The goal of the NeuroConnect project is - to generate anatomically realistic 3D neural network models, - to provide tools to analyze such models and - to extract information for numerical simulations of neural activity, particularly the synaptic connectivity. This requires the development of new methods to effectively specify, visualize, and quantify the information of interest in these potentially large (>500k neurons) and complex neural networks, as well as efficient data structures to represent and process this data. Methods to extract the anatomical data underlying the network model and the modeling approach have been developed in the past Cortex In Silico project.

Publications

2020
A Theory for the Emergence of Neocortical Network Architecture BioRxiv, 2020 Daniel Udvary, Philipp Harth, Jakob H. Macke, Hans-Christian Hege, Christiaan P. J. de Kock, Bert Sakmann, Marcel Oberlaender BibTeX
DOI
Modeling Synaptic Connectivity in Anatomically Realistic Neural Networks
2016
The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks Neuron, 92(5), pp. 1106-1121, 2016 Itamar D. Landau, Robert Egger, Vincent J. Dercksen, Marcel Oberlaender, Haim Sompolinsky PDF
BibTeX
DOI
Modeling Synaptic Connectivity in Anatomically Realistic Neural Networks
2015
Visual computing techniques for the reconstruction and analysis of anatomically realistic neural networks Doctoral thesis, Freie Universität Berlin, Christof Schütte, Markus Hadwiger (Advisors), 2015 Vincent J. Dercksen BibTeX
Modeling Synaptic Connectivity in Anatomically Realistic Neural Networks
2014
Generation of dense statistical connectomes from sparse morphological data Frontiers in Neuroanatomy, 8(129), 2014 (preprint available as ZIB-Report 14-43) Robert Egger, Vincent J. Dercksen, Daniel Udvary, Hans-Christian Hege, Marcel Oberlaender PDF (ZIB-Report)
BibTeX
DOI
Modeling Synaptic Connectivity in Anatomically Realistic Neural Networks