1. Neuroscience
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Variance predicts salience in central sensory processing

  1. Ann M Hermundstad  Is a corresponding author
  2. John J Briguglio
  3. Mary M Conte
  4. Jonathan D Victor
  5. Vijay Balasubramanian
  6. Gašper Tkačik
  1. University of Pennsylvania, United States
  2. Weill Cornell Medical College, United States
  3. Institute of Science and Technology Austria, Austria
Research Article
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Cite this article as: eLife 2014;3:e03722 doi: 10.7554/eLife.03722

Abstract

Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime - when sampling limitations constrain performance - efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.

Article and author information

Author details

  1. Ann M Hermundstad

    University of Pennsylvania, Philadelphia, United States
    For correspondence
    annherm@physics.upenn.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. John J Briguglio

    University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Mary M Conte

    Weill Cornell Medical College, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Jonathan D Victor

    Weill Cornell Medical College, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Vijay Balasubramanian

    University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Gašper Tkačik

    Institute of Science and Technology Austria, Klosterneuburg, Austria
    Competing interests
    The authors declare that no competing interests exist.

Ethics

Human subjects: The human subjects research (visual psychophysics) was approved by the Institutional Review Board of the Weill Cornell Medical College, and was in accord with the World Medical Association Declaration of Helsinki. Informed consent was obtained from each subject prior to the experimental sessions, and consent to publish was obtained from Mary Conte (MC), the one subject who is potentially identifiable by the initials since she is also an author.

Reviewing Editor

  1. Timothy Behrens, Oxford University, United Kingdom

Publication history

  1. Received: June 19, 2014
  2. Accepted: November 13, 2014
  3. Accepted Manuscript published: November 14, 2014 (version 1)
  4. Version of Record published: December 22, 2014 (version 2)

Copyright

© 2014, Hermundstad et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Neuroscience
    Yunguo Yu et al.
    Research Advance Updated

    Using the visual system as a model, we recently showed that the efficient coding principle accounted for the allocation of computational resources in central sensory processing: when sampling an image is the main limitation, resources are devoted to compute the statistical features that are the most variable, and therefore the most informative (eLife 2014;3:e03722. DOI: 10.7554/eLife.03722 Hermundstad et al., 2014). Building on these results, we use single-unit recordings in the macaque monkey to determine where these computations—sensitivity to specific multipoint correlations—occur. We find that these computations take place in visual area V2, primarily in its supragranular layers. The demonstration that V2 neurons are sensitive to the multipoint correlations that are informative about natural images provides a common computational underpinning for diverse but well-recognized aspects of neural processing in V2, including its sensitivity to corners, junctions, illusory contours, figure/ground, and ‘naturalness.’

    1. Neuroscience
    Enrico Schulz et al.
    Research Article Updated

    We investigated how the attenuation of pain with cognitive interventions affects brain connectivity using neuroimaging and a whole brain novel analysis approach. While receiving tonic cold pain, 20 healthy participants performed three different pain attenuation strategies during simultaneous collection of functional imaging data at seven tesla. Participants were asked to rate their pain after each trial. We related the trial-by-trial variability of the attenuation performance to the trial-by-trial functional connectivity strength change of brain data. Across all conditions, we found that a higher performance of pain attenuation was predominantly associated with higher functional connectivity. Of note, we observed an association between low pain and high connectivity for regions that belong to brain regions long associated with pain processing, the insular and cingulate cortices. For one of the cognitive strategies (safe place), the performance of pain attenuation was explained by diffusion tensor imaging metrics of increased white matter integrity.