Authors: Jyh-Ming Lien, Marco Morales, Nancy M. Amato

Venue: Neurocomputing
DOI: 10.1016/S0925-2312(02)00728-2
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Abstract:
The brain's extraordinary computational power to represent and interpret complex natural environments is essentially determined by the topology and geometry of the brain's architectures. We present a framework to construct cortical networks which borrows from probabilistic roadmap methods developed for robotic motion planning. We abstract the network as a large-scale directed graph, and use L-systems and statistical data to neurons that are morphologically indistinguishable from real neurons. We detect connections (synapses) between neurons using geometric proximity tests. 

@article{Lien-npaffc-2003, 
 author = {Jyh-Ming Lien and Marco Morales and Nancy M. Amato}, 
 doi = {https://doi.org/10.1016/S0925-2312(02)00728-2}, 
 issn = {0925-2312}, 
 journal = {Neurocomputing}, 
 keywords = {Cortical networks, PRM, BTS, L-system, Rectangle tree}, 
 note = {Computational Neuroscience: Trends in Research 2003}, 
 pages = {191 - 197}, 
 title = {Neuron PRM: a framework for constructing cortical networks}, 
 url = {http://www.sciencedirect.com/science/article/pii/S0925231202007282}, 
 volume = {52-54}, 
 year = {2003} 
}