NETMORPH: A framework for the stochastic generation of large scale neuronal networks with realistic morphology
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1
VU University, Netherlands
We present a novel simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. Existing models for network generation either do not consider the detailed morphology of single neurons, or, if they do, do not take into account the developmental aspects of morphogenesis and synapse formation. 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, the various actions of the growth cone, such as 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 are determined on the basis of proximity between axonal and dendritic branches. NETMORPH is a flexible and highly modular tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Here we show that realistic neuronal morphologies together with a simple synapse formation rule based on proximity can already produce synaptic connectivity patterns similar to those observed experimentally, particularly with respect to the distribution of connection lengths and the distribution of synapses on dendrites. Interestingly, our preliminary analyses indicate that the networks generated in this way exhibit characteristics of small-world connectivity, with a low frequency of highly connected neurons and a high frequency of weakly connected neurons.
Conference:
Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008.
Presentation Type:
Poster and Short Oral Presentation
Topic:
Large Scale Modeling
Citation:
Postma
F,
Koene
R,
Van Pelt
J and
Van Ooyen
A
(2008). NETMORPH: A framework for the stochastic generation of large scale neuronal networks with realistic morphology.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2008.
doi: 10.3389/conf.neuro.11.2008.01.089
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Received:
28 Jul 2008;
Published Online:
28 Jul 2008.
*
Correspondence:
Frank Postma, VU University, Cairo, Netherlands, f.postma@student.vu.nl