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Interactive Visualization: A Key Prerequisite for Reconstruction and Analysis of Anatomically Realistic Neural Networks


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

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Dercksen, V., Oberlaender, M., Sakmann, B., & Hege, H.-C. (2012). Interactive Visualization: A Key Prerequisite for Reconstruction and Analysis of Anatomically Realistic Neural Networks. In Visualization in Medicine and Life Sciences II: Progress and New Challenges (pp. 27-44). Berlin, Germany: Springer.

Recent progress in large-volume microscopy, tissue-staining, as well as in image processing methods and 3D anatomy reconstruction allow neuroscientists to extract previously inaccessible anatomical data with high precision. For instance, determination of neuron numbers, 3D distributions and 3D axonal and dendritic branching patterns support recently started efforts to reconstruct anatomically realistic network models of many thousand neurons. Such models aid in understanding neural network structure, and, by numerically simulating electro-physiological signaling, also to reveal their function. We illustrate the impact of visual computing on neurobiology at the example of important steps that are required for the reconstruction of large neural networks. In our case, the network to be reconstructed represents a single cortical column in the rat brain, which processes sensory information from its associated facial whisker hair. We demonstrate how analysis and reconstruction tasks, such as neuron somata counting and tracing of neuronal branches, have been incrementally accelerated – finally leading to efficiency gains of orders of magnitude. We also show how steps that are difficult to automatize can now be solved interactively with visual support. Additionally, we illustrate how visualization techniques have aided computer scientists during algorithm development. Finally, we present visual analysis techniques allowing neuroscientists to explore morphology and function of 3D neural networks. Altogether, we demonstrate that visual computing techniques make an essential difference in terms of scientific output, both qualitatively, i.e., whether particular goals can be achieved at all, and quantitatively in terms of higher accuracy, faster work-flow and larger scale processing. Such techniques have therefore become essential in the daily work of neuroscientists.