2000).

    Article  CAS  PubMed Central  Google Scholar 

  55. Hampson, R.E., Simeral, J.D. & Deadwyler, S.A. What ensemble recordings reveal about functional hippocampal cell encoding. Prog. Brain Res. 130, 345–357 (2001).

    Article  CAS  PubMed Central  Google Scholar 

  56. Nicolelis, M.A.L., Lin, C.-S., Woodward, D.J. & Chapin, J.K. Peripheral block of ascending cutaneous information induces immediate spatio-temporal changes in thalamic networks. Nature 361, 533–536 (1993).

    Article  CAS  Google Scholar 

  57. Olsson III, R.H., Buhl, D.L., Gulari, M.N., Buzsaki, G. & Wise, K.D. A silicon microelectrode array for simultaneous recording and stimulation in the hippocampus of free moving rats and mice. IEEE Eng. Med. Biol. Mag. 22, 1968–1671, 2003.

    Google Scholar 

  58. Taylor, D.M., Tillery, S.I. & Schwartz, A.B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).

    Article  CAS  Google Scholar 

  59. Shoham, S., Fellows, M.R. & Normann, R.A. Robust, automatic spike sorting using mixtures of multivariate t-distributions. J. Neurosci. Methods 127, 111–122 (2003).

    Article  PubMed Central  Google Scholar 

  60. Musial, P.G., Baker, S.N., Gerstein, G.L., King, E.A. & Keating, J.G. Signal-to-noise ratio improvement in multiple electrode recording. J. Neurosci. Methods 115, 29–43 (2002).

    Article  CAS  PubMed Central  Google Scholar 

  61. Pouzat, C., Mazor, O. & Laurent, G. Using noise signature to optimize spike-sorting and to assess neuronal classification quality. J. Neurosci. Methods 122, 43–57 (2002).

    Article  PubMed Central  Google Scholar 

  62. Quian Quiroga, R., Nádasdy, Z. & Ben-Shaul, Y. Unsupervised spike detection and sorting with wavelets and super-paramagnetic clustering. Neural Comput. (in press).

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

    Article  CAS  Google Scholar 

  64. Letelier, J.C. & Weber, P.P. Spike sorting based on discrete wavelet transform coefficients. J. Neurosci. Methods 101, 93–106 (2000).

    Article  CAS  PubMed Central  Google Scholar 

  65. Brown, E.N., Kass, R.E. & Mitra, P. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat. Neurosci. 7, 456–461 (2004).

    Article  CAS  PubMed Central  Google Scholar 

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Acknowledgements

I thank D.L. Buhl, J. Csicsvari, K.D., Harris, D.A. Henze, H. Hirase, J. Hetke, B. Jamieson, S. Montgomery, R. Olsson, A. Sirota and K.D. Wise for support and collaboration. Supported by National Institutes of Health (NS34994, NS43157; MH54671 and 1P41RR09754).

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  1. Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, 197 University Avenue, Newark, 07102, New Jersey, USA

    György Buzsáki

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Buzsáki, G. Large-scale recording of neuronal ensembles. Nat Neurosci 7, 446–451 (2004). https://doi.org/10.1038/nn1233

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