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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. Artificial Neural Networks and Machine Learning ICANN, 36-43.


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. BBCI Workshop Berlin, September 2012

Oganian, Y., Conrad, M., Aryani, A., Spalek, K., and Heekeren, H.R. (2012). Model-based variation of sublexical variables predicts language decisions in bilinguals. BCCN Symposium 2012

Schönfelder, V.H., and Wichmann, F.A. (2008). Machine learning and auditory psychophysics: Unveiling tone-in-noise detection. Berlin Brain Days, Berlin, Germany

Schönfelder, V.H., and Wichmann, F.A. (2009). Machine Learning in Auditory Psychophysics: System Identification beyond Regression Analysis. Berlin Brain Days, Berlin, Germany

Roemschied, F.A., Eberhard, M., Schleimer, J., Ronacher, B., and Schreiber, S. (2012). Combining sensitivity analysis with dimensional stacking to identify and visualize functional dependencies in conductance-based neuron model data. Bernstein Conference

Pröpper, R., Munk, M.H.J., and Obermayer, K. (2013). Memory load modulates spiking activity in prefrontal cortex. Bernstein Conference 2013

Helgadottir, L.I., Haenicke, J., Landgraf, T., and Nawrot, M.P. (2012). A robotic platform for spiking neural control architectures. Bernstein Conference proceedings [F128]

Haenicke, J., Pamir, E., and Nawrot, M.P. (2012). A spiking neuronal network model of fast associative learning in the honeybee. Bernstein Conference proceedings [F95]

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. Bernstein Conference proceedings [T24]

Meyer, J., Haenicke, J., Landgraf, T., Schmuker, M., Rojas, R., and Nawrot, M.P. (2011). A digital receptor neuron connecting remote sensor hardware to spiking neural networks. Bernstein Conference proceedings [W84]

Droste, F., and Lindner, B. (2013). Analytical results for integrate-and- re neurons driven by dichotomous noise. BMC Neuroscience, P243.

Meckenhäuser, G., Hennig, R.M., and Nawrot, M.P. (2011). Modeling phonotaxis in female Gryllus bimaculatus with arti cial neural networks. BMC Neuroscience, 234.

Onken, A., and Obermayer, K. (2008). Modeling Spike-Count Dependence Structures with Multivariate Poisson Distributions. BMC Neuroscience. BioMed Central Ltd, P127.

Droste, F., Schwalger, T., and Lindner, B. (2012). Heterogeneous short-term plasticity enables spectral separation of information in the neural spike train. BMC Neuroscience, P98.

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