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Efficient information coding and degeneracy in the nervous system

Efficient information coding (EIC) is a universal biological framework rooted in the fundamental principle that system responses should match their natural stimulus statistics for maximizing environmental information. Quantitatively assessed through information theory, such adaptation to the environ...

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Autores principales: Seenivasan, Pavithraa, Narayanan, Rishikesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613645/
https://www.ncbi.nlm.nih.gov/pubmed/35985074
http://dx.doi.org/10.1016/j.conb.2022.102620
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author Seenivasan, Pavithraa
Narayanan, Rishikesh
author_facet Seenivasan, Pavithraa
Narayanan, Rishikesh
author_sort Seenivasan, Pavithraa
collection PubMed
description Efficient information coding (EIC) is a universal biological framework rooted in the fundamental principle that system responses should match their natural stimulus statistics for maximizing environmental information. Quantitatively assessed through information theory, such adaptation to the environment occurs at all biological levels and timescales. The context dependence of environmental stimuli and the need for stable adaptations make EIC a daunting task. We argue that biological complexity is the principal architect that subserves deft execution of stable EIC. Complexity in a system is characterized by several functionally segregated subsystems that show a high degree of functional integration when they interact with each other. Complex biological systems manifest heterogeneities and degeneracy, wherein structurally different subsystems could interact to yield the same functional outcome. We argue that complex systems offer several choices that effectively implement EIC and homeostasis for each of the different contexts encountered by the system.
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spelling pubmed-76136452022-10-01 Efficient information coding and degeneracy in the nervous system Seenivasan, Pavithraa Narayanan, Rishikesh Curr Opin Neurobiol Article Efficient information coding (EIC) is a universal biological framework rooted in the fundamental principle that system responses should match their natural stimulus statistics for maximizing environmental information. Quantitatively assessed through information theory, such adaptation to the environment occurs at all biological levels and timescales. The context dependence of environmental stimuli and the need for stable adaptations make EIC a daunting task. We argue that biological complexity is the principal architect that subserves deft execution of stable EIC. Complexity in a system is characterized by several functionally segregated subsystems that show a high degree of functional integration when they interact with each other. Complex biological systems manifest heterogeneities and degeneracy, wherein structurally different subsystems could interact to yield the same functional outcome. We argue that complex systems offer several choices that effectively implement EIC and homeostasis for each of the different contexts encountered by the system. 2022-10-01 2022-08-17 /pmc/articles/PMC7613645/ /pubmed/35985074 http://dx.doi.org/10.1016/j.conb.2022.102620 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Seenivasan, Pavithraa
Narayanan, Rishikesh
Efficient information coding and degeneracy in the nervous system
title Efficient information coding and degeneracy in the nervous system
title_full Efficient information coding and degeneracy in the nervous system
title_fullStr Efficient information coding and degeneracy in the nervous system
title_full_unstemmed Efficient information coding and degeneracy in the nervous system
title_short Efficient information coding and degeneracy in the nervous system
title_sort efficient information coding and degeneracy in the nervous system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613645/
https://www.ncbi.nlm.nih.gov/pubmed/35985074
http://dx.doi.org/10.1016/j.conb.2022.102620
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