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Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84931

Egger,  R
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84910

Oberlaender,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Dercksen, V., Egger, R., Hege, H.-C., & Oberlaender, M. (2012). Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis. In Third Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM 2012) (pp. 17-24). Goslar, Germany: Eurographics Association.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B636-F
Abstract
The structural organization of neural circuitry is an important determinant of brain function. Thus, knowing the brain's wiring (the connectome) is key to understanding how it works. For example, understanding how sensory information is translated into behavior requires a comprehensive view of the microcircuits performing this translation at the level of individual neurons and synapses. Obtaining a wiring diagram, however, is nontrivial due to size, complexity and accessibility of the involved brain regions. Even when such data were available, it were difficult to analyze. Here we describe how a network of 0.5 million neurons and their synaptic connections, representing the vibrissal area of the rat primary somatosensory cortex, can be reconstructed. Furthermore, we present a framework for visual exploration of synaptic connectivity between (groups of) neurons within this model. It includes, first, the Cortical Column Connectivity Viewer (CCCV) that provides a hybrid abstract/spatial representation of the connections between neurons of different cell types and/or in different cortical columns. Second, it comprises a 3D view of cell type-specific synapse positions on selected morphologies. This framework is thus an effective tool to visually explore structural organization principles at the population, individual neuron and synapse levels.