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Welcome

The Neural Data Science group led by Alexander Ecker works at the interface of machine learning and computational neuroscience. We develop new methods and algorithms to make sense of large-scale neuroscience data. Moreover, we work on novel approaches to computer vision based on insights we gain from biological vision.

Currently, the Neural Data Science Group has 4 PhD students and 1 postdocs.

We collaborate very closely with the groups of Matthias Bethge, Fabian Sinz and Thomas Euler at the University of Tübingen, as well as with Andreas Tolias at Baylor College of Medicine, Houston, TX, USA. We are currently establising collaborations at the Göttingen Campus with several groups, including Tim Gollisch, Christoph Kleinn, Eberhard Bodenschatz and Viola Priesemann.

Teaching

Summer term 2020

Introduction to Machine Learning

Alexander Ecker

Winter term 2019/20

Introduction to Deep Learning with a focus on image recognition

Alexander Ecker

Bachelor’s and Master’s theses

General requirements

We expect prospective students to have substantial knowledge in machine learning, its mathematical foundations and Python programming. We therefore recommend that students interested in doing their thesis in our lab should take our courses on Machine Learning and Deep Learning.

Below, we provide a list of topics for theses. If you are interested in one of these topics and fullfill the mentioned requirements, please get in touch with the supervisor by mail.

Thesis offers

Use self-supervised learning as pre-training in specific domains

Supervisor: Timo Lüddecke

Learn a feature extractor for video.

Supervisor: Timo Lüddecke

Apply techniques for novel view synthesis with contrastive self-supervised learning

Supervisor: Timo Lüddecke

Research

M. Rolínek, V. Musil, A. Paulus, M. Vlastelica, C. Michaelis, G. Martius

Optimizing Rank-based Metrics with Blackbox Differentiation

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

Rotation-invariant clustering of functional cell types in primary visual cortex

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

The temporal structure of the inner retina at a single glance

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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M. A. Weis, K. Chitta, Y. Sharma, W. Brendel, M. Bethge, A. Geiger, A. S. Ecker

Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences

arXiv, 2020

@article{Weis2020, title: {Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences}, author: {M. A. Weis, K. Chitta, Y. Sharma, W. Brendel, M. Bethge, A. Geiger, A. S. Ecker}, year: {2020}, journal: {arXiv}, }

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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

A rotation-equivariant convolutional neural network model of primary visual cortex

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

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

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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S. A. Cadena, G. H. Denfield, E. Y. Walker, L. A. Gatys, A. S. Tolias, M. Bethge, A. S. Ecker

Deep convolutional models improve predictions of macaque V1 responses to natural images

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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S. A. Cadena, F. H. Sinz, T. Muhammad, E. Froudarakis, E. Cobos, E. Y. Walker, J. Reimer, M. Bethge, A. Tolias, A. S. Ecker

How well do deep neural networks trained on object recognition characterize the mouse visual system?

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

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

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

Inception loops discover what excites neurons most using deep predictive models

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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M. F. Burg, S. A. Cadena, G. H. Denfield, E. Y. Walker, A. S. Tolias, M. Bethge, A. S. Ecker

Learning Divisive Normalization in Primary Visual Cortex

bioRxiv, 2019

@article{burg2019learning, title: {Learning Divisive Normalization in Primary Visual Cortex}, author: {M. F. Burg, S. A. Cadena, G. H. Denfield, E. Y. Walker, A. S. Tolias, M. Bethge, A. S. Ecker}, year: {2019}, journal: {bioRxiv}, }

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Neural Data Science Group
Institute of Computer Science
University of Goettingen