Computational Concepts for Reconstructing and Simulating Brain Tissue.
Biophysically realistic neural networks
Brain tissue modeling
Computational brain science
Data-driven simulation
Digital Twin
Multi-modal data integration
Journal
Advances in experimental medicine and biology
ISSN: 0065-2598
Titre abrégé: Adv Exp Med Biol
Pays: United States
ID NLM: 0121103
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
26
4
2022
pubmed:
27
4
2022
medline:
29
4
2022
Statut:
ppublish
Résumé
It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.
Identifiants
pubmed: 35471542
doi: 10.1007/978-3-030-89439-9_10
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
237-259Informations de copyright
© 2022. The Author(s).
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