W. Brendel and M. Bethge. Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet. International Conference on Learning Representations (ICLR), May 2019.
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. International Conference on Learning Representations (ICLR), May 2019.
A. S. Ecker, F. H. Sinz, E. Froudarakis, P. G. Fahey, S. A. Cadena, E. Y. Walker, E. Cobos, J. Reimer, A. S. Tolias, and M. Bethge. A rotation-equivariant convolutional neural network model of primary visual cortex. International Conference on Learning Representations (ICLR), May 2019.
L. Schott, J. Rauber, W. Brendel, and M. Bethge. Towards the first adversarially robust neural network model on MNIST. International Conference on Learning Representations (ICLR), May 2019.
J.-H. Jacobsen, J. Behrmann, R. Zemel, and M. Bethge. Excessive Invariance Causes Adversarial Vulnerability. International Conference on Learning Representations (ICLR), 2019.
S. Schneider, A. S. Ecker, J. H. Macke, and M. Bethge. Multi-Task Generalization and Adaptation between Noisy Digit Datasets: An Empirical Study. NeurIPS Continual Learning Workshop, December 2018.
S. Schneider, A. S. Ecker, J. H. Macke, and M. Bethge. Salad: A Toolbox for Semi-supervised Adaptive Learning Across Domains. NeurIPS Machine Learning Open Source Software Workshop, December 2018.
C. Michaelis, I. Ustyuzhaninov, M. Bethge, and A. S. Ecker. One-Shot Instance Segmentation. arXiv, November 2018.
M. Subramaniyan, A. S. Ecker, S. S. Patel, R. J. Cotton, M. Bethge, X. Pitkow, P. Berens, and A. S. Tolias. Faster processing of moving compared with flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion. Journal of Neurophysiology, volume 120, issue 5, pages 2430-2452, November 2018.
M. Kümmerer, T. S. A. Wallis, and M. Bethge. Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics. The European Conference on Computer Vision (ECCV), September 2018.
A. Mathis, P. Mamidanna, K. Cury, T. Abe, V. Murthy, M. Mathis, and M. Bethge. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, volume 21, issue 9, pages 1281-1289, August 2018.
I. Ustyuzhaninov, C. Michaelis, W. Brendel, and M. Bethge. One-shot Texture Segmentation. arXiv, July 2018.
S. A. Cadena, M. A. Weis, L. A. Gatys, M. Bethge, and A. S. Ecker. Diverse feature visualizations reveal invariances in early layers of deep neural networks. The European Conference on Computer Vision (ECCV), July 2018.
A. Böttcher, W. Brendel, B. Englitz, and M. Bethge. Trace your sources in large-scale data: one ring to find them all. arXiv, March 2018.
T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, and M. Bethge. Image content is more important than Bouma's Law for scene metamers. bioRXiv, 2018.
W. Brendel, J. Rauber, A. Kurakin, N. Papernot, B. Veliqi, M. Salath\'e, S. P. Mohanty, and M. Bethge. Adversarial Vision Challenge. 32nd Conference on Neural Information Processing Systems (NIPS 2018) Competition Track, 2018.
R. Geirhos, C. R. M. Temme, J. Rauber, H. H. Schütt, M. Bethge, and F. A. Wichmann. Generalisation in humans and deep neural networks. Advances in Neural Information Processing Systems 31, 2018.
C. Michaelis, M. Bethge, and A. S. Ecker. One-Shot Segmentation in Clutter. ICML, 2018.
W. Brendel, J. Rauber, and M. Bethge. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. International Conference on Learning Representations, 2018.
G. H. Denfield, A. S. Ecker, T. J. Shinn, M. Bethge, and A. S. Tolias. Attentional fluctuations induce shared variability in macaque primary visual cortex. Nature Communications, 2018.
L. A. Gatys, M. Kümmerer, T. S. A. Wallis, and M. Bethge. Guiding human gaze with convolutional neural networks. arXiv, December 2017.
T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, and M. Bethge. A Parametric Texture Model Based on Deep Convolutional Features Closely Matches Texture Appearance for Humans. Journal of Vision, volume 17, issue 12, October 2017.
L. A. Gatys, A. S. Ecker, and M. Bethge. Texture and art with deep neural networks. Current Opinion in Neurobiology, volume 46, pages 178-186, October 2017.
M. Kümmerer, T. S. Wallis, L. A. Gatys, and M. Bethge. Understanding Low- and High-Level Contributions to Fixation Prediction. The IEEE International Conference on Computer Vision (ICCV), October 2017.
D. Klindt, A. S. Ecker, T. Euler, and M. Bethge. Neural system identification for large populations separating “what” and “where”. Advances in Neural Information Processing Systems 31, September 2017.
L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann, and E. Shechtman. Controlling Perceptual Factors in Neural Style Transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 2017.
I. Ustyuzhaninov*, W. Brendel*, L. Gatys, and M. Bethge. What does it take to generate natural textures? International Conference on Learning Representations, April 2017.
S. Cadena, A. Ecker, G. Denfield, E. Walker, A. Tolias, and M. Bethge. A goal-driven deep learning approach for V1 system identification. Computational and Systems Neuroscience Meeting (COSYNE 2017), February 2017.
K. Franke, P. Berens, T. Schubert, M. Bethge, T. Euler, and T. Baden. Inhibition decorrelates visual feature representations in the inner retina. Nature, volume 542, pages 439-444, February 2017.
C. M. Funke, L. A. Gatys, A. S. Ecker, and M. Bethge. Synthesising Dynamic Textures using Convolutional Neural Networks. arXiv, February 2017.
F. A. Wichmann, D. H. Janssen, R. Geirhos, G. Aguilar, H. H. Schütt, M. Maertens, and M. Bethge. Methods and measurements to compare men against machines. Electronic Imaging, Society for Imaging Science and Technology, volume 2017, issue 14, pages 36-45, January 2017.
R. Geirhos, D. Janssen, H. Schütt, M. Bethge, and F. Wichmann. Of human observers and deep neural networks: A detailed psychophysical comparison. Journal of Vision, The Association for Research in Vision and Ophthalmology, volume 17, issue 10, 2017.
S. A. Cadena, G. H. Denfield, E. Y. Walker, L. A. Gatys, A. S. Tolias, M. Bethge, and A. S. Ecker. Deep convolutional models improve predictions of macaque V1 responses to natural images. bioRxiv, Cold Spring Harbor Laboratory, 2017.
R. Geirhos, D. H. J. Janssen, H. H. Schütt, J. Rauber, M. Bethge, and F. A. Wichmann. Comparing deep neural networks against humans: object recognition when the signal gets weaker. arXiv, volume 170606969, 2017.
J. Rauber, W. Brendel, and M. Bethge. Foolbox: A Python toolbox to benchmark the robustness of machine learning models. Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning, 2017.
T. S. A. Wallis, S. Tobias, M. Bethge, and F. A. Wichmann. Detecting Distortions of Peripherally Presented Letter Stimuli under Crowded Conditions. Attention, Perception & Psychophysics, 2017.
S. Cadena, A. Ecker, G. Denfield, E. Walker, A. Tolias, and M. Bethge. A goal-driven deep learning approach for V1 system identification. Bernstein Conference 2016, September 2016.
R. Hosseini, S. Sra, L. Theis, and M. Bethge. Inference and Mixture Modeling with the Elliptical Gamma Distribution. Computational Statistics and Data Analysis, volume 101, pages 29-43, September 2016.
L. A. Gatys, M. Bethge, A. Hertzmann, and E. Shechtman. Preserving Color in Neural Artistic Style Transfer. arXiv, June 2016.
L. A. Gatys, A. S. Ecker, and M. Bethge. Image Style Transfer Using Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2016.
L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. Roman-Roson, T. Baden, T. Euler, A. S. Tolias, and M. Bethge. Benchmarking spike rate inference in population calcium imaging. Neuron, volume 90, issue 3, pages 471-482, May 2016.
T. S. A. Wallis, M. Bethge, and F. A. Wichmann. Testing models of peripheral encoding using metamerism in an oddity paradigm. Journal of Vision, volume 16, issue 2, March 2016.
A. S. Ecker, G. H. Denfield, M. Bethge, and A. S. Tolias. On the Structure of Neuronal Population Activity under Fluctuations in Attentional State. Journal of Neuroscience, volume 36, issue 5, pages 1775-1789, February 2016.
C. R. Cadwell, A. Palasantza, X. Jiang, P. Berens, Q. Deng, J. Reimer, K. Tolias, M. Bethge, R. Sandberg, and A. Tolias. Morphological, electrophysiological and transcriptomic profiling of single neurons using Patch-seq. Nature Biotechnology, volume 34, pages 199-203, January 2016.
T. Baden, P. Berens, K. Franke, M. Rezac, M. Bethge, and T. Euler. The functional diversity of retinal ganglion cells in the mouse. Nature, volume 529, pages 345-350, January 2016.
A. Mathis, D. Rokni, V. Kapoor, M. Bethge, and V. N. Murthy. Reading Out Olfactory Receptors: Feedforward Circuits Detect Odors in Mixtures without Demixing. Neuron, 2016.
L. Theis, A. van den Oord, and M. Bethge. A note on the evaluation of generative models. International Conference on Learning Representations, 2016.
M. Kuemmerer, T. Wallis, and M. Bethge. Information-theoretic model comparison unifies saliency metrics. Proceedings of the National Academy of Science, volume 112, issue 52, pages 16054-16059, October 2015.
L. A. Gatys, A. S. Ecker, and M. Bethge. A Neural Algorithm of Artistic Style. arXiv, August 2015.
L. Theis and M. Bethge. Generative Image Modeling Using Spatial LSTMs. Advances in Neural Information Processing Systems 28, June 2015.
L. A. Gatys, A. S. Ecker, T. Tchumatchenko, and M. Bethge. Synaptic unreliability facilitates information transmission in balanced cortical populations. Physical Review E, volume 91, issue 6, pages 62707, June 2015.
L. A. Gatys, A. S. Ecker, and M. Bethge. Texture Synthesis Using Convolutional Neural Networks. Advances in Neural Information Processing Systems 28, May 2015.
M. Kümmerer, L. Theis, and M. Bethge. Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. ICLR Workshop, May 2015.
S. Sra, R. Hosseini, L. Theis, and M. Bethge. Data modeling with the elliptical gamma distribution. Artificial Intelligence and Statistics, volume 18, 2015.
H. E. Gerhard, L. Theis, and M. Bethge. Modeling Natural Image Statistics. Biologically-inspired Computer Vision—Fundamentals and Applications, Wiley VCH, 2015.
F. Sinz, J.-P. Lies, S. Gerwinn, and M. Bethge. Natter: A Python Natural Image Statistics Toolbox. Journal of Statistical Software, volume 61, issue 5, October 2014.
M. Kümmerer, T. Wallis, and M. Bethge. How close are we to understanding image-based saliency? arXiv, September 2014.
H. E. Gerhard and M. Bethge. Towards rigorous study of artistic style: a new psychophysical paradigm. Art and Perception, volume 2, pages 23-44, August 2014.
E. Froudarakis, P. Berens, A. S. Ecker, R. J. Cotton, F. H. Sinz, D. Yatsenko, P. Saggau, M. Bethge, and A. S. Tolias. Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness. Nature Neuroscience, volume 17, pages 851-857, May 2014.
M. Bethge. Efficient Population Coding. Encyclopedia of Computational Neuroscience, Springer New York, pages 1-9, April 2014.
A. S. Ecker, P. Berens, R. J. Cotton, M. Subramaniyan, G. H. Denfield, C. R. Cadwell, S. M. Smirnakis, M. Bethge, and A. S. Tolias. State dependence of noise correlations in macaque primary visual cortex. Neuron, volume 82, issue 1, pages 235-248, April 2014.
J.-P. Lies, R. M. Häfner, and M. Bethge. Slowness and sparseness have diverging effects on complex cell learning. PLoS Computational Biology, volume 10, issue 3, January 2014.
L. A. Gatys, A. S. Ecker, T. Tchumatchenko, and M. Bethge. Synaptic unreliability facilitates information transmission in balanced cortical populations. Bernstein Conference, 2014.
L. A. Gatys, A. S. Ecker, T. Tchumatchenko, and M. Bethge. How much signal is there in the noise? Computational and Systems Neuroscience, 2014.
A. M. Chagas, L. Theis, B. Sengupta, M. Stüttgen, M. Bethge, and C. Schwarz. Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents. Frontiers in Neural Circuits, volume 7, issue 190, December 2013.
L. Theis, A. M. Chagas, D. Arnstein, C. Schwarz, and M. Bethge. Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification. PLoS Computational Biology, volume 9, issue 11, November 2013.
F. Sinz and M. Bethge. What is the Limit of Redundancy Reduction with Divisive Normalization? Neural Computation, July 2013.
R. M. Haefner, S. Gerwinn, J. H. Macke, and M. Bethge. Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nature Neuroscience, volume 16, pages 235-242, February 2013.
F. Sinz and M. Bethge. Temporal adaptation enhances efficient contrast gain control on natural images. PLoS Computational Biology, volume 9, issue 1, January 2013.
T. Baden, P. Berens, M. Bethge, and T. Euler. Spikes in Mammalian Bipolar Cells Support Temporal Layering of the Inner Retina. Current Biology, volume 23, issue 1, pages 48-52, January 2013.
H. E. Gerhard, F. A. Wichmann, and M. Bethge. How Sensitive Is the Human Visual System to the Local Statistics of Natural Images? PLoS Computational Biology, volume 9, issue 1, January 2013.
L. A. Gatys, A. S. Ecker, T. Tchumatchenko, and M. Bethge. Information Coding in the Variance of Neural Activity. Bernstein Conference, 2013.
L. Theis, D. Arnstein, A. M. Chagas, C. Schwarz, and M. Bethge. Beyond GLMs: a generative mixture modeling approach to neural system identification. Computational and Systems Neuroscience, 2013.
L. Theis, J. Sohl-Dickstein, and M. Bethge. Training sparse natural image models with a fast Gibbs sampler of an extended state space. Advances in Neural Information Processing Systems 25, November 2012.
L. Theis, R. Hosseini, and M. Bethge. Mixtures of conditional Gaussian scale mixtures: the best model for natural images. Frontiers in Computational Neuroscience, September 2012.
L. Theis, D. Arnstein, A. M. Chagas, C. Schwarz, and M. Bethge. Beyond GLMs: a generative mixture modeling approach to neural system identification. Frontiers in Computational Neuroscience, September 2012.
P. Berens, A. S. Ecker, R. J. Cotton, W. J. Ma, M. Bethge, and A. S. Tolias. A fast and simple population code for orientation in primate V1. Journal of Neuroscience, volume 32, issue 31, pages 10618-10626, August 2012.
L. Theis, R. Hosseini, and M. Bethge. Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations. PLoS ONE, Public Library of Science, volume 7, issue 7, July 2012.
T. Putzeys, M. Bethge, F. A. Wichmann, J. Wagemans, and R. Goris. A New Perceptual Bias Reveals Suboptimal Population Decoding of Sensory Responses. PLoS Computational Biolology, Public Library of Science, volume 8, issue 4, April 2012.
L. Theis, S. Gerwinn, F. Sinz, and M. Bethge. In All Likelihood, Deep Belief Is Not Enough. Journal of Machine Learning Research, volume 12, pages 3071-3096, November 2011.
A. S. Ecker, P. Berens, A. S. Tolias, and M. Bethge. The effect of noise correlations in populations of diversely tuned neurons. The Journal of Neuroscience, volume 31, issue 40, pages 14272-14283, September 2011.
J. H. Macke, M. Opper, and M. Bethge. Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity. Physical Review Letters, volume 106, issue 20, May 2011.
P. Berens, A. S. Ecker, S. Gerwinn, A. S. Tolias, and M. Bethge. Reassessing optimal neural population codes with neurometric functions. Proceedings of the National Academy of Sciences of the United States of America, volume 108, issue 11, pages 4423-4428, March 2011.
J. Macke, P. Berens, and M. Bethge. Statistical analysis of multi-cell recordings: linking population coding models to experimental data. Frontiers in Computational Neuroscience, volume 5, issue 35, 2011.
T. Kitching, A. Amara, M. Gill, S. Harmeling, C. Heymans, R. Massey, B. Rowe, T. Schrabback, L. Voigt, S. Balan, G. Bernstein, M. Bethge, S. Bridle, F. Courbin, M. Gentile, A. Heavens, M. Hirsch, R. Hosseini, A. Kiessling, D. Kirk, K. Kuijken, R. Mandelbaum, B. Moghaddam, G. Nurbaeva, S. Paulin-Henriksson, A. Rassat, J. Rhodes, B. Schölkopf, J. Shawe-Taylor, M. Shmakova, A. Taylor, M. Velander, L. van Waerbeke, D. Witherick, and D. Wittman. Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook. The Annals of Applied Statistics, volume 5, issue 3, pages 2231-2263, 2011.
J.-P. Lies, R. M. Häfner, and M. Bethge. Slow Subspace Analysis: a New Algorithm for Invariance Learning. Neurowissenschaftliche Nachwuchskonferenz (NeNa), 2011.
H. E. Gerhard, T. Wiecki, F. Wichmann, and M. Bethge. Perceptual sensitivity to statistical regularities in natural images. The 9th Göttingen Meeting of the German Neuroscience Society, 2011.
S. Gerwinn, J. Macke, and M. Bethge. Reconstructing stimuli from the spike times of leaky integrate and fire neurons. Frontiers in Neuroscience, volume 5, 2011.
F. Sinz and M. Bethge. $L_p$-nested symmetric distributions. Journal of Machine Learning Research, volume 11, pages 3409-3451, December 2010.
R. Hosseini, F. Sinz, and M. Bethge. Lower bounds on the redundancy of natural images. Vision Research, volume 50, issue 22, pages 2213-2222, October 2010.
A. S. Ecker, P. Berens, G. A. Keliris, M. Bethge, N. K. Logothetis, and A. S. Tolias. Decorrelated Neuronal Firing in Cortical Microcircuits. Science, volume 327, issue 5965, pages 584-587, January 2010.
J.-P. Lies, R. Häfner, and M. Bethge. What is the Goal of Complex Cell Coding in V1? AREADNE 2010: Research in Encoding And Decoding of Neural Ensembles, 2010.
J.-P. Lies, R. Häfner, and M. Bethge. What is the Goal of Complex Cell Coding in V1? Frontiers in Computational Neuroscience, 2010.
A. Ecker, P. Berens, G. Keliris, M. Bethge, N. Logothetis, and A. Tolias. Decorrelated Firing in Cortical Microcircuits. AREADNE 2010: Research in Encoding And Decoding of Neural Ensembles, 2010.
A. Ecker, P. Berens, G. Keliris, M. Bethge, N. Logothetis, and A. Tolias. Decorrelated neuronal firing in cortical microcircuits. 40th Annual Meeting of the Society for Neuroscience (Neuroscience 2010), 2010.
R. Hosseini, F. Sinz, and M. Bethge. New Estimate for the Redundancy of Natural Images. Frontiers in Computational Neuroscience, 2010.
L. Theis, S. Gerwinn, F. Sinz, and M. Bethge. Likelihood Estimation in Deep Belief Networks. Frontiers in Computational Neuroscience, 2010.
R. Häfner and M. Bethge. Evaluating neuronal codes for inference using Fisher information. Advances in Neural Information Processing Systems 23, 2010.
S. Bridle, S. T. Balan, M. Bethge, M. Gentile, S. Harmeling, C. Heymans, M. Hirsch, R. Hosseini, M. Jarvis, D. Kirk, T. Kitching, K. Kuijken, A. Lewis, S. Paulin-Henriksson, B. Schölkopf, M. Velander, L. Voigt, D. Witherick, A. Amara, G. Bernstein, F. Courbin, M. Gill, A. Heavens, R. Mandelbaum, R. Massey, B. Moghaddam, A. Rassat, A. Refregier, J. Rhodes, T. Schrabback, J. Shawe-Taylor, M. Shmakova, L. van Waerbeke, and D. Wittman. Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing. Monthly Notices of the Royal Astronomical Society, 2010.
S. Gerwinn, J. Macke, and M. Bethge. Bayesian inference for generalized linear models for spiking neurons. Frontiers in Computational Neuroscience, volume 4, 2010.
J. H. Macke, S. Gerwinn, L. White, M. Kaschube, and M. Bethge. Gaussian process methods for estimating cortical maps. NeuroImage, volume 56, issue 2, pages 570-581, 2010.
R. Hosseini and M. Bethge. Spectral Stacking: Unbiased Shear Estimation for Weak Gravitational Lensing. Max Planck Institute for Biological Cybernetics, October 2009.
S. Gerwinn, J. Macke, and M. Bethge. Bayesian population decoding of spiking neurons. Frontiers in Computational Neuroscience, volume 3, June 2009.
F. H. Sinz, S. Gerwinn, and M. Bethge. Characterization of the p-Generalized Normal Distribution. Journal of Multivariate Analysis, volume 100, issue 5, pages 817-820, May 2009.
J. Eichhorn, F. Sinz, and M. Bethge. Natural Image Coding in V1: How Much Use Is Orientation Selectivity? PLoS Computational Biology, volume 5, issue 4, April 2009.
J. Macke, M. Opper, and M. Bethge. The effect of pairwise neural correlations on global population statistics. Max Planck Institute for Biological Cybernetics, March 2009.
R. Hosseini and M. Bethge. Hierachical Models of Natural Images. Frontiers in Computational Neuroscience, 2009.
J.-P. Lies, J. Wang, J. Sohl-Dickstein, B. Olshausen, and M. Bethge. Unsupervised learning of disparity maps from stereo images. Frontiers in Computational Neuroscience, 2009.
P. Berens, J. Macke, A. Ecker, R. Cotton, M. Bethge, and A. Tolias. Sensory input statistics and network mechanisms in primate primary visual cortex. Frontiers in Systems Neuroscience, 2009.
F. Sinz and M. Bethge. A new class of distributions for natural images generalizing independent subspace analysis. Frontiers in Computational Neuroscience, 2009.
T. Putzeys, R. Goris, J. Wagemans, and M. Bethge. Inferring characteristics of stimulus encoding mechanisms using rippled noise stimuli. Journal of Vision, 2009.
J. Macke, P. Berens, A. Ecker, A. Tolias, and M. Bethge. Generating Spike Trains with Specified Correlation-Coeffcients. Neural Computation, 2009.
F. H. Sinz, E. Simoncelli, and M. Bethge. Hierarchical Modeling of Local Image Features through Lp-Nested Symmetric Distributions. Advances in Neural Information Processing Systems 22, pages 1696-1704, 2009.
J. H. Macke, S. Gerwinn, M. Kaschube, L. E. White, and M. Bethge. Bayesian estimation of orientation preference maps. Advances in Neural Information Processing Systems 22, 2009.
S. Gerwinn, P. Berens, and M. Bethge. A joint maximum-entropy model for binary neural population patterns and continuous signals. Advances in Neural Information Processing Systems 22, 2009.
P. Berens, S. Gerwinn, A. S. Ecker, and M. Bethge. Neurometric function analysis of population codes. Advances in Neural Information Processing Systems 22, 2009.
M. Bethge and R. Hosseini. Method and Device for Image Compression. Europäisches Patentamt, May 2008.
M. Bethge and P. Berens. Near-Maximum Entropy Models for Binary Neural Representations of Natural Images. Advances in Neural Information Processing Systems 20, 2008.
J.-P. Lies and M. Bethge. Image library for unsupervised learning of depth from stereo. Frontiers in Computational Neuroscience, 2008.
P. Berens, A. Ecker, M. Subramaniyan, J. Macke, P. Hauck, M. Bethge, and A. Tolias. Pairwise Correlations and Multineuronal Firing Patterns in the Primary Visual Cortex of the Awake, Behaving Macaque. AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, 2008.
M. Bethge, J. Macke, P. Berens, A. Ecker, and A. Tolias. Flexible Models for Population Spike Trains. AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, 2008.
A. Ecker, P. Berens, A. Hoenselaar, M. Subramaniyan, A. Tolias, and M. Bethge. Towards the neural basis of the flash-lag effect. International Workshop on Aspects of Adaptive Cortex Dynamics, 2008.
F. Sinz and M. Bethge. Redundancy Reduction in Natural Images: Quantifying the Effect of Orientation Selectivity and Contrast Gain Control. Gordon Research Conference: Sensory Coding and The Natural Environment, 2008.
F. Sinz and M. Bethge. The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction in Natural Images. Frontiers in Computational Neuroscience, 2008.
F. Sinz and M. Bethge. The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction. Advances in Neural Information Processing Systems 21, pages 1521-1528, 2008.
J. H. Macke, G. Zeck, and M. Bethge. Receptive Fields without Spike-Triggering. Advances in Neural Information Processing Systems 20, 2008.
S. Gerwinn, J. Macke, M. Seeger, and M. Bethge. Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. Advances in Neural Information Processing Systems 20, 2008.
F. H. Sinz and M. Bethge. How much can orientation selectivity and contrast gain control reduce the redundancies in natural images. Max Planck Institute for Biological Cybernetics, 2008.
M. Bethge. Der kollektiven Signalverarbeitung von Nervenzellen auf der Spur. Max-Planck Jahrbuch, 2008.
M. Seeger, S. Gerwinn, and M. Bethge. Bayesian Inference for Sparse Generalized Linear Models. Lecture Notes in Computer Science, 2007.
A. Ecker, P. Berens, M. Bethge, N. Logothetis, and A. Tolias. Studying the effects of noise correlations on population coding using a sampling method. Neural Coding, Computation and Dynamics (NCCD 07), 2007.
M. Bethge and C. Kayser. Do We Know What the Early Visual System Computes? Proceedings of the 31st Göttingen Neurobiology Conference, 2007.
J. H. Macke, G. Zeck, and M. Bethge. Estimating Population Receptive Fields in Space and Time. Proceedings of the Computational and Systems Neuroscience Meeting 2007 (COSYNE), 2007.
S. Gerwinn, M. Seeger, G. Zeck, and M. Bethge. Bayesian Neural System identification: error bars, receptive fields and neural couplings. Proceedings of the 31st Göttingen Neurobiology Conference, 2007.
M. Bethge, J. H. Macke, S. Gerwinn, and G. Zeck. Identifying temporal population codes in the retina using canonical correlation analysis. Proceedings of the 31st Göttingen Neurobiology Conference, 2007.
A. S. Ecker, P. Berens, M. Bethge, N. K. Logothetis, and A. S. Tolias. Studying the effects of noise correlations on population coding using a sampling method. Neural Coding, Computation and Dynamics, 2007.
P. Berens and M. Bethge. Near-Maximum Entropy Models for Binary Neural Representations of Natural Images. Neural Coding, Computation and Dynamics, 2007.
M. Bethge, S. Gerwinn, and J. H. Macke. Unsupervised learning of a steerable basis for invariant image representations. Proceedings of SPIE Human Vision and Electronic Imaging XII (EI105), 2007.
M. Bethge, T. V. Wiecki, and F. A. Wichmann. The Independent Components of Natural Images are Perceptually Dependent. Proceedings of SPIE Human Vision and Electronic Imaging XII (EI105), 2007.
M. Bethge. Factorial coding of natural images: How effective are linear model in removing higher-order dependencies? Journal of the Optical Society of America A, June 2006.
G. Silberberg, M. Bethge, H. Markram, K. Pawelzik, and M. Tsodyks. Dynamics of population rate codes in ensembles of neocortical neurons. Journal of Neurophysiology, 2004.
M. Bethge. Codes and Goals of Neuronal Representations. University of Bremen, 2003.
M. Bethge, D. Rotermund, and K. Pawelzik. Optimal neural rate coding leads to bimodal firing rate distributions. Network: Computation in Neural Systems, 2003.
M. Bethge, D. Rotermund, and K. Pawelzik. Optimal short-term population coding: when Fisher information fails. Neural Computation, October 2002.
M. Bethge, D. Rotermund, and K. Pawelzik. Binary Tuning is Optimal for Neural Rate Coding with High Temporal Resolution. Advances in Neural Information Processing Systems 15, 2002.
M. Bethge and K. Pawelzik. Population Coding with Unreliable Spikes. Neurocomputing, 2002.
J. Benda, M. Bethge, M. Henning, K. Pawelzik, and A. Herz. Spike-frequency adaptation: Phenomenological model and experimental tests. Neurocomputing, 2001.
M. Bethge and K. Pawelzik. Synchonous inhibition as a mechanism for unbiased selective gain control. Neurocomputing, 2001.
M. Bethge, K. Pawelzik, and T. Geisel. Brief pauses as signals for depressing synapses. Neurocomputing, 1999.