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Application of a “Staggered Walk” Algorithm for Generating Large-Scale Morphological Neuronal Networks
Author(s) -
Jack Zito,
Heraldo Memelli,
Kyle G. Horn,
Irene C Solomon,
Larry D. Wittie
Publication year - 2012
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2012/876357
Subject(s) - computer science , synapse , orientation (vector space) , projection (relational algebra) , neurite , scale (ratio) , algorithm , artificial neural network , neuroscience , artificial intelligence , biology , mathematics , physics , biochemistry , geometry , quantum mechanics , in vitro
Large-scale models of neuronal structures are needed to explore emergent properties of mammalian brains. Because these models have trillions of synapses, a major problem in their creation is synapse placement. Here we present a novel method for exploiting consistent fiber orientation in a neural tissue to perform a highly efficient modified plane-sweep algorithm, which identifies all regions of 3D overlaps between dendritic and axonal projection fields. The first step in placing synapses in physiological models is neurite-overlap detection, at large scales a computationally intensive task. We have developed an efficient “Staggered Walk” algorithm that can find all 3D overlaps of neurites where trillions of synapses connect billions of neurons.

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