Quantitative analysis of neuroanatomy

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...

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Bibliographic Details
Main Author: Hermann Cuntz (auth)
Other Authors: Stephen J. Eglen (auth), Julian M. L. Budd (auth), Patrik Krieger (auth)
Format: Electronic Book Chapter
Language:English
Published: Frontiers Media SA 2016
Series:Frontiers Research Topics
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Online Access:DOAB: download the publication
DOAB: description of the publication
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520 |a 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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653 |a Quantitative morphology 
653 |a connectomics 
653 |a spatial statistics 
653 |a Dendrites 
653 |a neuronal modelling 
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