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Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified...

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Autores principales: Heiney, Kristine, Huse Ramstad, Ola, Fiskum, Vegard, Christiansen, Nicholas, Sandvig, Axel, Nichele, Stefano, Sandvig, Ioanna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902700/
https://www.ncbi.nlm.nih.gov/pubmed/33643017
http://dx.doi.org/10.3389/fncom.2021.611183
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author Heiney, Kristine
Huse Ramstad, Ola
Fiskum, Vegard
Christiansen, Nicholas
Sandvig, Axel
Nichele, Stefano
Sandvig, Ioanna
author_facet Heiney, Kristine
Huse Ramstad, Ola
Fiskum, Vegard
Christiansen, Nicholas
Sandvig, Axel
Nichele, Stefano
Sandvig, Ioanna
author_sort Heiney, Kristine
collection PubMed
description It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.
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spelling pubmed-79027002021-02-25 Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation Heiney, Kristine Huse Ramstad, Ola Fiskum, Vegard Christiansen, Nicholas Sandvig, Axel Nichele, Stefano Sandvig, Ioanna Front Comput Neurosci Neuroscience It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered. Frontiers Media S.A. 2021-02-10 /pmc/articles/PMC7902700/ /pubmed/33643017 http://dx.doi.org/10.3389/fncom.2021.611183 Text en Copyright © 2021 Heiney, Huse Ramstad, Fiskum, Christiansen, Sandvig, Nichele and Sandvig. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Heiney, Kristine
Huse Ramstad, Ola
Fiskum, Vegard
Christiansen, Nicholas
Sandvig, Axel
Nichele, Stefano
Sandvig, Ioanna
Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation
title Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation
title_full Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation
title_fullStr Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation
title_full_unstemmed Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation
title_short Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation
title_sort criticality, connectivity, and neural disorder: a multifaceted approach to neural computation
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902700/
https://www.ncbi.nlm.nih.gov/pubmed/33643017
http://dx.doi.org/10.3389/fncom.2021.611183
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