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




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?. 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. Neuroimage


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]


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


Haeusler, C. (2014). The brain as Data Source - Model - Inspiration. 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.. 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. 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. 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. Brain Research


Häusler, C., and Kampa, B. (2011). Cooperativity of cortical ensembles during natural vision. 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. 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. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012


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