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An Efficient and Extendable Python Library to Analyze Neuronal Morphologies

An Efficient and Extendable Python Library to Analyze Neuronal Morphologies Neuroinform (2014) 12:619–622 DOI 10.1007/s12021-014-9232-7 NEWS ITEM An Efficient and Extendable Python Library to Analyze Neuronal Morphologies Benjamin Torben-Nielsen Published online: 13 June 2014 Springer Science+Business Media New York 2014 Neuronal morphology has been of interest to neuroscien- astandaloneversion with agraphicaluserinterface tists since Cajal and Golgi. Due to technical advances and (GUI). The TREES toolbox is a Matlab toolbox. Both tools data-sharing initiatives (Ascoli et al. 2007)wehaveac- allow users to load and quantify (populations of) digitally cess to more neuronal reconstructions than one could reconstructed neurons. The TREES toolbox has the advan- accumulate in a lifetime up to recently. It is known that tage of being implemented in Matlab and hence users can while neuronal morphology is highly diverse and variant easily integrate it in their own work-flow by scripting in (Soltesz 2005) it is pivotal for brain functioning because Matlab. Lately, there is a trend in computational neuro- the overlap between axons and dendrite limits the network science to use the Python programming language but connectivity (Peters’ rule (Peters and Payne 1993)) and there is no standalone program or library in Python to dendrites define how inputs are integrated to produce and perform basic morphological quantification. output signal http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

An Efficient and Extendable Python Library to Analyze Neuronal Morphologies

Neuroinformatics , Volume 12 (4) – Jun 13, 2014

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Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Biomedicine; Neurosciences; Bioinformatics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Neurology
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1007/s12021-014-9232-7
pmid
24924300
Publisher site
See Article on Publisher Site

Abstract

Neuroinform (2014) 12:619–622 DOI 10.1007/s12021-014-9232-7 NEWS ITEM An Efficient and Extendable Python Library to Analyze Neuronal Morphologies Benjamin Torben-Nielsen Published online: 13 June 2014 Springer Science+Business Media New York 2014 Neuronal morphology has been of interest to neuroscien- astandaloneversion with agraphicaluserinterface tists since Cajal and Golgi. Due to technical advances and (GUI). The TREES toolbox is a Matlab toolbox. Both tools data-sharing initiatives (Ascoli et al. 2007)wehaveac- allow users to load and quantify (populations of) digitally cess to more neuronal reconstructions than one could reconstructed neurons. The TREES toolbox has the advan- accumulate in a lifetime up to recently. It is known that tage of being implemented in Matlab and hence users can while neuronal morphology is highly diverse and variant easily integrate it in their own work-flow by scripting in (Soltesz 2005) it is pivotal for brain functioning because Matlab. Lately, there is a trend in computational neuro- the overlap between axons and dendrite limits the network science to use the Python programming language but connectivity (Peters’ rule (Peters and Payne 1993)) and there is no standalone program or library in Python to dendrites define how inputs are integrated to produce and perform basic morphological quantification. output signal

Journal

NeuroinformaticsSpringer Journals

Published: Jun 13, 2014

References