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

Randal A. Koene, Betty Tijms, Peter Van Hees, Frank Postma, Alexander De Ridder, Ger J.A. Ramakers, Jaap Van Pelt, Arjen Van Ooyen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)195-210
Number of pages16
JournalNeuroinformatics
Volume7
Issue number3
DOIs
Publication statusPublished - 1 Sep 2009
Externally publishedYes

Cite this