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  Quantitative analysis of neuroanatomy

Budd, J. M. L., Cuntz, H., Eglen, S. J., & Krieger, P. (Eds.). (2016). Quantitative analysis of neuroanatomy. Lausanne: Frontiers Media. doi:10.3389/978-2-88919-796-5.

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Budd_2016_QuantitativeAnalysis.pdf (Publisher version), 58MB
 
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2016
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Copyright © 2007-2016 Frontiers Media SA
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 Creators:
Budd, Julian M. L., Editor
Cuntz, Hermann1, 2, Editor                 
Eglen, Stephen J., Editor
Krieger, Patrik, Editor
Affiliations:
1Cuntz Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society, ou_3381227              
2Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society, Deutschordenstr. 46, 60528 Frankfurt, DE, ou_2074314              

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 Abstract: The true revolution in the age of digital neuroanatomy is the ability to extensively quantify anatomical structures and thus investigate structure-function relationships in great detail. Large-scale projects were recently launched with the aim of providing infrastructure for brain simulations. These projects will increase the need for a precise understanding of brain structure, e.g., through statistical analysis and models.

From articles in this Research Topic, we identify three main themes that clearly illustrate how new quantitative approaches are helping advance our understanding of neural structure and function. First, new approaches to reconstruct neurons and circuits from empirical data are aiding neuroanatomical mapping. Second, methods are introduced to improve understanding of the underlying principles of organization. Third, by combining existing knowledge from lower levels of organization, models can be used to make testable predictions about a higher-level organization where knowledge is absent or poor. This latter approach is useful for examining statistical properties of specific network connectivity when current experimental methods have not yet been able to fully reconstruct whole circuits of more than a few hundred neurons.

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 Dates: 2016
 Publication Status: Published online
 Pages: -
 Publishing info: Lausanne : Frontiers Media
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3389/978-2-88919-796-5
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