Variance predicts salience in central sensory processing
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 s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2014-11-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/03722 |
| _version_ | 1828167307719868416 |
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| author | Ann M Hermundstad John J Briguglio Mary M Conte Jonathan D Victor Vijay Balasubramanian Gašper Tkačik |
| author_facet | Ann M Hermundstad John J Briguglio Mary M Conte Jonathan D Victor Vijay Balasubramanian Gašper Tkačik |
| author_sort | Ann M Hermundstad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T02:14:34Z |
| format | Article |
| id | doaj.art-64f2749f6ab849e8ba861fb88fdc1152 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T02:14:34Z |
| publishDate | 2014-11-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-64f2749f6ab849e8ba861fb88fdc11522022-12-22T03:52:17ZengeLife Sciences Publications LtdeLife2050-084X2014-11-01310.7554/eLife.03722Variance predicts salience in central sensory processingAnn M Hermundstad0John J Briguglio1Mary M Conte2Jonathan D Victor3Vijay Balasubramanian4Gašper Tkačik5Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, United States; Laboratoire de Physique Théorique, École Normale Supérieure, Paris, FranceDepartment of Physics and Astronomy, University of Pennsylvania, Philadelphia, United StatesBrain and Mind Research Institute, Weill Cornell Medical College, New York, United StatesBrain and Mind Research Institute, Weill Cornell Medical College, New York, United StatesDepartment of Physics and Astronomy, University of Pennsylvania, Philadelphia, United States; Laboratoire de Physique Théorique, École Normale Supérieure, Paris, France; Initiative for the Theoretical Sciences, City University of New York Graduate Center, New York, United StatesInstitute of Science and Technology Austria, Klosterneuburg, AustriaInformation 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.https://elifesciences.org/articles/03722natural scene statisticsneural codingvisual cortexnormative theories |
| spellingShingle | Ann M Hermundstad John J Briguglio Mary M Conte Jonathan D Victor Vijay Balasubramanian Gašper Tkačik Variance predicts salience in central sensory processing eLife natural scene statistics neural coding visual cortex normative theories |
| title | Variance predicts salience in central sensory processing |
| title_full | Variance predicts salience in central sensory processing |
| title_fullStr | Variance predicts salience in central sensory processing |
| title_full_unstemmed | Variance predicts salience in central sensory processing |
| title_short | Variance predicts salience in central sensory processing |
| title_sort | variance predicts salience in central sensory processing |
| topic | natural scene statistics neural coding visual cortex normative theories |
| url | https://elifesciences.org/articles/03722 |
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