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Neuroinformatics. 2009 Sep;7(3):195-210. doi: 10.1007/s12021-009-9052-3. Epub 2009 Aug 12.

NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies.

Author information

1
Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands.

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.

PMID:
19672726
DOI:
10.1007/s12021-009-9052-3
[Indexed for MEDLINE]

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