Publications

Publications

2021

M. A. Weis, K. Chitta, Y. Sharma, W. Brendel, M. Bethge, A. Geiger, A. S. Ecker
Journal of Machine Learning Research, 2021
@article{Weis2021, title: {Benchmarking Unsupervised Object Representations for Video Sequences}, author: {M. A. Weis, K. Chitta, Y. Sharma, W. Brendel, M. Bethge, A. Geiger, A. S. Ecker}, year: {2021}, journal: {Journal of Machine Learning Research}, }
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K. Lurz, M. Bashiri, K. Willeke, A. Jagadish, E. Wang, E. Y. Walker, S. A. Cadena, T. Muhammad, E. Cobos, A. S. Tolias, A. S. Ecker, F. H. Sinz
International Conference on Learning Representations, 2021
@inproceedings{lurz2021generalization, title: {Generalization in data-driven models of primary visual cortex}, author: {K. Lurz, M. Bashiri, K. Willeke, A. Jagadish, E. Wang, E. Y. Walker, S. A. Cadena, T. Muhammad, E. Cobos, A. S. Tolias, A. S. Ecker, F. H. Sinz}, year: {2021}, booktitle: {International Conference on Learning Representations}, }
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M. Burg, S. Cadena, G. Denfield, E. Walker, A. Tolias, M. Bethge, A. Ecker
PLOS Computational Biology, 2021
show abstract
Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed.
@article{burg_2021_learning_divisive_normalization, title: {Learning divisive normalization in primary visual cortex}, author: {M. Burg, S. Cadena, G. Denfield, E. Walker, A. Tolias, M. Bethge, A. Ecker}, year: {2021}, journal: {PLOS Computational Biology}, }
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T. Lüddecke, A. S. Ecker
arXiv preprint arXiv:2112.10003, 2021
show abstract
Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system that can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text or an image. This approach enables us to create a unified model (trained once) for three common segmentation tasks, which come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation. We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense prediction. After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query. Different variants of the latter image-based prompts are analyzed in detail. This novel hybrid input allows for dynamic adaptation not only to the three segmentation tasks mentioned above, but to any binary segmentation task where a text or image query can be formulated. Finally, we find our system to adapt well to generalized queries involving affordances or properties. Source code: https://eckerlab.org/code/clipseg
@article{luddecke2021prompt, title: {Prompt-Based Multi-Modal Image Segmentation}, author: {T. Lüddecke, A. S. Ecker}, year: {2021}, journal: {arXiv preprint arXiv:2112.10003}, }
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M. A. Weis, L. Pede, T. Lüddecke, A. S. Ecker
arXiv, 2021
show abstract
Understanding the diversity of cell types and their function in the brain is one of the key challenges in neuroscience. The advent of large-scale datasets has given rise to the need of unbiased and quantitative approaches to cell type classification. We present GraphDINO, a purely data-driven approach to learning a low dimensional representation of the 3D morphology of neurons. GraphDINO is a novel graph representation learning method for spatial graphs utilizing self-supervised learning on transformer models. It smoothly interpolates between attention-based global interaction between nodes and classic graph convolutional processing. We show that this method is able to yield morphological cell type clustering that is comparable to manual feature-based classification and shows a good correspondence to expert-labeled cell types in two different species and cortical areas. Our method is applicable beyond neuroscience in settings where samples in a dataset are graphs and graph-level embeddings are desired.
@article{weis2021selfsupervised, title: {Self-supervised Representation Learning of Neuronal Morphologies}, author: {M. A. Weis, L. Pede, T. Lüddecke, A. S. Ecker}, year: {2021}, journal: {arXiv}, }
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D. Kobak, Y. Bernaerts, M. Weis, F. Scala, A. Tolias, P. Berens
Journal of the Royal Statistical Society: Series C (Applied Statistics), 2021
@article{weis2021sparse, title: {Sparse reduced-rank regression for exploratory visualisation of paired multivariate data}, author: {D. Kobak, Y. Bernaerts, M. Weis, F. Scala, A. Tolias, P. Berens}, year: {2021}, journal: {Journal of the Royal Statistical Society: Series C (Applied Statistics)}, }
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2020

V. Benson, A. Ecker
NeurIPS 2020 Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response (AI+HADR 2020), 2020
@inproceedings{benson2020assessing, title: {Assessing out-of-domain generalization for robust building damage detection}, author: {V. Benson, A. Ecker}, year: {2020}, booktitle: {NeurIPS 2020 Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response (AI+HADR 2020)}, }
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T. Lüddecke, A. Ecker
NeurIPS 2020 Workshop on Shared Visual Representations in Human & Machine Intelligence, 2020
@inproceedings{luddeckecnns, title: {CNNs efficiently learn long-range dependencies}, author: {T. Lüddecke, A. Ecker}, year: {2020}, booktitle: {NeurIPS 2020 Workshop on Shared Visual Representations in Human & Machine Intelligence}, }
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T. Lüddecke, F. Wörgötter
Fine-grained action plausibility rating
Robotics and Autonomous Systems (RAS), 2020
@article{lueddecke20, title: {Fine-grained action plausibility rating}, author: {T. Lüddecke, F. Wörgötter}, year: {2020}, journal: {Robotics and Autonomous Systems (RAS)}, }
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M. Rolínek, V. Musil, A. Paulus, M. Vlastelica, C. Michaelis, G. Martius
Computer Vision and Pattern Recognition (CVPR), 2020
@inproceedings{Rolínek2019a, title: {Optimizing Rank-based Metrics with Blackbox Differentiation}, author: {M. Rolínek, V. Musil, A. Paulus, M. Vlastelica, C. Michaelis, G. Martius}, year: {2020}, booktitle: {Computer Vision and Pattern Recognition (CVPR)}, }
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I. Ustyuzhaninov, S. A. Cadena, E. Froudarakis, P. G. Fahey, E. Y. Walker, E. Cobos, J. Reimer, F. H. Sinz, A. S. Tolias, M. Bethge, A. S. Ecker
International Conference on Learning Representations (ICLR), 2020
@inproceedings{Ustyuzhaninov2020a, title: {Rotation-invariant clustering of functional cell types in primary visual cortex}, author: {I. Ustyuzhaninov, S. A. Cadena, E. Froudarakis, P. G. Fahey, E. Y. Walker, E. Cobos, J. Reimer, F. H. Sinz, A. S. Tolias, M. Bethge, A. S. Ecker}, year: {2020}, booktitle: {International Conference on Learning Representations (ICLR)}, }
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Z. Zhao, D. Klindt, A. M. Chagas, K. P. Szatko, L. Rogerson, D. Protti, C. Behrens, D. Dalkara, T. Schubert, M. Bethge, K. Franke, P. Berens, A. S. Ecker, T. Euler
Scientific Reports, 2020
@article{Z*2020a, title: {The temporal structure of the inner retina at a single glance}, author: {Z. Zhao, D. Klindt, A. M. Chagas, K. P. Szatko, L. Rogerson, D. Protti, C. Behrens, D. Dalkara, T. Schubert, M. Bethge, K. Franke, P. Berens, A. S. Ecker, T. Euler}, year: {2020}, journal: {Scientific Reports}, }
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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, M. Bethge
International Conference on Learning Representations (ICLR), 2019
@inproceedings{ecker_2019, title: {A rotation-equivariant convolutional neural network model of primary visual cortex}, author: {A. S. Ecker, F. H. Sinz, E. Froudarakis, P. G. Fahey, S. A. Cadena, E. Y. Walker, E. Cobos, J. Reimer, A. S. Tolias, M. Bethge}, year: {2019}, journal: {International Conference on Learning Representations (ICLR)}, }
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C. Michaelis, B. Mitzkus, R. Geirhos, E. Rusak, O. Bringmann, A. S. Ecker, M. Bethge, W. Brendel
Machine Learning for Autonomous Driving Workshop, NeurIPS 2019, 2019
@inproceedings{michaelis2019dragon, title: {Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming}, author: {C. Michaelis, B. Mitzkus, R. Geirhos, E. Rusak, O. Bringmann, A. S. Ecker, M. Bethge, W. Brendel}, year: {2019}, booktitle: {Machine Learning for Autonomous Driving Workshop, NeurIPS 2019}, }
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T. Lüddecke, T. Kulvicius, F. Wörgötter
Context-based Affordance Segmentation from 2D Images for Robot Action
Robotics and Autonomous Systems (RAS), 2019
@article{lueddecke19a, title: {Context-based Affordance Segmentation from 2D Images for Robot Action}, author: {T. Lüddecke, T. Kulvicius, F. Wörgötter}, year: {2019}, journal: {Robotics and Autonomous Systems (RAS)}, }
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S. A. Cadena, G. H. Denfield, E. Y. Walker, L. A. Gatys, A. S. Tolias, M. Bethge, A. S. Ecker
PLoS Computational Biology, 2019
@article{Cadena2019, title: {Deep convolutional models improve predictions of macaque V1 responses to natural images}, author: {S. A. Cadena, G. H. Denfield, E. Y. Walker, L. A. Gatys, A. S. Tolias, M. Bethge, A. S. Ecker}, year: {2019}, journal: {PLoS Computational Biology}, }
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T. Lüddecke, A. Agostini, M. Fauth, M. Tamosiunaite, F. Wörgötter
Distributional Semantics of Objects in Visual Scenes in Comparison to Text
Artificial Intelligence, 2019
@article{lueddecke19, title: {Distributional Semantics of Objects in Visual Scenes in Comparison to Text}, author: {T. Lüddecke, A. Agostini, M. Fauth, M. Tamosiunaite, F. Wörgötter}, year: {2019}, journal: {Artificial Intelligence}, }
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S. A. Cadena, F. H. Sinz, T. Muhammad, E. Froudarakis, E. Cobos, E. Y. Walker, J. Reimer, M. Bethge, A. Tolias, A. S. Ecker
NeurIPS Neuro AI Workshop, 2019
@inproceedings{Cadena2019b, title: {How well do deep neural networks trained on object recognition characterize the mouse visual system?}, author: {S. A. Cadena, F. H. Sinz, T. Muhammad, E. Froudarakis, E. Cobos, E. Y. Walker, J. Reimer, M. Bethge, A. Tolias, A. S. Ecker}, year: {2019}, journal: {NeurIPS Neuro AI Workshop}, }
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R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, W. Brendel
International Conference on Learning Representations (ICLR), 2019
@inproceedings{Geirhos2019a, title: {ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness}, author: {R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, W. Brendel}, year: {2019}, journal: {International Conference on Learning Representations (ICLR)}, }
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E. Y. Walker, F. H. Sinz, E. Froudarakis, P. G. Fahey, T. Muhammad, A. S. Ecker, E. Cobos, J. Reimer, X. Pitkow, A. S. Tolias
Nature Neuroscience, 2019
@article{Walker2019, title: {Inception loops discover what excites neurons most using deep predictive models}, author: {E. Y. Walker, F. H. Sinz, E. Froudarakis, P. G. Fahey, T. Muhammad, A. S. Ecker, E. Cobos, J. Reimer, X. Pitkow, A. S. Tolias}, year: {2019}, journal: {Nature Neuroscience}, }
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2018

G. H. Denfield, A. S. Ecker, T. J. Shinn, M. Bethge, A. S. Tolias
Nature Communications, 2018
@article{Denfield2018, title: {Attentional fluctuations induce shared variability in macaque primary visual cortex}, author: {G. H. Denfield, A. S. Ecker, T. J. Shinn, M. Bethge, A. S. Tolias}, year: {2018}, journal: {Nature Communications}, }
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S. A. Cadena, M. A. Weis, L. A. Gatys, M. Bethge, A. S. Ecker
The European Conference on Computer Vision (ECCV), 2018
@inproceedings{Cadena2018a, title: {Diverse feature visualizations reveal invariances in early layers of deep neural networks}, author: {S. A. Cadena, M. A. Weis, L. A. Gatys, M. Bethge, A. S. Ecker}, year: {2018}, journal: {The European Conference on Computer Vision (ECCV)}, }
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M. Subramaniyan, A. S. Ecker, S. S. Patel, R. J. Cotton, M. Bethge, X. Pitkow, P. Berens, A. S. Tolias
Journal of Neurophysiology, 2018
@article{Subramaniyan2018a, title: {Faster processing of moving compared with flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion}, author: {M. Subramaniyan, A. S. Ecker, S. S. Patel, R. J. Cotton, M. Bethge, X. Pitkow, P. Berens, A. S. Tolias}, year: {2018}, journal: {Journal of Neurophysiology}, }
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T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, M. Bethge
bioRXiv, 2018
@article{Wallis2018a, title: {Image content is more important than Bouma's Law for scene metamers}, author: {T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, M. Bethge}, year: {2018}, journal: {bioRXiv}, }
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S. Schneider, A. S. Ecker, J. H. Macke, M. Bethge
NeurIPS Continual Learning Workshop, 2018
@inproceedings{schneider2018multitask, title: {Multi-Task Generalization and Adaptation between Noisy Digit Datasets: An Empirical Study}, author: {S. Schneider, A. S. Ecker, J. H. Macke, M. Bethge}, year: {2018}, journal: {NeurIPS Continual Learning Workshop}, }
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C. Michaelis, I. Ustyuzhaninov, M. Bethge, A. S. Ecker
arXiv, 2018
@article{Michaelis2018b, title: {One-Shot Instance Segmentation}, author: {C. Michaelis, I. Ustyuzhaninov, M. Bethge, A. S. Ecker}, year: {2018}, journal: {arXiv}, }
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C. Michaelis, M. Bethge, A. S. Ecker
ICML, 2018
@inproceedings{Michaelis2018a, title: {One-Shot Segmentation in Clutter}, author: {C. Michaelis, M. Bethge, A. S. Ecker}, year: {2018}, booktitle: {ICML}, }
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S. Schneider, A. S. Ecker, J. H. Macke, M. Bethge
NeurIPS Machine Learning Open Source Software Workshop, 2018
@inproceedings{schneider2018salad, title: {Salad: A Toolbox for Semi-supervised Adaptive Learning Across Domains}, author: {S. Schneider, A. S. Ecker, J. H. Macke, M. Bethge}, year: {2018}, journal: {NeurIPS Machine Learning Open Source Software Workshop}, }
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F. H. Sinz, A. S. Ecker, P. G. Fahey, E. Y. Walker, E. Cobos, E. Froudarakis, D. Yatsenko, X. Pitkow, J. Reimer, A. S. Tolias
Advances in Neural Information Processing Systems 32, 2018
@inproceedings{Sinz2018a, title: {Stimulus domain transfer in recurrent models for large scale cortical population prediction on video}, author: {F. H. Sinz, A. S. Ecker, P. G. Fahey, E. Y. Walker, E. Cobos, E. Froudarakis, D. Yatsenko, X. Pitkow, J. Reimer, A. S. Tolias}, year: {2018}, booktitle: {Advances in Neural Information Processing Systems 32}, }
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2017

T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, M. Bethge
Journal of Vision, 2017
@article{wallis_parametric_2017, title: {A Parametric Texture Model Based on Deep Convolutional Features Closely Matches Texture Appearance for Humans}, author: {T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, M. Bethge}, year: {2017}, journal: {Journal of Vision}, }
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L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann, E. Shechtman
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017
@inproceedings{Gatys2017a, title: {Controlling Perceptual Factors in Neural Style Transfer}, author: {L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann, E. Shechtman}, year: {2017}, booktitle: {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, }
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T. Lüddecke, F. Wörgötter
Learning to Segment Affordances
IEEE International Conference on Computer Vision Workshops (ICCVW), 2017
@inproceedings{lueddecke17, title: {Learning to Segment Affordances}, author: {T. Lüddecke, F. Wörgötter}, year: {2017}, booktitle: {IEEE International Conference on Computer Vision Workshops (ICCVW)}, }
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D. Klindt, A. S. Ecker, T. Euler, M. Bethge
Advances in Neural Information Processing Systems 31, 2017
@inproceedings{Klindt*2017a, title: {Neural system identification for large populations separating “what” and “where”}, author: {D. Klindt, A. S. Ecker, T. Euler, M. Bethge}, year: {2017}, booktitle: {Advances in Neural Information Processing Systems 31}, }
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C. M. Funke, L. A. Gatys, A. S. Ecker, M. Bethge
arXiv, 2017
@techreport{Funke2017, title: {Synthesising Dynamic Textures using Convolutional Neural Networks}, author: {C. M. Funke, L. A. Gatys, A. S. Ecker, M. Bethge}, year: {2017}, journal: {arXiv}, }
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L. A. Gatys, A. S. Ecker, M. Bethge
Current Opinion in Neurobiology, 2017
@article{Gatys2017b, title: {Texture and art with deep neural networks}, author: {L. A. Gatys, A. S. Ecker, M. Bethge}, year: {2017}, journal: {Current Opinion in Neurobiology}, }
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2016

L. A. Gatys, A. S. Ecker, M. Bethge
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016
@inproceedings{Gatys2016a, title: {Image Style Transfer Using Convolutional Neural Networks}, author: {L. A. Gatys, A. S. Ecker, M. Bethge}, year: {2016}, booktitle: {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, }
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A. S. Ecker, G. H. Denfield, M. Bethge, A. S. Tolias
Journal of Neuroscience, 2016
@article{Ecker2016, title: {On the Structure of Neuronal Population Activity under Fluctuations in Attentional State}, author: {A. S. Ecker, G. H. Denfield, M. Bethge, A. S. Tolias}, year: {2016}, journal: {Journal of Neuroscience}, }
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2015

L. A. Gatys, A. S. Ecker, M. Bethge
arXiv, 2015
@article{Gatys2015c, title: {A Neural Algorithm of Artistic Style}, author: {L. A. Gatys, A. S. Ecker, M. Bethge}, year: {2015}, journal: {arXiv}, }
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X. Jiang, S. Shen, C. Cadwell, P. Berens, F. Sinz, A. S. Ecker, S. Patel, A. Tolias
Science, 2015
@article{Jiang2015a, title: {Principles of connectivity among morphologically defined cell types in adult neocortex}, author: {X. Jiang, S. Shen, C. Cadwell, P. Berens, F. Sinz, A. S. Ecker, S. Patel, A. Tolias}, year: {2015}, journal: {Science}, }
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L. A. Gatys, A. S. Ecker, T. Tchumatchenko, M. Bethge
Physical Review E, 2015
@article{Gatys2015a, title: {Synaptic unreliability facilitates information transmission in balanced cortical populations}, author: {L. A. Gatys, A. S. Ecker, T. Tchumatchenko, M. Bethge}, year: {2015}, journal: {Physical Review E}, }
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L. A. Gatys, A. S. Ecker, M. Bethge
Advances in Neural Information Processing Systems 28, 2015
@inproceedings{Gatys2015b, title: {Texture Synthesis Using Convolutional Neural Networks}, author: {L. A. Gatys, A. S. Ecker, M. Bethge}, year: {2015}, booktitle: {Advances in Neural Information Processing Systems 28}, }
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2014

A. S. Ecker, A. S. Tolias
Nature Neuroscience, 2014
@article{Ecker2014a, title: {Is there signal in the noise?}, author: {A. S. Ecker, A. S. Tolias}, year: {2014}, journal: {Nature Neuroscience}, }
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E. Froudarakis, P. Berens, A. S. Ecker, R. J. Cotton, F. H. Sinz, D. Yatsenko, P. Saggau, M. Bethge, A. S. Tolias
Nature Neuroscience, 2014
@article{Froudarakis2014a, title: {Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness}, author: {E. Froudarakis, P. Berens, A. S. Ecker, R. J. Cotton, F. H. Sinz, D. Yatsenko, P. Saggau, M. Bethge, A. S. Tolias}, year: {2014}, journal: {Nature Neuroscience}, }
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A. S. Ecker, P. Berens, R. J. Cotton, M. Subramaniyan, G. H. Denfield, C. R. Cadwell, S. M. Smirnakis, M. Bethge, A. S. Tolias
Neuron, 2014
@article{2014a, title: {State dependence of noise correlations in macaque primary visual cortex}, author: {A. S. Ecker, P. Berens, R. J. Cotton, M. Subramaniyan, G. H. Denfield, C. R. Cadwell, S. M. Smirnakis, M. Bethge, A. S. Tolias}, year: {2014}, journal: {Neuron}, }
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2013

M. Subramaniyan, A. S. Ecker, P. Berens, A. S. Tolias
PLoS ONE, 2013
@article{2013a, title: {Macaque monkeys perceive the flash lag illusion}, author: {M. Subramaniyan, A. S. Ecker, P. Berens, A. S. Tolias}, year: {2013}, journal: {PLoS ONE}, }
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2012

P. Berens, A. S. Ecker, R. J. Cotton, W. J. Ma, M. Bethge, A. S. Tolias
Journal of Neuroscience, 2012
@article{Berens2012b, title: {A fast and simple population code for orientation in primate V1}, author: {P. Berens, A. S. Ecker, R. J. Cotton, W. J. Ma, M. Bethge, A. S. Tolias}, year: {2012}, journal: {Journal of Neuroscience}, }
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2011

P. Berens, A. S. Ecker, S. Gerwinn, A. S. Tolias, M. Bethge
Proceedings of the National Academy of Sciences of the United States of America, 2011
@article{Berens2011a, title: {Reassessing optimal neural population codes with neurometric functions}, author: {P. Berens, A. S. Ecker, S. Gerwinn, A. S. Tolias, M. Bethge}, year: {2011}, journal: {Proceedings of the National Academy of Sciences of the United States of America}, }
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A. S. Ecker, P. Berens, A. S. Tolias, M. Bethge
The Journal of Neuroscience, 2011
@article{Ecker2011a, title: {The effect of noise correlations in populations of diversely tuned neurons}, author: {A. S. Ecker, P. Berens, A. S. Tolias, M. Bethge}, year: {2011}, journal: {The Journal of Neuroscience}, }
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Neural Data Science Group
Institute of Computer Science
University of Goettingen