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Schmuker, M., Häusler, C., Nawrot, M.P. (). The honeybee olfactory system as a template for better neuromorphic classifiers. 9th Göttingen Meeting of the German Neuroscience Society, March 23-27



Schönfelder, V., and Wichmann, F.A. (2012). Sparse regularized regression identi es behaviorally-relevant stimulus features from psychophysical data. Journal of the Acoustical Society of America, 3953-3969.


Schönfelder, V.H., and Wichmann, F.A. (2013). Identi cation of stimulus cues in narrow-band tone-in-noise detection using sparse observer models. Journal of the Acoustical Society of America, 447-463.


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. (2011). Extracting auditory cues in tone-in-noise detection with a sparse feature selection algorithm. Front. Comput. Neurosci.


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


Schönfelder, V.H., Fründ, I., and Wichmann, F.A. (2011). Peering into the black box: Using sparse feature selection to identify critical stimulus properties in audition. Berlin Brain Days, Berlin, Talk


Schönfelder, V. (2008). Machine Learning and Psychophysics: Unveiling Tone-in-Noise Detection. PhD Symposium at BCCN Conference, Munich, Germany


Schönfelder, V.H. and Wichmann, F.A. (2010). Machine Learning in Auditory Psychophysics: System Identification with Sparse Pattern Classifiers. Proceedings of KogWis – 10th Meeting of the German Society for Cognitive Science, Potsdam, 2010


Schönfelder, V.H., and Wichmann, F.A. (2012). Sparse regularized regression identifies behaviorally-relevant stimulus features from psychophysical data. The Journal of the Acoustical Society of America, 3953-3969.


Schönfelder, V., and Wichmann, F. (2013). Identification of stimulus cues in narrow-band tone-in-noise detection using sparse observer models. Journal of the Acoustic Society of America, 447-463.


Schreuder, M., Riccio, A., Risetti, M., Dähne, S., Ramsey, A., Williamson, J., Mattia, D., and Tangermann, M. (2013). User-centered design in BCI - a case study. Artificial Intelligence in Medicine (accepted)


Schultze-Kraft, R., Görgen, K., Wenzel, M., Haynes, J.-D., and Blankertz, B. (2013). Cooperating brains: Joint control of a dual-BCI. Peer-reviewed Conference Proceedings of the Joint Action Meeting JAM 2013, Berlin, Germany


Seymour, K., Stein, T., Sanders, L.L.O., Guggenmos, M., Theophil, I., and Sterzer, P. (2013). Altered contextual modulation of primary visual cortex responses in schizophrenia. Neuropsychopharmacology, 1-6.


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