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Efficient processing of natural scenes in visual cortex

Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This “efficient coding” principle has been used to explain many aspe...

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Autores principales: Tesileanu, Tiberiu, Piasini, Eugenio, Balasubramanian, Vijay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760692/
https://www.ncbi.nlm.nih.gov/pubmed/36545653
http://dx.doi.org/10.3389/fncel.2022.1006703
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author Tesileanu, Tiberiu
Piasini, Eugenio
Balasubramanian, Vijay
author_facet Tesileanu, Tiberiu
Piasini, Eugenio
Balasubramanian, Vijay
author_sort Tesileanu, Tiberiu
collection PubMed
description Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This “efficient coding” principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.
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spelling pubmed-97606922022-12-20 Efficient processing of natural scenes in visual cortex Tesileanu, Tiberiu Piasini, Eugenio Balasubramanian, Vijay Front Cell Neurosci Cellular Neuroscience Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This “efficient coding” principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience. Frontiers Media S.A. 2022-12-05 /pmc/articles/PMC9760692/ /pubmed/36545653 http://dx.doi.org/10.3389/fncel.2022.1006703 Text en Copyright © 2022 Tesileanu, Piasini and Balasubramanian. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular Neuroscience
Tesileanu, Tiberiu
Piasini, Eugenio
Balasubramanian, Vijay
Efficient processing of natural scenes in visual cortex
title Efficient processing of natural scenes in visual cortex
title_full Efficient processing of natural scenes in visual cortex
title_fullStr Efficient processing of natural scenes in visual cortex
title_full_unstemmed Efficient processing of natural scenes in visual cortex
title_short Efficient processing of natural scenes in visual cortex
title_sort efficient processing of natural scenes in visual cortex
topic Cellular Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760692/
https://www.ncbi.nlm.nih.gov/pubmed/36545653
http://dx.doi.org/10.3389/fncel.2022.1006703
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