Cortex in Silico
Construction and Visualization of an Atlas of a Standard Cortex at Cellular Resolution
The goal of this project is to develop tools to model and analyze 3D neural networks of the rat primary somatosensory cortex, also called 'barrel cortex'. This will help neuroscientists to better understand structure-function relationships of sensory cortices at cellular level. Cortices are organized into multiple cortical columns – the smallest anatomical and functional unit of the mammalian cortex. Although single cell reconstructions of functionally characterized neurons are available, so far it has not been possible to reconstruct an average cortical column. This requires consideration of neuron reconstructions and landmark information from many individual brains.
Therefore, a software environment based on interactive and automatic techniques will be implemented which enables researchers to efficiently reconstruct individual neurons and anatomical structures from histological sections. Furthermore, tools will be developed to create anatomically realistic networks of neurons and their synaptic connections representing, e.g., a cortical column or the barrel cortex. Finally, new effective tools will be created for the visual analysis of such models, allowing neuroscientists to better understand cortical anatomy and function. Further information is available in the detailed project description.
Marianne Krabi (03/2010 -12/2012)
Jonas Hörsch (10/2010 - 04/2011)
Maria Gensel (11/2005 - 04/2009)
Max Planck Institute for Biological Cybernetics, Department of Computational Neuroanatomy, Tübingen, Germany (Dr. Marcel Oberlaender)
Max Planck Institute of Neurobiology, Department of Systems and Computational Neurobiology, Martinsried, Germany (Prof. Dr. Bert Sakmann, Prof. Dr. Alexander Borst)
Max Planck Florida Institute, Department of Digital Neuroanatomy, Jupiter, USA (Prof. Dr. Bert Sakmann, Dr. Marcel Oberlaender)
Heidelberg University, Interdisciplinary Center for Scientific Computing, Germany (Prof. Dr. Peter Bastian)
Max Planck Institute of Neurobiology (03/2007-02/2013)
- Vincent J. Dercksen, Robert Egger, Hans-Christian Hege, Marcel Oberlaender. Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis. Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), p. 17-24, Norrköping, Sweden, 2012.
- Hanspeter Pfister, Verena Kaynig, Charl P. Botha, Stefan Bruckner, Vincent J. Dercksen, Hans-Christian Hege, Jos B. T. M. Roerdink. Visualization in Connectomics. Technical report. arXiv:1206.1428, 2012.
- Vincent J. Dercksen, Marcel Oberlaender, Bert Sakmann, Hans-Christian Hege. Interactive Visualization – a Key Prerequisite for Reconstruction of Anatomically Realistic Neural Networks. Visualization in Medicine and Life Sciences II. L. Linsen, H. Hagen, B. Hamann and H.-C. Hege (eds.), p. 27 – 44, 2012.
- Marcel Oberlaender, Christiaan P. J. de Kock, Randy M. Bruno, Alejandro Ramirez, Hanno S. Meyer, Vincent J. Dercksen, Moritz Helmstaedter, Bert Sakmann. Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex. Cerebral Cortex, 22(10):2375-2391, 2012.
- Stefan Lang, Vincent J. Dercksen, Bert Sakmann, Marcel Oberlaender. Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex. Neural Networks, 24(9):998 – 1011, 2011.
- Anja Kuß, Maria Gensel, Björn Meyer, Vincent J. Dercksen, Steffen Prohaska. Effective Techniques to Visualize Filament-Surface Relationships. Computer Graphics Forum, Vol. 29, p. 1003–1012, 2010.
- Vincent J. Dercksen, Britta Weber, David Günther, Marcel Oberlaender, Steffen Prohaska, Hans-Christian Hege. Automatic alignment of stacks of filament data. Proc. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, USA, p. 971–974, 2009.
- Marcel Oberlaender, Vincent J. Dercksen, Robert Egger, Maria Gensel, Bert Sakmann, Hans-Christian Hege. Automated three-dimensional detection and counting of neuron somata. Journal of Neuroscience Methods, 180(1):147–160, 2009.