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Clemens, J., Kutzki, O., Ronacher, B., Schreiber, S., and Wohlgemuth, S. (2011). Efficient transformation of an auditory population code in a small sensory system [23]. PNAS, 13812-17.

Clemens, J. (2012). Neural computation in small sensory systems. Lessons on sparse and adaptive coding [24]. Humboldt-Universität zu Berlin


D'Albis, T., Haenicke, J., Strube-Bloss, M.F., Schmuker, M., Menzel, R., and Nawrot, M.P. (2011). Learning-induced changes at the single neuron level predict behavioral performance in the honeybee [25]. Bernstein Conference proceedings [T24]

Daähne, S., Meinecke, F.C., Haufe, S., Höhne, J., Tangermann, M., Müller, K.-R., and Nikulin, V. (2013). SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters [26]. NeuroImage (in press)

Dähne, S., Höhne, J., Schreuder, M., and Tangermann, M. (2011). Slow feature analysis - A tool for extraction of discriminating event-related potentials in brain-computer interfaces [27]. Artificial Neural Networks and Machine Learning ICANN, 36-43.

Dähne, S., Bießmann, F., Meinecke, F.C., Mehnert, J., Fazli, S., and Müller, K.-R. (2013). Integration of multivariate data streams with bandpower signals [28]. IEEE Transactions on Multimedia, 1001-1013.

Dähne, S., Müller, K.-R., and Tangermann, M. (2011). Slow feature analysis as a potential preprocessing tool in BCI [29]. International Journal of Bioelectromagnetism, 100-101.

Dähne, S., Höhne, J., and Tangermann, M. (2011). Adaptive classification improves control performance in ERP-based BCIs [30]. Proceedings of the 5th International BCI Conference 2011, 92-95.

Dähne, S., Höhne, J., and Tangermann, M. (2011). Band power features correlate with performance in auditory brain-computer interface [31]. Frontiers in Human Neuroscience Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI)

Dähne, S., Höhne, J., Haufe, S., Meinecke, F., Tangermann, M., Nikulin, V., and Müller, K.-R. (2012). Multi-variate correlation of power spectral density [32]. In Proceedings of the HBM Conference 2012, Beijing, China, June 2012

Dähne, S., Höhne, J., Haufe, S., Meinecke, F., Tangermann, M., Nikulin, V., and Müller, K.-R. (2012). Optimal spatial filters for correlating band power with cognitive function [33]. BBCI Workshop Berlin, September 2012

Dähne, S., Wilbert, N., and Wiskott, L. (2009). Learning Complex Cell Units from Simulated Prenatal Retinal Waves with Slow Feature Analysis [34]. BMC Neuroscience 2009, P129.

Davison, A., Benda, J., Eglen. S, Harris, K., Jackson, T., Gerhard, S., Gerkin, R., Grewe, J., Mouček, R., Pröpper, R., Sessions, H., Smith, L., Sobolev, A., Sommer, F., Stoewer, A., Teeters, J., and Wachtler, T. (2013). Considerations for developing a standard for storing electrophysiology data in HDF5 [35]. Neuroinformatics (accepted)

Doisy, G., Bodiroža, S., and Jetvić, A. (2013). Spatially unconstrained, gesture-based humanrobot interaction [36]. Proceedings of the 2013 8th ACM/IEEE International Conference on Human-Robot Interaction, 117-118.

Dold, H. (2013). On modelling data from visual psychophysics: A Bayesian graphical model approach [37]. Technische Universität Berlin

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