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Rea, E., Rummel, J., Schmidt, T.T., Hadar, R., Heinz, A., Mathe, A.A., and Winter, C. (2013). Anti-anhedonic e ect 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 [23]. Brain Stimulation


Häusler, C., Susemihl, A.K., and Nawrot, M.P. (2013). Natural image sequences constrain dynamic receptive elds and imply a sparse code [24]. 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 [25]. 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 [26]. BMC Neuroscience 2009, P86.


Droste, F., and Lindner, B. (2013). Analytical results for integrate-and- re neurons driven by dichotomous noise [27]. BMC Neuroscience, P243.


Meckenhäuser, G., Hennig, R.M., and Nawrot, M.P. (2011). Modeling phonotaxis in female Gryllus bimaculatus with arti cial neural networks [28]. BMC Neuroscience, 234.


Onken, A., and Obermayer, K. (2008). Modeling Spike-Count Dependence Structures with Multivariate Poisson Distributions [29]. 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 [30]. 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 [31]. 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 [32]. 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 [33]. Bernstein Conference proceedings [F95]


Helgadottir, L.I., Haenicke, J., Landgraf, T., and Nawrot, M.P. (2012). A robotic platform for spiking neural control architectures [34]. Bernstein Conference proceedings [F128]


Pröpper, R., Munk, M.H.J., and Obermayer, K. (2013). Memory load modulates spiking activity in prefrontal cortex [35]. 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 [36]. Bernstein Conference


Schönfelder, V.H., and Wichmann, F.A. (2008). Machine learning and auditory psychophysics: Unveiling tone-in-noise detection [37]. Berlin Brain Days, Berlin, Germany


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