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Extrinsic vs Intrinsic Criticality in Systems with Many Components

Biological systems with many components often exhibit seemingly critical behaviors, characterized by atypically large correlated fluctuations. Yet the underlying causes remain unclear. Here we define and examine two types of criticality. Intrinsic criticality arises from interactions within the syst...

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Autores principales: Ngampruetikorn, Vudtiwat, Nemenman, Ilya, Schwab, David J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557788/
https://www.ncbi.nlm.nih.gov/pubmed/37808085
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author Ngampruetikorn, Vudtiwat
Nemenman, Ilya
Schwab, David J.
author_facet Ngampruetikorn, Vudtiwat
Nemenman, Ilya
Schwab, David J.
author_sort Ngampruetikorn, Vudtiwat
collection PubMed
description Biological systems with many components often exhibit seemingly critical behaviors, characterized by atypically large correlated fluctuations. Yet the underlying causes remain unclear. Here we define and examine two types of criticality. Intrinsic criticality arises from interactions within the system which are fine-tuned to a critical point. Extrinsic criticality, in contrast, emerges without fine tuning when observable degrees of freedom are coupled to unobserved fluctuating variables. We unify both types of criticality using the language of learning and information theory. We show that critical correlations, intrinsic or extrinsic, lead to diverging mutual information between two halves of the system, and are a feature of learning problems, in which the unobserved fluctuations are inferred from the observable degrees of freedom. We argue that extrinsic criticality is equivalent to standard inference, whereas intrinsic criticality describes fractional learning, in which the amount to be learned depends on the system size. We show further that both types of criticality are on the same continuum, connected by a smooth crossover. In addition, we investigate the observability of Zipf’s law, a power-law rank-frequency distribution often used as an empirical signature of criticality. We find that Zipf’s law is a robust feature of extrinsic criticality but can be nontrivial to observe for some intrinsically critical systems, including critical mean-field models We further demonstrate that models with global dynamics, such as oscillatory models, can produce observable Zipf’s law without relying on either external fluctuations or fine tuning. Our findings suggest that while possible in theory, fine tuning is not the only, nor the most likely, explanation for the apparent ubiquity of criticality in biological systems with many components. Our work offers an alternative interpretation in which criticality, specifically extrinsic criticality, results from the adaptation of collective behavior to external stimuli.
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spelling pubmed-105577882023-10-07 Extrinsic vs Intrinsic Criticality in Systems with Many Components Ngampruetikorn, Vudtiwat Nemenman, Ilya Schwab, David J. ArXiv Article Biological systems with many components often exhibit seemingly critical behaviors, characterized by atypically large correlated fluctuations. Yet the underlying causes remain unclear. Here we define and examine two types of criticality. Intrinsic criticality arises from interactions within the system which are fine-tuned to a critical point. Extrinsic criticality, in contrast, emerges without fine tuning when observable degrees of freedom are coupled to unobserved fluctuating variables. We unify both types of criticality using the language of learning and information theory. We show that critical correlations, intrinsic or extrinsic, lead to diverging mutual information between two halves of the system, and are a feature of learning problems, in which the unobserved fluctuations are inferred from the observable degrees of freedom. We argue that extrinsic criticality is equivalent to standard inference, whereas intrinsic criticality describes fractional learning, in which the amount to be learned depends on the system size. We show further that both types of criticality are on the same continuum, connected by a smooth crossover. In addition, we investigate the observability of Zipf’s law, a power-law rank-frequency distribution often used as an empirical signature of criticality. We find that Zipf’s law is a robust feature of extrinsic criticality but can be nontrivial to observe for some intrinsically critical systems, including critical mean-field models We further demonstrate that models with global dynamics, such as oscillatory models, can produce observable Zipf’s law without relying on either external fluctuations or fine tuning. Our findings suggest that while possible in theory, fine tuning is not the only, nor the most likely, explanation for the apparent ubiquity of criticality in biological systems with many components. Our work offers an alternative interpretation in which criticality, specifically extrinsic criticality, results from the adaptation of collective behavior to external stimuli. Cornell University 2023-09-25 /pmc/articles/PMC10557788/ /pubmed/37808085 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Ngampruetikorn, Vudtiwat
Nemenman, Ilya
Schwab, David J.
Extrinsic vs Intrinsic Criticality in Systems with Many Components
title Extrinsic vs Intrinsic Criticality in Systems with Many Components
title_full Extrinsic vs Intrinsic Criticality in Systems with Many Components
title_fullStr Extrinsic vs Intrinsic Criticality in Systems with Many Components
title_full_unstemmed Extrinsic vs Intrinsic Criticality in Systems with Many Components
title_short Extrinsic vs Intrinsic Criticality in Systems with Many Components
title_sort extrinsic vs intrinsic criticality in systems with many components
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557788/
https://www.ncbi.nlm.nih.gov/pubmed/37808085
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