, Volume 12, Issue 4, pp 619–622 | Cite as

An Efficient and Extendable Python Library to Analyze Neuronal Morphologies

  • Benjamin Torben-Nielsen
News Item

Neuronal morphology has been of interest to neuroscientists since Cajal and Golgi. Due to technical advances and data-sharing initiatives (Ascoli et al. 2007) we have access to more neuronal reconstructions than one could accumulate in a lifetime up to recently. It is known that while neuronal morphology is highly diverse and variant (Soltesz 2005) it is pivotal for brain functioning because the overlap between axons and dendrite limits the network connectivity (Peters’ rule (Peters and Payne 1993)) and dendrites define how inputs are integrated to produce and output signal (Torben-Nielsen and Stiefel 2010). Moreover, morphological anomalies and changes are often implicated in neuro-developmental and degenerative diseases (Kaufmann and Moser 2000). These insights could not have been established without the ability to rigorously quantify neuronal morphologies.

Nowadays quantification is done on reconstructed neuronal morphologies, that is, digital representations of neuronal structures....


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Computational Neuroscience UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan

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