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Spatio-temporal correlations and visual signalling in a complete neuronal population

Nature volume 454, pages 995999 (21 August 2008) | Download Citation

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Abstract

Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies1,2,3, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses4,5. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding6. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.

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Change history

  • 19 September 2008

    In the online-only extended Methods, two equations were corrected on 19 September 2008. Please see the erratum at the end of the PDF for details.

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Acknowledgements

We thank M. Bethge, C. Brody, D. Butts, P. Latham, M. Lengyel, S. Nirenberg and R. Sussman for comments and discussions; G. Field, M. Greschner, J. Gauthier and C. Hulse for experimental assistance; M. I. Grivich, D. Petrusca, W. Dabrowski, A. Grillo, P. Grybos, P. Hottowy and S. Kachiguine for technical development; H. Fox, M. Taffe, E. Callaway and K. Osborn for providing access to retinas; and S. Barry for machining. Funding was provided a Royal Society USA/Canada Research Fellowship (J.W.P.); NSF IGERT DGE-03345 (J.S.); NEI grant EY018003 (E.J.C., L.P. and E.P.S.); Gatsby Foundation Pilot Grant (L.P.); Burroughs Wellcome Fund Career Award at the Scientific Interface (A.S.); US National Science Foundation grant PHY-0417175 (A.M.L.); McKnight Foundation (A.M.L. and E.J.C.); and HHMI (J.W.P., L.P. and E.P.S.).

Author information

Affiliations

  1. Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N 3AR, UK

    • Jonathan W. Pillow
  2. The Salk Institute, 10010 North Torrey Pines Road, San Diego, California 92037, USA

    • Jonathon Shlens
    •  & E. J. Chichilnisky
  3. Department of Statistics and Center for Theoretical Neuroscience, Columbia University, 1255 Amsterdam Avenue, New York, New York 10027, USA

    • Liam Paninski
  4. Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, California 95064, USA

    • Alexander Sher
    •  & Alan M. Litke
  5. Howard Hughes Medical Institute, Center for Neural Science, and Courant Institute of Mathematical Sciences, New York University, 4 Washington Place, Room 809, New York, New York 10003, USA

    • Eero P. Simoncelli

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Corresponding author

Correspondence to Jonathan W. Pillow.

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    Supplementary Information 1

    The file contains Supplementary Discussion with Supplementary Figures S1-S10 and Legends.

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https://doi.org/10.1038/nature07140

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