Skip to main content

Methods for Studying Functional Interactions Among Neuronal Populations

  • Protocol

Part of the book series: METHODS IN MOLECULAR BIOLOGY™ ((MIMB,volume 489))

Abstract

How do populations of neurons work together to control behavior? To study this issue, our group simultaneously records from populations of neurons across multiple electrodes in multiple brain regions during operant behavior. Here, we describe methods for quantifying the relationship between neuronal population activity and performance of operant behavioral tasks. We describe statistical techniques, based on time- and trial-shuffling, that can establish the significance of correlations between multiple and simultaneously recorded spike trains. Then, we describe several approaches to studying functional interactions between neurons, including principal component analysis, cross-correlation analysis, analyses of rate correlations, and analyses of shared predictive information. Finally, we compare these techniques using a sample data set and discuss how the combined use of these techniques can lead to novel insights regarding neuronal interactions during behavior.

This is a preview of subscription content, log in via an institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Nicolelis, M.A., et al., Simultaneous encoding of tactile information by three primate cortical areas. Nat Neurosci, 1998. 1(7): p. 621–30.

    Article  CAS  PubMed  Google Scholar 

  2. Nicolelis, M.A., ed. Methods In Neuronal Ensemble Recording. 1998, CRC Press: Boca Raton, FL.

    Book  Google Scholar 

  3. Wessberg, J., et al., Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature, 2000. 408(6810): p. 361–5.

    Article  CAS  PubMed  Google Scholar 

  4. Carmena, J.M., et al., Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol, 2003. 1(2): p. E42.

    Article  PubMed  Google Scholar 

  5. Laubach, M., N.S. Narayanan, and E.Y. Kimchi, Single-neuron and ensemble contributions to decoding simultaneously recorded spike trains, in Neuronal population recordings, C. Holscher, Editor. 2007.

    Google Scholar 

  6. Shepherd, G., Synaptic Organization of The Brain. 34d ed. 2003, Oxford: Oxford University Press.

    Google Scholar 

  7. Mountcastle, V.B., Perceptual Neuroscience: The Cerebral Cortex. 1998, Cambridge, MA: Harvard College.

    Google Scholar 

  8. Gochin, P.M., et al., Neural ensemble coding in inferior temporal cortex. J Neurophysiol, 1994. 71(6): p. 2325–37.

    CAS  PubMed  Google Scholar 

  9. Britten, K.H., et al., A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis Neurosci, 1996. 13(1): p. 87–100.

    Article  CAS  PubMed  Google Scholar 

  10. Rolls, E.T., A. Treves, and M.J. Tovee, The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex. Exp Brain Res, 1997. 114(1): p. 149–62.

    Article  CAS  PubMed  Google Scholar 

  11. Rolls, E.T., et al., Information encoding in the inferior temporal visual cortex: contributions of the firing rates and the correlations between the firing of neurons. Biol Cybern, 2004. 90(1): p. 19–32.

    Article  PubMed  Google Scholar 

  12. Reich, D.S., F. Mechler, and J.D. Victor, Independent and redundant information in nearby cortical neurons. Science, 2001. 294(5551): p. 2566–8.

    Google Scholar 

  13. Zohary, E., M.N. Shadlen, and W.T. Newsome, Correlated neuronal discharge rate and its implications for psychophysical performance. Nature, 1994. 370(6485): p. 140–3.

    Article  CAS  PubMed  Google Scholar 

  14. Narayanan, N.S., E.Y. Kimchi, and M. Laubach, Redundancy and synergy of neuronal ensembles in motor cortex. J Neurosci, 2005. 25(17): p. 4207–16.

    Article  CAS  PubMed  Google Scholar 

  15. Averbeck, B.B. and D. Lee, Neural noise and movement-related codes in the macaque supplementary motor area. J Neurosci, 2003. 23(20): p. 7630–41.

    CAS  PubMed  Google Scholar 

  16. Averbeck, B.B., et al., Neural activity in prefrontal cortex during copying geometrical shapes. II. Decoding shape segments from neural ensembles. Exp Brain Res, 2003. 150(2): p. 142–53.

    PubMed  Google Scholar 

  17. Averbeck, B.B. and D. Lee, Coding and transmission of information by neural ensembles. Trends Neurosci, 2004. 27(4): p. 225–30.

    Article  CAS  PubMed  Google Scholar 

  18. Dan, Y., et al., Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus. Nat Neurosci, 1998. 1(6): p. 501–7.

    Article  CAS  PubMed  Google Scholar 

  19. Vaadia, E., et al., Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature, 1995. 373(6514): p. 515–8.

    Article  CAS  PubMed  Google Scholar 

  20. Narayanan, N.S., N.K. Horst, and M. Laubach, Reversible inactivations of rat medial prefrontal cortex impair the ability to wait for a stimulus. Neuroscience, 2006.

    Google Scholar 

  21. Narayanan, N.S. and M. Laubach, Top-down control of motor cortex ensembles by dorsomedial prefrontal cortex. Neuron, 2006. 52(5): p. 921–31.

    Article  CAS  PubMed  Google Scholar 

  22. Laubach, M., M. Shuler, and M.A. Nicolelis, Independent component analyses for quantifying neuronal ensemble interactions. J Neurosci Methods, 1999. 94(1): p. 141–54.

    Article  CAS  PubMed  Google Scholar 

  23. Aertsen, A.M. and G.L. Gerstein, Evaluation of neuronal connectivity: sensitivity of cross-correlation. Brain Res, 1985. 340(2): p. {341–54.

    Article  CAS  PubMed  Google Scholar 

  24. Perkel, D.H., et al., Nerve-impulse patterns: A quantitative display technique for three neurons. Brain Res, 1975. 100(2): p. 271–96.

    Article  CAS  PubMed  Google Scholar 

  25. Constantinidis, C., M.N. Franowicz, and P.S. Goldman-Rakic, Coding specificity in cortical microcircuits: A multiple-electrode analysis of primate prefrontal cortex. J Neurosci, 2001. 21(10): p. 3646–55.

    CAS  PubMed  Google Scholar 

  26. Brody, C.D., Slow covariations in neuronal resting potentials can lead to artefactually fast cross-correlations in their spike trains. J Neurophysiol, 1998. 80(6): p. 3345–51.

    CAS  PubMed  Google Scholar 

  27. Aertsen, A.M., et al., Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J Neurophysiol, 1989. 61(5): p. 900–17.

    CAS  PubMed  Google Scholar 

  28. Vaadia, E., K. Kurata, and S.P. Wise, Neuronal activity preceding directional and nondirectional cues in the premotor cortex of rhesus monkeys. Somatosens Mot Res, 1988. 6(2): p. 207–30.

    Article  CAS  PubMed  Google Scholar 

  29. Paz, R., et al., Emotional enhancement of memory via amygdala-driven facilitation of rhinal interactions. Nat Neurosci, 2006. 9(10): p. 1321–9.

    Article  CAS  PubMed  Google Scholar 

  30. Tsukada, M., et al., Dynamical Cell Assembly Hypothesis - Theoretical Possibility of Spatio-temporal Coding in the Cortex. Neural Netw, 1996. 9(8): p. 1303–1350.

    Article  PubMed  Google Scholar 

  31. Gawne, T.J. and B.J. Richmond, How independent are the messages carried by adjacent inferior temporal cortical neurons? J Neurosci, 1993. 13(7): p. 2758–71.

    CAS  PubMed  Google Scholar 

  32. Witten, I. and E. Frank, Data Mining. 2000, San Diego, CA: Academic Press.

    Google Scholar 

  33. Laubach, M., Wavelet-based processing of neuronal spike trains prior to discriminant analysis. J Neurosci Methods, 2004. 134(2): p. 159–68.

    Article  PubMed  Google Scholar 

  34. Mallat, S. and Z. Zhang, Matching pursuits with time-frequency dictionaries. IEEE Transactions, 1993. 41(12): p. 3397–3415.

    Article  Google Scholar 

  35. Friedman, J., Regularized discriminant analysis. Journal of American Statisical Association, 1989. 84(405): p. 166–175.

    Google Scholar 

  36. Hastie, T., R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer Series in Statistics. 2001, New York, NY: Springer-Verlag.

    Google Scholar 

  37. Schneidman, E., W. Bialek, and M.J. Berry, 2nd, Synergy, redundancy, and independence in population codes. J Neurosci, 2003. 23(37): p. 11539–53.

    Google Scholar 

  38. Averbeck, B.B. and D. Lee, Effects of noise correlations on information encoding and decoding. J Neurophysiol, 2006. 95(6): {p. 3633–44.

    Article  PubMed  Google Scholar 

  39. Harris, K.D., et al., Organization of cell assemblies in the hippocampus. Nature, 2003. 424(6948): p. 552–6.

    Article  CAS  PubMed  Google Scholar 

  40. Gray, C.M., et al., Synchronization of oscillatory neuronal responses in cat striate cortex: temporal properties. Vis Neurosci, 1992. 8(4): p. 337–47.

    Article  CAS  PubMed  Google Scholar 

  41. Pesaran, B., et al., Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat Neurosci, 2002. 5(8): p. 805–11.

    Article  CAS  PubMed  Google Scholar 

  42. Averbeck, B.B., P.E. Latham, and A. Pouget, Neural correlations, population coding and computation. Nat Rev Neurosci, 2006. 7(5): p. 358–66.

    Google Scholar 

Download references

Acknowledgments

We thank Eyal Kimchi and Nicole Horst for critical comments and helpful discussions. This work was supported by funds from the Tourette Syndrome Association, Kavli Institute at Yale, and the John B. Pierce Laboratory for ML and from an NIH training grant to the Yale Medical Scientist Training Program and Army Research Office for NSN.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Narayanan, N.S., Laubach, M. (2009). Methods for Studying Functional Interactions Among Neuronal Populations. In: Hyder, F. (eds) Dynamic Brain Imaging. METHODS IN MOLECULAR BIOLOGY™, vol 489. Humana Press. https://doi.org/10.1007/978-1-59745-543-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-59745-543-5_7

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-934115-74-9

  • Online ISBN: 978-1-59745-543-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics