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

Koene, R. A., Tijms, B., Van Hees, P., Postma, F., De Ridder, A., Ramakers, G. J. A, Van Pelt, J., and Van Ooyen, A. (2009). Neuroinformatics 7: 195-210. [Full text: PDF]

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.

NETMORPH (version 2011-06-24) and its manual (updated 2014-04-03) can be freely downloaded from ModelDB (accession number 182135). For more information, see also my NETMORPH page and the Scholarpedia article on NETMORPH.