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Discriminating Natural Image Statistics from Neuronal Population Codes
The power law provides an efficient description of amplitude spectra of natural scenes. Psychophysical studies have shown that the forms of the amplitude spectra are clearly related to human visual performance, indicating that the statistical parameters in natural scenes are represented in the nervo...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845616/ https://www.ncbi.nlm.nih.gov/pubmed/20360849 http://dx.doi.org/10.1371/journal.pone.0009704 |
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author | Tajima, Satohiro Okada, Masato |
author_facet | Tajima, Satohiro Okada, Masato |
author_sort | Tajima, Satohiro |
collection | PubMed |
description | The power law provides an efficient description of amplitude spectra of natural scenes. Psychophysical studies have shown that the forms of the amplitude spectra are clearly related to human visual performance, indicating that the statistical parameters in natural scenes are represented in the nervous system. However, the underlying neuronal computation that accounts for the perception of the natural image statistics has not been thoroughly studied. We propose a theoretical framework for neuronal encoding and decoding of the image statistics, hypothesizing the elicited population activities of spatial-frequency selective neurons observed in the early visual cortex. The model predicts that frequency-tuned neurons have asymmetric tuning curves as functions of the amplitude spectra falloffs. To investigate the ability of this neural population to encode the statistical parameters of the input images, we analyze the Fisher information of the stochastic population code, relating it to the psychophysically measured human ability to discriminate natural image statistics. The nature of discrimination thresholds suggested by the computational model is consistent with experimental data from previous studies. Of particular interest, a reported qualitative disparity between performance in fovea and parafovea can be explained based on the distributional difference over preferred frequencies of neurons in the current model. The threshold shows a peak at a small falloff parameter when the neuronal preferred spatial frequencies are narrowly distributed, whereas the threshold peak vanishes for a neural population with a more broadly distributed frequency preference. These results demonstrate that the distributional property of neuronal stimulus preference can play a crucial role in linking microscopic neurophysiological phenomena and macroscopic human behaviors. |
format | Text |
id | pubmed-2845616 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-28456162010-04-01 Discriminating Natural Image Statistics from Neuronal Population Codes Tajima, Satohiro Okada, Masato PLoS One Research Article The power law provides an efficient description of amplitude spectra of natural scenes. Psychophysical studies have shown that the forms of the amplitude spectra are clearly related to human visual performance, indicating that the statistical parameters in natural scenes are represented in the nervous system. However, the underlying neuronal computation that accounts for the perception of the natural image statistics has not been thoroughly studied. We propose a theoretical framework for neuronal encoding and decoding of the image statistics, hypothesizing the elicited population activities of spatial-frequency selective neurons observed in the early visual cortex. The model predicts that frequency-tuned neurons have asymmetric tuning curves as functions of the amplitude spectra falloffs. To investigate the ability of this neural population to encode the statistical parameters of the input images, we analyze the Fisher information of the stochastic population code, relating it to the psychophysically measured human ability to discriminate natural image statistics. The nature of discrimination thresholds suggested by the computational model is consistent with experimental data from previous studies. Of particular interest, a reported qualitative disparity between performance in fovea and parafovea can be explained based on the distributional difference over preferred frequencies of neurons in the current model. The threshold shows a peak at a small falloff parameter when the neuronal preferred spatial frequencies are narrowly distributed, whereas the threshold peak vanishes for a neural population with a more broadly distributed frequency preference. These results demonstrate that the distributional property of neuronal stimulus preference can play a crucial role in linking microscopic neurophysiological phenomena and macroscopic human behaviors. Public Library of Science 2010-03-25 /pmc/articles/PMC2845616/ /pubmed/20360849 http://dx.doi.org/10.1371/journal.pone.0009704 Text en Tajima, Okada. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Tajima, Satohiro Okada, Masato Discriminating Natural Image Statistics from Neuronal Population Codes |
title | Discriminating Natural Image Statistics from Neuronal Population Codes |
title_full | Discriminating Natural Image Statistics from Neuronal Population Codes |
title_fullStr | Discriminating Natural Image Statistics from Neuronal Population Codes |
title_full_unstemmed | Discriminating Natural Image Statistics from Neuronal Population Codes |
title_short | Discriminating Natural Image Statistics from Neuronal Population Codes |
title_sort | discriminating natural image statistics from neuronal population codes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845616/ https://www.ncbi.nlm.nih.gov/pubmed/20360849 http://dx.doi.org/10.1371/journal.pone.0009704 |
work_keys_str_mv | AT tajimasatohiro discriminatingnaturalimagestatisticsfromneuronalpopulationcodes AT okadamasato discriminatingnaturalimagestatisticsfromneuronalpopulationcodes |