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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...

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Detalles Bibliográficos
Autores principales: Chen, Guozhang, Scherr, Franz, Maass, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
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.
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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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