NETMORPH is a modular simulation tool for building synaptically
connected networks with realistic neuron morphologies.  Axonal and
dendritic morphologies are created by using stochastic rules for the
behavior of individual growth cones, the structures at the tip of
outgrowing axons and dendrites (collectively called neurites) that
mediate neurite elongation and branching. In brief, each growth cone
has at each time step a probability to elongate the trailing neurite,
to branch and produce two daughter growth cones, and to turn and
change the direction of neurite outgrowth. The parameter values of the
outgrowth model can be optimized so as to obtain an optimal match with
the morphology of specific neuron types. Neurons are positioned in 3D
space and grow out independently of each other. Axons and dendrites
are not guided by any extracellular cues. Synapses between neurons are
formed when crossing axonal and dendritic segments come sufficiently
close to each other.

NETMORPH is written in C++ and tailored to a Unix operating
environment. Windows users can provide such an environment through
Cygwin. After compilation of NETMORPH, one can grow single-neuron
morphologies or networks of neurons with realistic morphologies. A
simulation run of NETMORPH is based on a script, a text file
containing the parameter values of the simulation. The output of
NETMORPH consists of a number of files specifying the generated neuron
morphologies and synaptic connectivity. Visualization of neurons and
networks can be done by a basic visualization tool incorporated in
NETMORPH or by a separate java program called NEURON3D.

Provided here are 1) the NETMORPH program (version 2011-06-24), 2) the
NETMORPH manual (updated 2014-04-03), 3) the visualization program
NEURON3D (file name: Neuron4D.rar), and 4) some documentation on
NEURON3D. The NETMORPH manual describes how NETMORPH can be installed,
provides a number of example scripts, and explains all the parameters
that control a NETMORPH simulation. NETMORPH was developed in the
Neuroinformatics Group at the Department of Integrative
Neurophysiology, VU University Amsterdam, The Netherlands, by Randal
Koene, Jaap van Pelt and Arjen van Ooyen, with assistance from Betty
Tijms, Peter van Hees, Frank Postma, Sander de Ridder, Sacha
Hoedemaker, Andrew Carnell and Pieter Laurens Baljon. The work was
supported by grants from the Netherlands Organization for Scientific
Research (CASPAN: 635.100.005) and the European Union (NEURoVERS-it:
019247; SECO: 216593) awarded to Jaap van Pelt and Arjen van Ooyen.
