Bringing Anatomical Information into Neuronal Network Models.
Brain atlases
Brain connectivity
Cytoarchitecture
Neuroanatomy
Predictive connectomics
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
For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of "predictive connectomics" estimate connectivity where the data are lacking based on statistical relationships with known quantities. Exploiting organizational principles that link the plethora of data in a unifying framework can be useful for informing computational models. Besides overarching principles, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication.
Identifiants
pubmed: 35471541
doi: 10.1007/978-3-030-89439-9_9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
201-234Informations de copyright
© 2022. Springer Nature Switzerland AG.
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