The brain has extraordinary computational power to represent and interpret complex natural environments. These natural computations are essentially determined by the topology and geometry of the brain's architectures. We present a framework to construct a 3D model of a cortical network using probabilistic roadmap methods. Although not the usual motion planning problem, our objective of building a network that encodes the pathways of the cortical network is analogous to the PRM objective of constructing roadmaps that contain a representative sample of feasible paths. We represent the network as a large-scale directed graph, and use L-systems and statistics data to `grow' neurons that are morphologically indistinguishable from real neurons. We detect connections (synapses) between neurons using geometric proximity tests.