Nature 454, 995-999 (21 August 2008) | doi:10.1038/nature07140; Received 16 July 2007; Accepted 5 June 2008; Published online 23 July 2008; Corrected 19 September 2008

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

Jonathan W. Pillow1, Jonathon Shlens2, Liam Paninski3, Alexander Sher4, Alan M. Litke4, E. J. Chichilnisky2 & Eero P. Simoncelli5

  1. Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N 3AR, UK
  2. The Salk Institute, 10010 North Torrey Pines Road, San Diego, California 92037, USA
  3. Department of Statistics and Center for Theoretical Neuroscience, Columbia University, 1255 Amsterdam Avenue, New York, New York 10027, USA
  4. Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, California 95064, USA
  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

Correspondence to: Jonathan W. Pillow1 Correspondence and requests for materials should be addressed to J.W.P. (Email: pillow@gatsby.ucl.ac.uk).

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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