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 'grow' neurons that are morphologically indistinguishable from real neurons. We detect connections (synapses) between neurons using geometric proximity tests.
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