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Journal of Computational Neuroscience

, Volume 12, Issue 3, pp 165–174 | Cite as

Redundancy and Synergy Arising from Pairwise Correlations in Neuronal Ensembles

  • Michele Bezzi
  • Mathew E. Diamond
  • Alessandro Treves
Article

Abstract

Multielectrode arrays allow recording of the activity of many single neurons, from which correlations can be calculated. The functional roles of correlations can be revealed by measures of the information conveyed by neuronal activity; a simple formula has been shown to discriminate the information transmitted by individual spikes from the positive or negative contributions due to correlations (Panzeri et al., 1999). Here, this analysis, previously applied to recordings from small ensembles, is developed further by considering a model of a large ensemble, in which correlations among the signal and noise components of neuronal firing are small in absolute value and entirely random in origin. Even such small random correlations are shown to lead to large possible synergy or redundancy, whenever the time window for extracting information from neuronal firing extends to the order of the mean interspike interval. In addition, a sample of recordings from rat barrel cortex illustrates the mean time window at which such “corrections” dominate when correlations are, as often in the real brain, neither random nor small. The presence of this kind of correlations for a large ensemble of cells restricts further the time of validity of the expansion.

mutual information population coding short-time expansion temporal coding somatosensory system barrel cortex 

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References

  1. Aertsen AMHJ, Gerstein GL, Habib MK, Palm G (1989) Dynamics of neural firing correlation. J. Neurophysol. 61: 900–917.Google Scholar
  2. Amit DJ (1997) Is synchronization necessary and is it sufficient? Behav. Brain Sci. 20: 683.Google Scholar
  3. Bialek W, Rieke F, de Ruyter van Steveninck RR, Warland D (1991) Reading a neural code. Science 252: 1854–1857.Google Scholar
  4. deCharms RC, Merzenich MM (1996) Primary cortical representation of sounds by the coordination of action potential. Nature 381: 610–613.Google Scholar
  5. Eckhorn R, Pöpel B (1974) Rigorous and extended application of information theory to the afferent visual system of the cat. I. Basic concept. Kybernetik 16: 191–200.Google Scholar
  6. Gawne TJ, Richmond BJ (1993) How independent are the messages carried by adjacent inferior temporal cortical neurons? J. Neurosci. 13: 2758–2771.Google Scholar
  7. Golledge DR, Hildetag CC, Tovee MJ (1996) A solution to the binding problem? Curr. Biol. 6: 1092–1095.Google Scholar
  8. Lebedev MA, Mirabella G, Erchova I, Diamond ME (2000) Experience-dependent plasticity of rat barrel cortex: Redistribution of activity across barrel-columns. Cerebral Cortex 10: 23–31.Google Scholar
  9. Martignon L, Deco G, Laskey K, Diamond M, Freiwald W, Vaadia E (2000) Neural coding: Higher order temporal patterns in the neurostatics of cell assemblies. Neural Comput. 12: 1–33.Google Scholar
  10. Maynard EM, Hatsopoulos NG, Ojakangas CL, Acuna BD, Sanes JN, Normann RA, Donoghue JP (1999) Neuronal interactions improve cortical population coding ofmovement direction. J. Neurosci. 19: 8083–8093.Google Scholar
  11. Optican LM, Richmond BJ (1987) Temporal encoding of twodimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis. J. Neurophysiol. 76: 3986–3982.Google Scholar
  12. Panzeri S, Schultz SR (2001) A unified approach to the study of temporal, correlational and rate coding. Neural Comput. 13: 1311–1349.Google Scholar
  13. Panzeri S, Schultz SR, Treves A, Rolls ET (1999) Correlations and the encoding of information in the nervous system. Proc. Roy. Soc. (London) B 266: 1001–1012.Google Scholar
  14. Panzeri S, Treves A (1996) Analytical estimates of limited sampling biases in different information measures. Network 7: 87–107.Google Scholar
  15. Riehle A, Grun S, Diesmann M, Aertsen AMHJ (1997) Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278: 1950–1953.Google Scholar
  16. Rieke F, Warland D, de Ruyter van Steveninck RR, Bialek W (1996) Spikes: Exploring the Neural Code. MIT Press, Cambridge, MA.Google Scholar
  17. Rolls ET, Treves A (1998) Neural Networks and Brain Function. Oxford University Press, Oxford.Google Scholar
  18. Rolls ET, Treves A, Tovee MJ (1997) The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex. Exper. Brain Res. 114: 149–162.Google Scholar
  19. Shadlen MN, Newsome WT(1998) The variable discharge of cortical neurons: Implications for connectivity, computation and coding. J. Neurosci. 18: 3870–3896.Google Scholar
  20. Shannon CE (1948) A mathematical theory of communication. AT&T Bell Lab. Tech. J. 27: 279–423.Google Scholar
  21. Singer W, Engel AK, Kreiter AK, Munk MHJ, Neuenschwander S, Roelfsema P (1997) Neuronal assemblies: Necessity, signature and detectability. Trends. Cogn. Sci. 1: 252–261.Google Scholar
  22. Skaggs WE, McNaughton BL, Wilson MA, Barnes CA(1992) Quantification of what it is that hippocampal cell fires encodes. Soc.Neurosci. Abstr.: 1216.Google Scholar
  23. Vaadia E, Haalman I, Abeles M, Bergaman H, Prut Y, Slovin H, Aertsen A (1995) Dynamics of neural interactions in monkey cortex in relation to behavioural events. Nature 373: 515–518.Google Scholar
  24. Zohary E, Shadlen MN, Newsome WT (1994) Correlated neuronal discharge rate and its implication for psychophysical performance. Nature 370: 140–143.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Michele Bezzi
    • 1
  • Mathew E. Diamond
    • 1
  • Alessandro Treves
    • 1
  1. 1.SISSA Program in NeuroscienceTriesteItaly

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