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A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing
We analyze visual processing capabilities of a large-scale model for area V1 that arguably provides the most comprehensive accumulation of anatomical and neurophysiological data to date. We find that this brain-like neural network model can reproduce a number of characteristic visual processing capa...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629744/ https://www.ncbi.nlm.nih.gov/pubmed/36322646 http://dx.doi.org/10.1126/sciadv.abq7592 |
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author | Chen, Guozhang Scherr, Franz Maass, Wolfgang |
author_facet | Chen, Guozhang Scherr, Franz Maass, Wolfgang |
author_sort | Chen, Guozhang |
collection | PubMed |
description | We analyze visual processing capabilities of a large-scale model for area V1 that arguably provides the most comprehensive accumulation of anatomical and neurophysiological data to date. We find that this brain-like neural network model can reproduce a number of characteristic visual processing capabilities of the brain, in particular the capability to solve diverse visual processing tasks, also on temporally dispersed visual information, with remarkable robustness to noise. This V1 model, whose architecture and neurons markedly differ from those of deep neural networks used in current artificial intelligence (AI), such as convolutional neural networks (CNNs), also reproduces a number of characteristic neural coding properties of the brain, which provides explanations for its superior noise robustness. Because visual processing is substantially more energy efficient in the brain compared with CNNs in AI, such brain-like neural networks are likely to have an impact on future technology: as blueprints for visual processing in more energy-efficient neuromorphic hardware. |
format | Online Article Text |
id | pubmed-9629744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-96297442022-11-04 A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing Chen, Guozhang Scherr, Franz Maass, Wolfgang Sci Adv Neuroscience We analyze visual processing capabilities of a large-scale model for area V1 that arguably provides the most comprehensive accumulation of anatomical and neurophysiological data to date. We find that this brain-like neural network model can reproduce a number of characteristic visual processing capabilities of the brain, in particular the capability to solve diverse visual processing tasks, also on temporally dispersed visual information, with remarkable robustness to noise. This V1 model, whose architecture and neurons markedly differ from those of deep neural networks used in current artificial intelligence (AI), such as convolutional neural networks (CNNs), also reproduces a number of characteristic neural coding properties of the brain, which provides explanations for its superior noise robustness. Because visual processing is substantially more energy efficient in the brain compared with CNNs in AI, such brain-like neural networks are likely to have an impact on future technology: as blueprints for visual processing in more energy-efficient neuromorphic hardware. American Association for the Advancement of Science 2022-11-02 /pmc/articles/PMC9629744/ /pubmed/36322646 http://dx.doi.org/10.1126/sciadv.abq7592 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Neuroscience Chen, Guozhang Scherr, Franz Maass, Wolfgang A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing |
title | A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing |
title_full | A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing |
title_fullStr | A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing |
title_full_unstemmed | A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing |
title_short | A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing |
title_sort | data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629744/ https://www.ncbi.nlm.nih.gov/pubmed/36322646 http://dx.doi.org/10.1126/sciadv.abq7592 |
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