Analyse neuronaler Daten

Meyer, R. (2016). Correlations and Coding in Primary Visual Cortex. Technische Universität Berlin

Kneer, F. (2016). Cortical Spreading Depression in reaction-diffusion models. Technische Universität Berlin

Ladenbauer, J. (2015). The Collective Dynamics of Adaptive Neurons: Insights from Single Cell and Network Models. Technische Universität Berlin

Pröpper, R. (2015). New tools for electrophysiological data analysis and their application to a working memory study. Technische Universität Berlin

Höhne, J. (2014). Machine learning techniques for neurotechnology with applications for healthy users and patients. Technische Universität Berlin

Kodewitz, A. (2013). Methods for Large Volume Image Analysis. Universite d’Evry

Bock, A. (2013). Removal of the Cardiac Cycle Artefact and Subsequent Coupling Analysis between Cortex and Basal Ganglia. Charité Unviersitätsmedizin Berlin

Dold, H. (2012). On Modeling Data from Visual Psychophysics. Technische Universität Berlin

Stimberg, M. (2011). Computational models of contrast and orientation processing in primary visual cortex. Technische Universität Berlin

Onken, A. (2011). Stochastic Analysis of Neural Spike Count Dependencies. Technische Universität Berlin

Franke, F. (2011). Real-Time Analysis of Extracellular Multielectrode Recordings. Technische Universität Berlin

Schönknecht, S. (2011). Untersuchung von Form und Funktion eines Insektenneurons anhand von Kompartimentmodellen. Technische Universität Berlin

Natora, M. (2011). Adaptive and Blind Array Processing Techniques for Extracellular Electrode Recordings. Technische Universität Berlin

Murfi, H. (2010). Machine Learning for Text Indexing: Concept Extraction, Keyword Extraction and Tag Recommendation. Technische Universität Berlin

Nickisch, H. (2010). Bayesian Inference and Experimental Design for Large Generalised Linear Models. Technische Universität Berlin

Kallerhoff, P. (2009). Processing of expected values in human visual cortex. Technische Universität Berlin

Adiloglu, K. (2009). A Paradigmatic Approach for the Melodic Analysis. Technische Universität Berlin

Grünewälder, S. (2009). Application of Statistical Estimation Theory, Adaptive Sensory Systems and Time Series Processing to Reinforcement Learning. Technische Universität Berlin

Wimmer, K. (2009). Neural Mechanisms and Computational Principles of Adaptive Sensory Processing. Technische Universität Berlin

Mohr, J. (2008). Machine Learning Methods for Life Sciences: Intelligent Data Analysis in Bio- and Chemoinformatics. Technische Universität Berlin