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The brain’s unique take on algorithms

Perspectives for understanding the brain vary across disciplines and this has challenged our ability to describe the brain’s functions. In this comment, we discuss how emerging theoretical computing frameworks that bridge top-down algorithm and bottom-up physics approaches may be ideally suited for...

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Autores principales: Aimone, James B., Parekh, Ojas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10432563/
https://www.ncbi.nlm.nih.gov/pubmed/37587103
http://dx.doi.org/10.1038/s41467-023-40535-z
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Parekh, Ojas
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Parekh, Ojas
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description Perspectives for understanding the brain vary across disciplines and this has challenged our ability to describe the brain’s functions. In this comment, we discuss how emerging theoretical computing frameworks that bridge top-down algorithm and bottom-up physics approaches may be ideally suited for guiding the development of neural computing technologies such as neuromorphic hardware and artificial intelligence. Furthermore, we discuss how this balanced perspective may be necessary to incorporate the neurobiological details that are critical for describing the neural computational disruptions within mental health and neurological disorders.
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spelling pubmed-104325632023-08-18 The brain’s unique take on algorithms Aimone, James B. Parekh, Ojas Nat Commun Comment Perspectives for understanding the brain vary across disciplines and this has challenged our ability to describe the brain’s functions. In this comment, we discuss how emerging theoretical computing frameworks that bridge top-down algorithm and bottom-up physics approaches may be ideally suited for guiding the development of neural computing technologies such as neuromorphic hardware and artificial intelligence. Furthermore, we discuss how this balanced perspective may be necessary to incorporate the neurobiological details that are critical for describing the neural computational disruptions within mental health and neurological disorders. Nature Publishing Group UK 2023-08-16 /pmc/articles/PMC10432563/ /pubmed/37587103 http://dx.doi.org/10.1038/s41467-023-40535-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
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Aimone, James B.
Parekh, Ojas
The brain’s unique take on algorithms
title The brain’s unique take on algorithms
title_full The brain’s unique take on algorithms
title_fullStr The brain’s unique take on algorithms
title_full_unstemmed The brain’s unique take on algorithms
title_short The brain’s unique take on algorithms
title_sort brain’s unique take on algorithms
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10432563/
https://www.ncbi.nlm.nih.gov/pubmed/37587103
http://dx.doi.org/10.1038/s41467-023-40535-z
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