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Adaptive filtering enhances information transmission in visual cortex

Nature volume 439, pages 936–942 (2006)Cite this article

Abstract

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depends on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.

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Figure 1: Filters and nonlinearities for two simple cells.
Figure 2: Neural filters compensate for changes in the input power spectrum.
Figure 3: Receptive field adaptation increases information transmission.
Figure 4: Adaptation dynamics.

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Acknowledgements

We acknowledge suggestions from W. Bialek on the design of experiments and subsequent data analysis. We thank M. Caywood, B. St Amant and K. MacLeod for help with experiments. We thank P. Sabes, M. Kvale and S. Palmer for helpful suggestions on statistical aspects of data analysis. Computing resources were provided by the National Science Foundation under the following NSF programs: Partnerships for Advanced Computational Infrastructure at the San Diego Supercomputer Center through an NSF cooperative agreement, Distributed Terascale Facility (DTF) and Terascale Extensions (enhancements to the Extensible Terascale Facility). This research was supported through a grant to K.M. from the National Eye Institute and by a grant from the Swartz Foundation and a career development award from the National Institute of Mental Health to T.S.

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Authors and Affiliations

  1. Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 513 Parnassus Avenue, California, 94143-0444, San Francisco, USA

    Tatyana O. Sharpee, Michael P. Stryker & Kenneth D. Miller

  2. Department of Physiology, University of California, San Francisco, 513 Parnassus Avenue, California, 94143-0444, San Francisco, USA

    Tatyana O. Sharpee, Hiroki Sugihara, Andrei V. Kurgansky, Sergei P. Rebrik, Michael P. Stryker & Kenneth D. Miller

  3. Center for Theoretical Neuroscience, Center for Neurobiology and Behavior, Columbia University Medical School, N.Y.S.P.I. Kolb Research Annex, 1051 Riverside Drive, Unit 87, New York, 10032-2695, New York, USA

    Kenneth D. Miller

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  1. Tatyana O. Sharpee
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Correspondence to Tatyana O. Sharpee.

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Supplementary information

Supplementary Video 1

This movie shows an example of natural stimulus sequence used in the experiment. Movies were shown without colour.

Supplementary Video 2

This movie compares noise STA and MID filters for cell 856 2.

Supplementary Video 3

This movie compares noise MID and natural MID filters for cell 856 2.

Supplementary Video 4

This movie compares noise STA and MID filters for cell 946 3.

Supplementary Video 5

This movie compares noise MID and natural MID filters for cell 946 3 (QuickTime movie; 467KB).

Supplementary Notes

This file contains Supplementary Discussion, Supplementary Methods and Supplementary Figure Legends.

Supplementary Figure 1

This figure shows the spatial frequency profiles of receptive fields from the two example cells of Figure 1.

Supplementary Figure 2

This figure shows spatial frequency sensitivity on a cell-by-cell basis.

Supplementary Figure 3

This figure shows the nonlinear input/output function P(spike|x)/P(spike) for two example cells.

Supplementary Figure 4

This figure shows the increase in information on a cell-by-cell from adjustments in neural filters following adaptation.

Supplementary Figure 5

This figure shows coarse evolution of adaptation of neural filters.

Supplementary Figure 6

This figure shows dynamics of adaptation to natural stimuli in the beginning and final stages.

Supplementary Figure 7

This figure shows information for a filter derived from noise stimuli and applied to both noise and natural stimuli.

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Sharpee, T., Sugihara, H., Kurgansky, A. et al. Adaptive filtering enhances information transmission in visual cortex. Nature 439, 936–942 (2006). https://doi.org/10.1038/nature04519

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