Max Planck Institute for Dynamics and Self-Organization -- Department for Nonlinear Dynamics and Network Dynamics Group
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Publications of Silke Dodel

M. Voultsidou, S. Dodel, and M. Herrmann (2007).
Feature extraction in fMRI data using random matrix theory
Comput. and  Visualiz. in Science 10(2):100-110.

M. Voultsidou, S. Dodel, and J.M. Herrmann (2005).
Neural networks approach to clustering of activity in fMRI Data
IEEE Transactions in Medical Imaging 12(8):987-996.

S. Dodel, J.M. Herrmann, and T. Geisel (2002).
Functional Connectivity by Cross-Correlation Clustering
Neurocomputing 44-46:1065-1070.

S. Dodel, J.M. Herrmann, and T. Geisel (2001).
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing. Lecture Notes in Computer Science 2036
In: . Springer, London, chapter Stimulus-Independent Data Analysis for fMRI, pages 39-53.

S. Dodel, J.M. Herrmann, and T. Geisel (2001).
Temporal Versus Spatial PCA and ICA in Data Analysis for fMRI
In: 28th Göttingen Neurobiology Conference, Göttingen. Thieme Verlag, pages 897.

S. Dodel, M. Herrmann, and T. Geisel (2001).
Is brain activity spatially or temporally correlated?
NeuroImage 13(6):110.

S. Dodel, J.M. Herrmann, and T. Geisel (2000).
Localization of brain activity - blind separation for fMRI data
Neurocomputing 32-33:701-708.

S. Dodel, J.M. Herrmann, and T. Geisel (2000).
Comparison of temporal and spatial ICA in fMRI data analysis
In: ICA 2000 Proceedings, pages 543-547.

S. Dodel, J.M. Herrmann, and T. Geisel (2000).
Stimulus-independent data analysis for fMRI
Unpublished.

S. Dodel, J.M. Herrmann, and T. Geisel (1999).
Components of brain activity - Data analysis for fMRI
In: Proc. ICANN 99, pages 1023-1028.