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Neural Data Science
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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 5 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

Winter term 2020/21

Lecture Deep Learning

Introduction to Deep Learning with a focus on image recognition

Alexander Ecker

Summer term 2020

Lecture Machine Learning

Introduction to Machine Learning

Alexander Ecker

Winter term 2019/20

Lecture Deep Learning

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 strongly recommend that students interested in doing their thesis in our lab should take our courses on Machine Learning, Deep Learning and took the Fachpraktikum Data Science. Exceptions are possible if well motivated.

Further recommended lectures are:

Please note, our thesis supervision capacity is limited and we receive more thesis inquiries than we are able supervise. Therefore, we have to select candidates. If you are interested, please write an eMail with the subject “master thesis” or “bachelor thesis” containing one to three sentences about what you would like to work on and your study record to Alexander Ecker.

We will get back to you within a few days. Otherwise, do not hesitate to remind us :).

Thesis offers

Domain-specific self-supervised learning
Use self-supervised learning as pre-training in specific domains
Supervisor: Timo Lüddecke
Object-centric representation learning
Building a unified framework for object-centric representation learning models.
Supervisor: Marissa Weis
Self-supervised learning on video
Learn a feature extractor for video.
Supervisor: Timo Lüddecke
Self-supervised learning using view synthesis
Apply techniques for novel view synthesis with contrastive self-supervised learning
Supervisor: Timo Lüddecke

Research

2020

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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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
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