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

complexity connectivity criticality in vitro neural networks neural computation neural disorder neuronal avalanches plasticity

Journal

Frontiers in computational neuroscience
ISSN: 1662-5188
Titre abrégé: Front Comput Neurosci
Pays: Switzerland
ID NLM: 101477956

Informations de publication

Date de publication:
2021
Historique:
received: 28 09 2020
accepted: 18 01 2021
entrez: 1 3 2021
pubmed: 2 3 2021
medline: 2 3 2021
Statut: epublish

Résumé

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.

Identifiants

pubmed: 33643017
doi: 10.3389/fncom.2021.611183
pmc: PMC7902700
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

611183

Informations de copyright

Copyright © 2021 Heiney, Huse Ramstad, Fiskum, Christiansen, Sandvig, Nichele and Sandvig.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Kristine Heiney (K)

Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Ola Huse Ramstad (O)

Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Vegard Fiskum (V)

Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Nicholas Christiansen (N)

Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Axel Sandvig (A)

Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Department of Clinical Neuroscience, Umeå University Hospital, Umeå, Sweden.
Department of Neurology, St. Olav's Hospital, Trondheim, Norway.

Stefano Nichele (S)

Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
Department of Holistic Systems, Simula Metropolitan, Oslo, Norway.

Ioanna Sandvig (I)

Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Classifications MeSH