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Hackmack, K. (2010). Predicting Clinical Disability in Multiple Sclerosis Based on Structural MRI [23]. International Conference on Neuroimmunology


Hackmack, K. (2010). Decoding Symptom Severity in Multiple Sclerosis Using Structural MRI Brain Patterns [24]. Human Brain Mapping


Hackmack, K. (2012). Decoding Multiple Sclerosis and Related Disease Parameters Using Structural Brain MRI and Multivariate Analysis Algorithms [25]. BCCN/ Charité Berlin


Hackmack, K., Weygandt, M., Wuerfel, J., Pfueller, C., Bellmann-Strobl, J., Paul, F., and Haynes, J.-D. (2012). can we overcome the clinico-radiological paradox in multiple sclerosis? [26]. Journal of Neurology, 2151-60.


Hackmack, K., Paul, F., Weygandt, M., Allefeld, C., and Haynes, J.-D. (2012). Multi-scale classification of disease using structural MRI and wavelet transform [27]. Neuroimage


Haenicke, J., Pamir, E., and Nawrot, M.P. (2012). A spiking neuronal network model of fast associative learning in the honeybee [28]. Bernstein Conference proceedings [F95]


Haenicke, J., Pamir, E., and Nawrot, M.P. (2013). A computational model of fast associative learning in the honeybee [29]. Conference talk, symposium 13, 10th Meeting of the German Neuroscience Society Gottingen


Haeusler, C. (2014). The brain as Data Source - Model - Inspiration [30]. Freie Universität Berlin


Hahne, J.M., Rehbaum, H., Bießmann, F., Meinecke, F.C., Müller, K.-R., Jiang, N., Farina, D., and Parra, L.C. (2012). Simultaneous and proportional control of 2D wrist movements with myoelectric signals. [31]. 2012 IEEE International Workshop on Machine Learning for Signal Processing


Häusler, C., Nawrot, M. P., and Schmuker, M. (2010). A Neuromorphic Model of Dual Pathway Odour Classification [32]. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience


Häusler, C., Nawrot, M. P., and Schmuker, M. (2011). A spiking neuron classifier network with a deep architecture inspired by the olfactory system of the honeybee [33]. 5th International IEEE EMBS Conference on Neural Engineering 2011, Cancun, Mexico


Häusler, C., Susemihl, A.K., and Nawrot, M.P. (2013). Natural image sequences constrain dynamic receptive elds and imply a sparse code [34]. Brain Research


Häusler, C., and Kampa, B. (2011). Cooperativity of cortical ensembles during natural vision [35]. Invited Talk, Joint Workshop of the German Research Training Groups in Computer Science, June 20th-24th, 2011, Schloss Dagstuhl


Häusler, C., and Susemihl, A. (2012). Temporal autoencoding restricted Boltzmann machines [36]. Deep Learning and Unsupervised Feature Learning Workshop at NIPS


Häusler, C., and Susemihl, A. (2012). Encoding and recall of natural image sequences with conditionally restricted Boltzmann machines [37]. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012


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