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ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks

An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generati...

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Autores principales: Herbers, Patrick, Calvo, Iago, Diaz-Pier, Sandra, Robles, Oscar D., Mata, Susana, Toharia, Pablo, Pastor, Luis, Peyser, Alexander, Morrison, Abigail, Klijn, Wouter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8777257/
https://www.ncbi.nlm.nih.gov/pubmed/35069166
http://dx.doi.org/10.3389/fninf.2021.766697
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author Herbers, Patrick
Calvo, Iago
Diaz-Pier, Sandra
Robles, Oscar D.
Mata, Susana
Toharia, Pablo
Pastor, Luis
Peyser, Alexander
Morrison, Abigail
Klijn, Wouter
author_facet Herbers, Patrick
Calvo, Iago
Diaz-Pier, Sandra
Robles, Oscar D.
Mata, Susana
Toharia, Pablo
Pastor, Luis
Peyser, Alexander
Morrison, Abigail
Klijn, Wouter
author_sort Herbers, Patrick
collection PubMed
description An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model.
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spelling pubmed-87772572022-01-22 ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks Herbers, Patrick Calvo, Iago Diaz-Pier, Sandra Robles, Oscar D. Mata, Susana Toharia, Pablo Pastor, Luis Peyser, Alexander Morrison, Abigail Klijn, Wouter Front Neuroinform Neuroscience An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model. Frontiers Media S.A. 2022-01-07 /pmc/articles/PMC8777257/ /pubmed/35069166 http://dx.doi.org/10.3389/fninf.2021.766697 Text en Copyright © 2022 Herbers, Calvo, Diaz-Pier, Robles, Mata, Toharia, Pastor, Peyser, Morrison and Klijn. https://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
Herbers, Patrick
Calvo, Iago
Diaz-Pier, Sandra
Robles, Oscar D.
Mata, Susana
Toharia, Pablo
Pastor, Luis
Peyser, Alexander
Morrison, Abigail
Klijn, Wouter
ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks
title ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks
title_full ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks
title_fullStr ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks
title_full_unstemmed ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks
title_short ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks
title_sort congen—a simulator-agnostic visual language for definition and generation of connectivity in large and multiscale neural networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8777257/
https://www.ncbi.nlm.nih.gov/pubmed/35069166
http://dx.doi.org/10.3389/fninf.2021.766697
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