Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Multiple neural spike train data analysis: state-of-the-art and future challenges

Nature Neuroscience volume 7, pages 456–461 (2004)Cite this article

Abstract

Multiple electrodes are now a standard tool in neuroscience research that make it possible to study the simultaneous activity of several neurons in a given brain region or across different regions. The data from multi-electrode studies present important analysis challenges that must be resolved for optimal use of these neurophysiological measurements to answer questions about how the brain works. Here we review statistical methods for the analysis of multiple neural spike-train data and discuss future challenges for methodology research.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Transition from voltage signal recordings to measures of association for three neural spike trains.
Figure 2: Decoding of position from ensemble rat neural spiking activity40,50.

Similar content being viewed by others

References

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

    Article  CAS  Google Scholar 

  2. Daley, D. & Vere-Jones, D. An Introduction to the Theory of Point Process, 2nd ed. (Springer-Verlag, New York, 2003).

    Google Scholar 

  3. Lewicki, M.S. 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 

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

    Article  CAS  Google Scholar 

  5. 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 

  6. Nguyen, D., Frank, L.M. & Brown, E.N. An application of reversible-jump MCMC to spike classification of multiunit extracellular recordings. Network Comput. Neural Syst. 14, 61–82 (2003).

    Article  Google Scholar 

  7. Brody, C.D. Correlations without synchrony. Neural Comput. 11, 1537–1551 (1999).

    Article  CAS  Google Scholar 

  8. Brillinger, D.R. Nerve cell spike train data analysis: a progression of techniques. J. Amer. Stat. Assoc. 87, 260–271 (1992).

    Article  Google Scholar 

  9. Gerstein, G.L. & Perkel, D.H. Simultaneously recorded trains of action potentials: analysis and functional interpretation. Science 164, 828–830 (1969).

    Article  CAS  Google Scholar 

  10. Abeles, M. Quantification, smoothing, and confidence limits for single-unit histograms J. Neurosci. Methods 5, 317–325 (1982).

    Article  CAS  Google Scholar 

  11. Kass, R.E., Ventura, V. & Cai, C. Statistical smoothing of neuronal data. Network Comput. Neural Syst. 14, 5–15 (2003).

    Article  Google Scholar 

  12. Aertsen, A., Gerstein, G.L., Habib, M.K. & Palm, G. Dynamics of neural firing correlation: modulation of “effective connectivity”. J. Neurophysiol. 61, 900–917 (1989).

    Article  CAS  Google Scholar 

  13. Ito, H. & Tsuji, S. Model dependence in quantification of spike interdependency by joint peri-stimulus time histogram. Neural Comput. 12, 195–217 (2000).

    Article  CAS  Google Scholar 

  14. Baker, S.N. & Gerstein, G.L. Determination of response latency and its application to normalization of cross-correlation measures. Neural Comput. 13, 1351–1377 (2001).

    Article  CAS  Google Scholar 

  15. Bar-Gad, I., Ritov, Y., Vaadia, E. & Bergmann, H. Failure in identification of overlapping spikes from multiple neuron activity causes artificial correlations. J. Neurosci. Methods 107, 1–13 (2001).

    Article  CAS  Google Scholar 

  16. Softky, W.R. & Koch, C. The highly irregular firing of cortical cells is consistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Martignon, L. et al. Neural coding: higher-order temporal patterns in the neurostatistics of cell assemblies. Neural Comput. 12, 2621–2653 (2000).

    Article  CAS  Google Scholar 

  19. Abeles, M. & Gerstein, G.L. Detecting spatiotemporal firing patterns among simultaneously recorded single neurons. J. Neurophysiol. 60, 909–924 (1988).

    Article  CAS  Google Scholar 

  20. Grün, S., Diesmann, M. & Aertsen, A. Unitary events in multiple single-neuron spiking activity: II. Nonstationary data. Neural Comput. 14, 81–119 (2002).

    Article  Google Scholar 

  21. Gütig, R., Aertsen, A. & Rotter, S. Statistical significance of coincident spikes: count-based versus rate-based statistics. Neural Comput. 14, 121–153 (2002).

    Article  Google Scholar 

  22. Pipa, G. & Grün, S. Non-parametric significance estimation of joint-spike events by shuffling and resampling. Neurocomputing 52–54, 31–37 (2003).

    Article  Google Scholar 

  23. Oram, M.W., Wiener, M.C., Lestienne, R. & Richmond, B.J. Stochastic nature of precisely timed spike patterns in visual system neuronal responses. J. Neurophysiol. 81, 3021–3033 (1999).

    Article  CAS  Google Scholar 

  24. Amarasingham, A. Temporal Structure in Nervous System Activity dissertation, Brown Univ. (2002).

    Google Scholar 

  25. Pawitan, Y. In All Likelihood: Statistical Modelling and Inference Using Likelihood (Oxford Univ. Press, New York, 2001).

    Google Scholar 

  26. Brillinger, D.R. Maximum likelihood analysis of spike trains of interacting nerve cells. Biol. Cybern. 59, 189–200 (1988).

    Article  CAS  Google Scholar 

  27. Barbieri, R., Quirk, M.C., Frank, L.M., Wilson, M.A. & Brown, E.N. Construction and analysis of non-Poisson stimulus response models of neural spike train activity. J. Neurosci. Methods 105, 25–37 (2001).

    Article  CAS  Google Scholar 

  28. Kass, R.E. & Ventura, V. A spike train probability model. Neural Comput. 13, 1713–1720 (2001).

    Article  CAS  Google Scholar 

  29. Brown, E.N., Barbieri, R., Eden, U.T. & Frank, L.M. Likelihood methods for neural data analysis. in Computational Neuroscience: A Comprehensive Approach (ed. Feng, J.) 253–286 (CRC Press, Boca Raton, 2003).

    Google Scholar 

  30. Chornoboy, E.S., Schramm, L.P. & Karr, A.F. Maximum likelihood identification of neural point process systems. Biol. Cybern. 59, 265–275 (1988).

    Article  CAS  Google Scholar 

  31. Brillinger, D.R. Comparative aspects of the study of ordinary time series and of point processes. in Developments in Statistics Vol. 1, 33–129 (Academic Press, Orlando, 1978).

    Google Scholar 

  32. Jarvis, M.R. & Mitra, P.P. Sampling properties of the spectrum and coherency in sequences of action potentials. Neural Comput. 13, 717–749 (2001).

    Article  CAS  Google Scholar 

  33. Thomson, D.J. & Chave, A.D. Jackknifed error estimates for spectra, coherences, and transfer functions. in Advances in Spectrum Analysis and Array Processing (ed., Haykin, S.) 58–113 (Prentice Hall, Englewood Cliffs, NJ, 1991).

    Google Scholar 

  34. Brillinger, D.R. Time Series (Holt, Rinehart and Winston, New York, 1981).

    Google Scholar 

  35. Percival, D.B. & Walden, A.T. Wavelet Methods for Time Series Analysis (Cambridge Univ. Press, Cambridge, UK, 2000).

    Book  Google Scholar 

  36. Percival, D.B. & Walden, A.T. Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques (Cambridge Univ. Press, Cambridge, UK, 2002).

    Google Scholar 

  37. Pesaran, B., Pezaris, J.S., Sahani, M., Mitra, P.P. & Andersen, R.A. Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat. Neurosci. 5, 805–811 (2002).

    Article  CAS  Google Scholar 

  38. 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 

  39. Rieke, F., Warland, D., de Ruyter van Steveninck, R. & Bialek, W. Spikes: Exploring the Neural Code (MIT Press, Cambridge, 1997).

    Google Scholar 

  40. Brown, E.N., Frank, L.M., Tang, D., Quirk, M.C. & Wilson, M.A. A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. J. Neurosci. 18, 7411–7425 (1998).

    Article  CAS  Google Scholar 

  41. Georgopoulos, A.P., Kettner, R.E. & Schwartz, A.B. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

    Article  CAS  Google Scholar 

  42. 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 

  43. Warland, D.K., Reinagel, P. & Meister, M. Decoding visual information from a population of retinal ganglion cells. J. Neurophysiol. 78, 2336–2350 (1997).

    Article  CAS  Google Scholar 

  44. Stanley, G.B., Li, F.F. & Dan, Y. Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus. J. Neurosci. 19, 8036–8042 (1999).

    Article  CAS  Google Scholar 

  45. Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).

    Article  CAS  Google Scholar 

  46. Serruya, M.D., Hatsopoulos, N.G., Paninski, L., Fellows, M.R. & Donoghue, J.P. Instant neural control of a movement signal. Nature 416, 141–142 (2002).

    Article  CAS  Google Scholar 

  47. Zhang, K., Ginzburg, I., McNaughton, B.L. & Sejnowski, T.J. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. J. Neurophysiol. 79, 1017–1044 (1998).

    Article  CAS  Google Scholar 

  48. Wiener, M.C. & Richmond, B.J. Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model. J. Neurosci. 23, 2394–2406 (2003).

    Article  CAS  Google Scholar 

  49. Gao, Y., Black, M.J., Bienenstock, E., Wu, W. & Donoghue, J.P. A quantitative comparison of linear and non-linear models of motor cortical activity for the encoding and decoding of arm motions. First Intl. IEEE/EMBS Conf. on Neural Eng. 189–192 (2003).

  50. Barbieri, R. et al. Dynamic analyses of information encoding by neural ensembles. Neural Comput. 16, 277–307 (2004).

    Article  Google Scholar 

  51. Brockwell, A.E., Rojas, A.L. & Kass, R.E. Recursive Bayesian decoding of motor cortical signals by particle filtering J. Neurophysiol. 91, 1899–1907 (2004).

    Article  CAS  Google Scholar 

  52. Borst, A. & Theunissen, F.E. Information theory and neural coding. Nat. Neurosci. 2, 947–957 (1999).

    Article  CAS  Google Scholar 

  53. Reich, D.S., Melcher, F. & Victor, J.D. Independent and redundant information in nearby cortical neurons. Science 294, 2566–2568 (2001).

    Article  CAS  Google Scholar 

  54. Nirenberg, S., Carcieri, S.M., Jacobs, A.L. & Latham, P.E. Retinal ganglion cells act largely as independent encoders. Nature 411, 698–701 (2001).

    Article  CAS  Google Scholar 

  55. Strong, S.P., Koberle, R., de Ruyter van Steveninck, R.R. & Bialek, W. Entropy and information in neural spike trains. Phys. Rev. Lett. 80, 197–200 (1998).

    Article  CAS  Google Scholar 

  56. Berger, T. Living information theory. IEEE Information Theory Society Newsletter 53, 1–20 (2003).

    Google Scholar 

  57. Stuart, L., Walter, M. & Borisyuk, R. Visualiation of synchronous firing in multi-dimensional spike trains. BioSystems 67, 265–279 (2002).

    Article  CAS  Google Scholar 

  58. Abdallahi, L.M., La Rota, C., Beguin, M. & François, O. Parameter estimation in a model for multidimensional recording of neuronal data: a Gibbsian approximation approach. Biol. Cybern. 89, 170–178 (2003).

    Article  Google Scholar 

  59. Qaquish, B.F. A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means Biometrika 90, 455–464 (2003).

    Article  Google Scholar 

  60. Brown, E.N., Nguyen, D.P., Frank, L.M., Wilson, M.A. & Solo, V. An analysis of neural receptive field plasticity by point process adaptive filtering. Proc. Natl. Acad. Sci. USA 98, 12261–12266 (2001).

    Article  CAS  Google Scholar 

  61. Dayan, P. & Abbott, L.F. Theoretical Neuroscience (MIT Press, Cambridge, 2001).

    Google Scholar 

  62. Shikri, O., Hansel, D. & Sompolinsky, H. Rate models for conductance-based cortical neuronal networks. Neural Comput. 5, 1809–1841 (2003).

    Google Scholar 

  63. Victor, J.D. & Brown, E.N. Information and statistical structure in spike trains. Network Comput. Neural Syst. 14, 1–4 (2003).

    Article  Google Scholar 

  64. Logethetis, N.K., Pauls, J., Augath, M., Trinath, T. & Oelterman, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    Article  Google Scholar 

  65. Aksay, E., Baker, R., Seung, H.S. & Tank, D.W. Correlated discharge among pairs within the oculomotor horizontal velocity-to-position integrator. J. Neurosci. 23, 10852–10858 (2003).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Support for this work was provided in part by NIH grants MH66410 to P.M. and E.N.B., MH62528 to P.M., MH64537 to R.E.K., and MH59733, MH61637 and DA015664 to E.N.B. We thank S. Grün for comments on an earlier draft of this manuscript, G. Gerstein for permission to use Fig. 1d and R. Barbieri for help preparing the figures.

Author information

Authors and Affiliations

  1. Department of Anesthesia and Critical Care, and the Division of Health Sciences and Technology, Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology, Boston, 02114, Massachusetts, USA

    Emery N Brown

  2. Department of Statistics, Carnegie Mellon University and the Center for the Neural Basis of Cognition, Pittsburgh, 15208, Pennsylvania, USA

    Robert E Kass

  3. Cold Spring Harbor Laboratory, Cold Spring Harbor, 11724, New York, USA

    Partha P Mitra

Authors
  1. Emery N Brown
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Robert E Kass
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Partha P Mitra
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Emery N Brown.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brown, E., Kass, R. & Mitra, P. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 7, 456–461 (2004). https://doi.org/10.1038/nn1228

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1228

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing