Neuron PRM: a framework for constructing cortical networks Academic Article

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 'grow' neurons that are morphologically indistinguishable from real neurons. We detect connections (synapses) between neurons using geometric proximity tests. 2003 Elsevier Science B.V. All rights reserved.

authors

• Amato, Nancy

published proceedings

• NEUROCOMPUTING

author list (cited authors)

• Lien, J. M., Morales, M., & Amato, N. M.

citation count

• 6

complete list of authors

• Lien, JM||Morales, M||Amato, NM

publication date

• June 2003

publisher

• Elsevier  Publisher

published in

• Neurocomputing  Journal

keywords

• Bts
• Cortical Networks
• L-system
• Prm
• Rectangle Tree

Digital Object Identifier (DOI)

• 10.1016/S0925-2312(02)00728-2

start page

• 191

end page

• 197

volume

• 52-4

URL

• http://dx.doi.org/10.1016/s0925-2312(02)00728-2