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Neuroinformatics
Erschienen in: Neuroinformatics 3/2009

01.09.2009

NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies

verfasst von: Randal A. Koene, Betty Tijms, Peter van Hees, Frank Postma, Alexander de Ridder, Ger J. A. Ramakers, Jaap van Pelt, Arjen van Ooyen

Erschienen in: Neuroinformatics | Ausgabe 3/2009

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Abstract

We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.
Vorheriger Artikel On Comparing Neuronal Morphologies with the Constrained Tree-edit-distance

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Metadaten
Titel
NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies
verfasst von
Randal A. Koene
Betty Tijms
Peter van Hees
Frank Postma
Alexander de Ridder
Ger J. A. Ramakers
Jaap van Pelt
Arjen van Ooyen
Publikationsdatum
01.09.2009
Verlag
Humana Press Inc
Erschienen in
Neuroinformatics / Ausgabe 3/2009
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI
https://doi.org/10.1007/s12021-009-9052-3

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