Noise Correlations and Information Encoding and Decoding

  • Bruno B. Averbeck
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 3)


Neuronal noise is correlated in the brain, and these correlations can affect both information encoding and decoding. In this chapter we discuss the recent progress that has been made, both theoretical and empirical, on how noise correlations affect information encoding and decoding. Specifically, we discuss theoretical results which show the conditions under which correlations either do or do not cause the amount of encoded information to saturate in modestly large populations of neurons. Correspondingly, we also describe the conditions under which information decoding can be affected by the presence of correlations. Complementing the theory, empirical studies have generally shown that the effects of correlations on both encoding and decoding are small in pairs of neurons. However, theory shows that small effects at the level of pairs of neurons can lead to large effects in populations. Thus, it is difficult to draw conclusions about the effects of correlations at the population level by studying pairs of neurons. Therefore, we conclude the chapter by briefly considering the issues around estimating information in larger populations.


Classification Performance Fisher Information Neural Response Tuning Curve Noise Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abbott LF, Dayan P (1999) The effect of correlated variability on the accuracy of a population code. Neural Comput 11:91–101PubMedCrossRefGoogle Scholar
  2. 2.
    Averbeck BB, Crowe DA, Chafee MV, et al. (2003) Neural activity in prefrontal cortex during copying geometrical shapes II. Decoding shape segments from neural ensembles. Exp Brain Res 150:142–153Google Scholar
  3. 3.
    Averbeck BB, Latham PE, Pouget A (2006) Neural correlations, population coding and computation. Nat Rev Neurosci 7:358–366PubMedCrossRefGoogle Scholar
  4. 4.
    Averbeck BB, Lee D (2003) Neural noise and movement-related codes in the macaque supplementary motor area. J Neurosci 23:7630–7641PubMedGoogle Scholar
  5. 5.
    Averbeck BB, Lee D (2004) Coding and transmission of information by neural ensembles. Trends Neurosci 27:225–230PubMedCrossRefGoogle Scholar
  6. 6.
    Averbeck BB, Lee D (2006) Effects of noise correlations on information encoding and decoding. J Neurophysiol 95:3633–3644PubMedCrossRefGoogle Scholar
  7. 7.
    Basseville M (1989) Distance measures for signal processing and pattern recognition. Signal Process 18:349–369CrossRefGoogle Scholar
  8. 8.
    Britten KH, Shadlen MN, Newsome WT, et al. (1993) Responses of neurons in macaque MT to stochastic motion signals. Vis Neurosci 10:1157–1169PubMedCrossRefGoogle Scholar
  9. 9.
    Buracas GT, Zador AM, DeWeese MR, et al. (1998) Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron 20:959–969PubMedCrossRefGoogle Scholar
  10. 10.
    Casella G, Berger RL (1990) Statistical Inference. Duxbury Press, Belmont, CAGoogle Scholar
  11. 11.
    Cover TM, Thomas JA (1991) Elements of Information Theory. Wiley, New YorkCrossRefGoogle Scholar
  12. 12.
    Dan Y, Alonso JM, Usrey WM, et al. (1998) Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus. Nat Neurosci 1:501–507PubMedCrossRefGoogle Scholar
  13. 13.
    Gawne TJ, Kjaer TW, Hertz JA, et al. (1996) Adjacent visual cortical complex cells share about 20% of their stimulus-related information. Cereb Cortex 6:482–489PubMedCrossRefGoogle Scholar
  14. 14.
    Gawne TJ, Richmond BJ (1993) How independent are the messages carried by adjacent inferior temporal cortical neurons? J Neurosci 13:2758–2771PubMedGoogle Scholar
  15. 15.
    Georgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233:1416–1419PubMedCrossRefGoogle Scholar
  16. 16.
    Golledge HD, Panzeri S, Zheng F, et al. (2003) Correlations, feature-binding and population coding in primary visual cortex. Neuroreport 14:1045–1050PubMedCrossRefGoogle Scholar
  17. 17.
    Hinton GE, McClelland JL, Rumelhart DE (1986) Distributed Representations. In: Rumelhart DE and McClelland JL (ed) Parallel Distributed Processing. Explorations in the microstructure of cognition. Volume 1: Foundations. The MIT Press, Cambridge, MAGoogle Scholar
  18. 18.
    Johnson RA, Wichern DW (1998) Applied Multivariate Statistical Analysis. Prentice Hall, Saddle River, NJGoogle Scholar
  19. 19.
    Maynard EM, Hatsopoulos NG, Ojakangas CL, et al. (1999) Neuronal interactions improve cortical population coding of movement direction. J Neurosci 19:8083–8093PubMedGoogle Scholar
  20. 20.
    Montani F, Kohn A, Smith MA, et al. (2007) The role of correlations in direction and contrast coding in the primary visual cortex. J Neurosci 27:2338–2348PubMedCrossRefGoogle Scholar
  21. 21.
    Nirenberg S, Carcieri SM, Jacobs AL, et al. (2001) Retinal ganglion cells act largely as independent encoders. Nature 411:698–701PubMedCrossRefGoogle Scholar
  22. 22.
    Oram MW, Hatsopoulos NG, Richmond BJ, et al. (2001) Excess synchrony in motor cortical neurons provides redundant direction information with that from coarse temporal measures. J Neurophysiol 86:1700–1716PubMedGoogle Scholar
  23. 23.
    Panzeri S, Pola G, Petroni F, et al. (2002) A critical assessment of different measures of the information carried by correlated neuronal firing. Biosystems 67:177–185PubMedCrossRefGoogle Scholar
  24. 24.
    Panzeri S, Schultz SR (2001) A unified approach to the study of temporal, correlational, and rate coding. Neural Comput 13:1311–1349PubMedCrossRefGoogle Scholar
  25. 25.
    Panzeri S, Schultz SR, Treves A, et al. (1999) Correlations and the encoding of information in the nervous system. Proc R Soc Lond B Biol Sci 266:1001–1012CrossRefGoogle Scholar
  26. 26.
    Perkel DH, Bullock TH (1969) Neural Coding. In: Schmitt FO, Melnechuk T, Quarton GC and Adelman G (eds) Neurosciences Research Symposium Summaries. The MIT Press, Cambridge, MA 3: 405–527Google Scholar
  27. 27.
    Petersen RS, Panzeri S, Diamond ME (2001) Population coding of stimulus location in rat somatosensory cortex. Neuron 32:503–514PubMedCrossRefGoogle Scholar
  28. 28.
    Petersen RS, Panzeri S, Diamond ME (2002) Population coding in somatosensory cortex. Curr Opin Neurobiol 12:441–447PubMedCrossRefGoogle Scholar
  29. 29.
    Pola G, Thiele A, Hoffmann KP, et al. (2003) An exact method to quantify the information transmitted by different mechanisms of correlational coding. Network 14:35–60PubMedCrossRefGoogle Scholar
  30. 30.
    Poor HV (1994) An Introduction to Signal Detection and Estimation. Springer, New YorkGoogle Scholar
  31. 31.
    Sejnowski TJ (1988) Neural populations revealed. Nature 332:308Google Scholar
  32. 32.
    Seriès P, Latham PE, Pouget A (2004) Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations. Nat Neurosci 7:1129–1135PubMedCrossRefGoogle Scholar
  33. 33.
    Shamir M, Sompolinsky H (2004) Nonlinear Population Codes. Neural Comput 16:1105–1136PubMedCrossRefGoogle Scholar
  34. 34.
    Shamir M, Sompolinsky H (2006) Implications of neuronal diversity on population coding. Neural Comput 18:1951–1986PubMedCrossRefGoogle Scholar
  35. 35.
    Sompolinsky H, Yoon H, Kang K, et al. (2001) Population coding in neuronal systems with correlated noise. Phys Rev E 64:051904CrossRefGoogle Scholar
  36. 36.
    Strong SP, Koberle R, De Ruyter Van, Steveninck RR, et al. (1998) Entropy and information in neural spike trains. Phys Rev Lett 80:197–200CrossRefGoogle Scholar
  37. 37.
    Tolhurst DJ, Movshon JA, Thompson ID, et al. (1981) The dependence of response amplitude and variance of cat visual cortical neurones on stimulus contrast. Exp Brain Res 41:414–419PubMedCrossRefGoogle Scholar
  38. 38.
    Wilke SD, Eurich CW (2002) Representational accuracy of stochastic neural populations. Neural Comput 14:155–189PubMedCrossRefGoogle Scholar
  39. 39.
    Wu S, Nakahara H, Amari S (2001) Population coding with correlation and an unfaithful model. Neural Comput 13:775–797PubMedCrossRefGoogle Scholar
  40. 40.
    Zohary E, Shadlen MN, Newsome WT (1994) Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370:140–143PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Bruno B. Averbeck
    • 1
  1. 1.Sobell Department of Motor Neuroscience and Movement DisordersInstitute of Neurology, UCLLondonUK

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