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Electrical Engineering and Computer ScienceSensory Computation in Neural Systems
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Rea, E., Rummel, J., Schmidt, T.T., Hadar, R., Heinz, A., Mathe, A.A., and Winter, C. (2013). Anti-anhedonic eect of deep brain stimulation of the prefrontal cortex and the dopaminergic reward system in a genetic rat model of depression: an intracranial self-stimulation paradigm study. Brain Stimulation
Häusler, C., Susemihl, A.K., and Nawrot, M.P. (2013). Natural image sequences constrain dynamic receptive elds and imply a sparse code. Brain Research
Roemschied, F.A., Ronacher, B., Eberhard, M.J., and Schreiber, S. (2011). Temperature Differentially Affects Subsequent Layers of Auditory Neurons in the Locust. Computational Neuroscience Meeting, Stockholm, Sweden, P287.
Bießmann, F., Gretton, A., Meinecke, F.C., Rainer, G., Müller K.-R., Logothetis, N., and Rauch, A. (2009). Investigating neurovascular coupling using canonical correlation analysis between pharmacological MRI and electrophysiology. BMC Neuroscience 2009, P86.
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 articial 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.
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]
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]
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]
Helgadottir, L.I., Haenicke, J., Landgraf, T., and Nawrot, M.P. (2012). A robotic platform for spiking neural control architectures. Bernstein Conference proceedings [F128]
Pröpper, R., Munk, M.H.J., and Obermayer, K. (2013). Memory load modulates spiking activity in prefrontal cortex. Bernstein Conference 2013
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
Schönfelder, V.H., and Wichmann, F.A. (2008). Machine learning and auditory psychophysics: Unveiling tone-in-noise detection. Berlin Brain Days, Berlin, Germany
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