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