Direkt zum Inhalt
 
 
Bannergrafik (FB04)
 
  Startseite  
 
Sie sind hier:» Universität » Psychologie » Arbeitseinheit Theoretische Neurowissenschaft
  • Print this page
  • create PDF file

Theoretical Neuroscience Group

dompic2013cropped.jpg
Prof. Dr. Dominik Endres
Head: Jun.Prof. Dr. Dominik Endres
Philipps-University Marburg
Department of Psychology
Section for General and Biological Psychology
Gutenbergstr. 18
35036 Marburg, Germany
Phone: +49-(0)6421-28-23818 dominik.endres[at]uni-marburg.de

Research Overview

Motivation: how do our brains represent the knowledge that both dogs and birds are animals, or that a car is a special type of vehicle with four wheels and an engine? More generally speaking, how do entities come to have meaning? Answering this fundamental cognitive neuroscience question would have several important applications. For example, it might enable us to design assistive technology for patients with certain degenerative diseases, e.g. semantic dementia (visual associative agnosia, Alzheimer's). On the more technical side, if we understood how the brain represents relational information on different levels of the (visual) cortical hierarchy, we would be able to bride the gap between mostly sensory-driven, bottom-up approaches in computer vision and machine learning on the one hand, and semantic-level, logical AI approaches (such as Markov logic or Bayesian logic programs) on the other hand.

Previous work: we used Formal Concept Analysis (FCA), a mathematical framework from the theory of ordered sets, for semantic neural decoding: instead of just trying to figure out which stimulus was presented ('classical' neural decoding), we demonstrated how to explore the semantic relationships (e.g. hierarchies, product-of-expert like coding) in the neural representation of large sets of stimuli, both in neurophysiological data [Endres et al. 2010b,Endres & Födiak 2009], and recently in human fMRI recordings [Endres et al. 2012].

Current research objectives: we are developing computational theories about the the neural representation of semantic relationships in the brain. To test these theories through a combination of behavioral and fMRI experiments, we seek for interested experimental collaborators. We also want to understand how such relationships are learned from data, using natural and synthetic stimuli with controllable structure, which can be generated in the virtual reality lab at the department of Psychology in Marburg. For validation of the results and technical applicability, we will develop a unified machine-learning framework for conceptual learning by fusing probability theory with FCA, in the context of stochastic process learning. This work is a contribution to the deep model structure learning efforts which are currently of great interest to the machine learning community. Our work to date has focused on object semantics, but we are currently extending our approaches towards action semantics building on our previous work on movement primitives [Endres et al. 2013a,Endres et al. 2013b,Velychko et al. 2014]. To this end, we plan to include extensions of FCA for the representation of relations with higher arity (e.g. ternary: actor-action-goal relations) in this framework.

Publications

Book Chapter

 

1
D. Endres, E. Chiovetto, and M.A.. Giese.
Bayesian approaches for learning of primitive-based compact representations of complex human activities.
In: J.P. Laumond and A. Naoko (eds.) Dance notation and robot motion, Springer Tracts in Advanced Robotics, 14 pages, 2015.
DOI:10.1007/978-3-319-25739-6_6

 

Journal Papers

 

2
D. Clever, M. Harant, K.H. Koch, K. Mombaur, and D. Endres.
A novel approach for the generation of complex humanoid walking sequences based on a combination of optimal control and learning of movement primitives.
Robotics and Autonomous Systems, 14 pages, 2016.
DOI: 10.1016/j.robot.2016.06.001

 

3
E.L. Doolittle, B. Gingras, D. Endres, and W.T.S. Fitch.
Overtone-based pitch selection in hermit thrush song: Unexpected convergence with scale construction in human music.
Proceedings of the National Academy of Sciences of the United States of America, 111(46):16616-16621, 2014.
DOI: 10.1073/pnas.1406023111

 

4
D. Endres, E. Chiovetto, and M.A. Giese.
Model selection for the extraction of movement primitives.
Frontiers in Computational Neuroscience, 7:185, 2013.
DOI: 10.3389/fncom.2013.00185.

 

5
D. Endres, Meirovitch Y., Flash T., and M.A. Giese.
Segmenting sign language into motor primitives with Bayesian binning.
Frontiers in Computational Neuroscience, 7:68, 2013.
DOI: 10.3389/fncom.2013.00185.

 

6
D. Endres, A. Christensen, L. Omlor, and M. A. Giese.
Emulating human observers with Bayesian binning: segmentation of action streams.
ACM Transactions on Applied Perception (TAP), 8(3):16:1-12, 2011.
DOI: 10.1145/2010325.2010326.

 

7
D. Endres, P. Földiák, and U. Priss.
An application of Formal Concept Analysis to semantic neural decoding.
Annals of Mathematics and Artificial Intelligence, 57(3-4):233-248, 2010.
DOI: 10.1007/s10472-010-9196-8.

 

8
D. Endres, J. Schindelin, P. Földiák, and M. Oram.
Modelling spike trains and determining latencies with Bayesian binning.
Journal of Physiology (Paris), 104(3-4), 2010.
DOI:10.1016/j.jphysparis.2009.11.015.

 

9
D. Endres and M. Oram.
Feature extraction from spike trains with Bayesian binning: 'latency is where the signal starts'.
Journal of Computational Neuroscience, 29(1-2):149-169, 2010.
DOI:10.1007/s10827-009-0157-3.

 

10
P. Földiák and D. Endres.
Sparse coding.
Scholarpedia, 3(1):2984, 2008.
http://www.scholarpedia.org/article/Sparse_coding.

 

11
D. Endres and P. Földiák.
Exact Bayesian bin classification: a fast alternative to Bayesian classification and its application to neural response analysis.
Journal of Computational Neuroscience, 24(1):24-35, 2008.
DOI: 10.1007/s10827-007-0039-5.

 

12
D. Endres and P. Földiák.
Bayesian bin distribution inference and mutual information.
IEEE Transactions on Information Theory, 51(11):3766-3779, 2005.
DOI: 10.1109/TIT.2005.856954.

 

13
D. Endres and J. Schindelin.
A new metric for probability distributions.
IEEE Transactions on Information Theory, 49(7):1858-1860, 2002.
DOI: 10.1109/TIT.2003.813506.

 

14
D. Endres and P. Riegler.
Learning dynamics on different timescales.
Journal of Physics A: Mathematical and General, 32(49):8655-8663, 1999.

Conference Papers

 

1
A. Yegenoglu, P. Quaglio, E. Torre, S. Grün, and D. Endres.
Exploring the usefulness of formal concept analysis for robust detection of spatio-temporal spike patterns in massively parallel spike trains.
In Proceedings of the International Conference on Conceptual Structures (ICCS) 2016, pages 1-14. LNAI 9717, Springer.

 

2
K.H. Koch, D. Clever, K. Mombauer, and D. Endres.
Learning movement primitives from optimal and dynamically feasible trajectories for humanoid walking.
In Proceedings of the IEEE-RAS International Conference on Humanoid Robotics (Humanoids 2015), pages 866-873, 2015.

 

3
D. Velychko, D. Endres, N. Taubert, and M.A. Giese.
Coupling Gaussian process dynamical models with product-of-experts kernels.
In Proceedings of the 24th International Conference on Artificial Neural Networks, LNCS 8681, pages 603-610. Springer, 2014.
DOI: 10.1007/978-3-319-11179-7_76.

 

4
N. Taubert, M. Löffler, N. Ludolph, A. Christensen, D. Endres, and M.A. Giese.
A virtual reality setup for controllable, stylized real-time interactions between humans and avatars with sparse Gaussian process dynamical models.
Proceedings of the ACM Symposium on Applied Perception, pages 41-44, 2013.
DOI: 10.1145/2492494.2492515.

 

5
D. Endres, R. Adam, M.A. Giese, and U. Noppeney.
Understanding the semantic structure of human fMRI brain recordings with Formal Concept Analysis.
In Proceedings of the 10h International Conference on Formal Concept Analysis (ICFCA 2012), LNAI 7278, pages 96-111. Springer, 2012.

 

6
N. Taubert, A. Christensen, D. Endres, and M.A. Giese.
Online simulation of emotional interactive behaviors with hierarchical gaussian process dynamical models.
In Proceedings of the ACM Symposium on Applied Perception, pages 25-32. ACM, 2012.

 

7
D. Endres, H. Neumann, M. Kolesnik, and M.A. Giese.
Hooligan detection: the effects of saliency and expert knowledge.
In Proceedings of the 4th International Conference for Imaging in Crime Detection and Prevention (ICDP 2011), pages 1-6. IET, ISBN-978-1-84919-565-2, 2011.
BEST PAPER AWARD.

 

8
D. Endres, A. Christensen, L. Omlor, and M. A. Giese.
Segmentation of action streams: human observers vs. Bayesian binning.
In S. Edelkamp and J. Bach, editors, KI 2011, LNAI 7006, pages 75-86. Springer, 2011.
BEST APPLIED PAPER AWARD.

 

9
D. Oberhoff, D. Endres, M.A. Giese, and M. Kolesnik.
Gates for handling occlusion in Bayesian models of images.
In S. Edelkamp and J. Bach, editors, KI2011, LNAI 7006, pages 228-232. Springer, 2011.

 

10
N. Taubert, D. Endres, A. Christensen, and M.A. Giese.
Shaking hands in latent space: modeling emotional interactions with Gaussian process latent variable models.
In S. Edelkamp and J. Bach, editors, KI2011, LNAI 7006, pages 330-334. Springer, 2011.

 

11
D. Endres and P. Földiák.
Interpreting the neural code with Formal Concept Analysis.
In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 425-432. 2009.
http://books.nips.cc/nips21.html.

 

12
D. Endres, P. Földiák, and U. Priss.
An application of Formal Concept Analysis to neural decoding.
In Proceedings of the Sixth International Conference on Concept Lattices and Their Applications (CLA2008), Olomouc, Czech Republic, 2009.
http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-433/.

 

13
D. Endres, M. Oram, J. Schindelin, and P. Földiák.
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms.
In J.C. Platt, D. Koller, Y Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 393-400. 2008.
http://books.nips.cc/nips20.html.

 

14
D. Endres and P. Földiák.
Quadratic programming for learning sparse codes.
In Proceedings of the ninth international conference on artificial neural networks (ICANN99), IEE, volume 2, pages 593-596. IEE Conference Publication No. 470. London: Institution of Electrical Engineers, 1999.

Abstracts

 

1
L. Fedorov, D. Endres, J. Vangeneuden, and M.A. Giese.
Neurodynamical model for the multi-stable perception of biological motion.
Journal of Vision, 14(10):1007, 2014.

 

2
E. Chiovetto, D. Endres, A. d’Avella, and M.A. Giese.
Model selection for the extraction of EMG synergies.
Presented at the annual meeting of the Neural Control of Movement Society, NCM. Amsterdam, The Netherlands, 2014.

 

3
E. Chiovetto, D. Endres, C. Curio, and M.A. Giese.
Perceptual integration of kinematic components for the recognition of emotional facial expressions.
Journal of Vision, 14(10):205, 2014.

 

4
T. Beck, D. Endres, A. Lindner, and M.A. Giese.
Active Sampling supported Comparison of Causal Inference Models for Agency Attribution in Goal-Directed Actions.
Journal of Vision, 14(10):838, 2014.

 

5
D.M. Endres, A. Smilgin, Z. Sun, M. Junker, M. Prsa, P.W. Dicke, M.A. Giese, and P. Thier.
Forces matter for relating spikes to saccade parameters in motoneurons and the oculomotor cerebellum.
Presented at the Bernstein Conference, Göttingen, Germany, 2014.
http://dx.doi.org/10.12751/nncn.bc2014.0184doi: 10.12751/nncn.bc2014.0184.

 

6
T.F. Beck, D. Endres, A. Lindner, and M.A. Giese.
Agency attribution in goal-directed actions: Active sampling improving bayesian model comparison.
Presented at the Bernstein Conference, Göttingen, Germany, 2014.
http://dx.doi.org/10.12751/nncn.bc2014.0149doi: 10.12751/nncn.bc2014.0149.

 

7
D. Endres and M.A. Giese.
Testing the order-theoretic similarity model and making perceived similarity explicit with Formal Concept Analysis.
Presented at the 36th European Conference on Visual Perception, Bremen, Germany, 2013.

 

8
D. Endres, R. Adam, U. Noppeney, and M.A. Giese.
Connecting brain and mind with formal concept analysis: a data-driven semantic investigation of the explicit coding hypothesis.
Presented at the 10th German Neurobiology Meeting, Göttingen, Germany, 2013.

 

9
D. Endres, A. Smilgin, P.W. Dicke, M.A. Giese, and P. Thier.
Simple spikes of Purkinje cells: pre-dictive, post-dictive or both?
Presented at the Bernstein Conference, Tübingen, Germany, 2013.
https://portal.g-node.org/abstracts/bc13/#/doi/nncn.bc2013.0221doi: 10.12751/nncn.bc2013.0221.

 

10
C.A. Merritt, D. Endres, A.-K. Weiser, H.-O. Karnath, and M.A. Giese.
Detecting errors of human action semantics using Markov logic networks as tool to quantify behavioral deficits in apraxia.
Presented at the Bernstein Conference, Tübingen, Germany, 2013.
https://portal.g-node.org/abstracts/bc13/#/doi/nncn.bc2013.0080doi: 10.12751/nncn.bc2013.0080.

 

11
L. Fedorov, D. Endres, J. Vangeneuden, and M.A. Giese.
Neurodynamical model for the multi-stable perception of biological motion.
Presented at the Bernstein Conference, Tübingen, Germany, 2013.
https://portal.g-node.org/abstracts/bc13/#/doi/nncn.bc2013.0079doi: 10.12751/nncn.bc2013.0079.

 

12
T.F. Beck, C. Wilke, B. Wirxel, D. Endres, A. Lindner, and M.A. Giese.
Me - not me or in between? comparison of causal inference models for agency attribution in goal-directed actions.
Presented at the Bernstein Conference, Tübingen, Germany, 2013.
https://portal.g-node.org/abstracts/bc13/#/doi/nncn.bc2013.0078doi: 10.12751/nncn.bc2013.0078.

 

13
D. Endres, R. Adam, U. Noppeney, and M.A. Giese.
Explicit coding in the brain: data-driven semantic analysis of human fmri bold responses with formal concept analysis.
Presented at Bernstein Conference, Munich, Germany, 2012.

 

14
T.F. Beck, C. Wilke, B. Wirxel, D. Endres, A. Lindner, and M.A. Giese.
Did i do it? causal inference of authorship in goal-directed actions.
In Perception ECVP suppl., volume 40, page 227, 2011.

 

15
N. Taubert, A. Christensen, D. Endres, and M.A. Giese.
Perception of synthetically generated interactive human emotional body expressions.
In Perception ECVP suppl., volume 41, page 101, 2012.

 

16
T.F. Beck, C. Wilke, B. Wirxel, D. Endres, A. Lindner, and M.A. Giese.
Did i do it? causal inference of authorship in goal-directed actions for impoverished stimuli.
In Perception ECVP suppl., volume 41, page 255, 2012.

 

17
V. Zwanger, D. Endres, and M.A. Giese.
Hooligan detection: the effects of spatial and temporal expert knowledge.
In Perception ECVP suppl., volume 40, page 207, 2011.

 

18
T.F. Beck, C. Wilke, B. Wirxel, D. Endres, A. Lindner, and M.A. Giese.
Did i do it? causal inference of agency in goal-directed actions.
In Perception ECVP suppl., volume 40, page 227, 2011.

 

19
D. Endres and M. Oram.
Modeling non-stationarity and inter-spike dependency in high-level visual cortical area stsa.
Presented at the 9th Göttingen meeting of the German neuroscience society, 2011.

 

20
T.F. Beck, C. Wilke, B. Wirxel, D. Endres, A. Lindner, and M.A. Giese.
A Bayesian graphical model for the influence of agency attribution on perception and control of self-action.
Presented at the 9th Göttingen meeting of the German neuroscience society, 2011.

 

21
T.F. Beck, C. Wilke, B. Wirxel, D. Endres, A. Lindner, and M.A. Giese.
It was (not) me: Causal inference of agency in goal-directed actions.
In Nature preceedings. Presented at COSYNE, 2011.
http://dx.doi.org/10.1038/npre.2011.5858.2DOI: 10.1038/npre.2011.5858.2.

 

22
D. Endres, M. Höffken, F. Vintila, N. Bruce, J. Bouecke, P. Kornprobst, H. Neumann, and M. Giese.
Hooligan detection: the effects of saliency and expert knowledge.
In Perception ECVP suppl., volume 39, page 193, 2010.

 

23
D. Endres, C. Christensen, C. Beck, J. Bouecke, L. Omlor, H. Neumann, and M. Giese.
Segmentation of action streams: comparison between human and statistically optimal performance.
Presented at the Vision Sciences Society 10th Annual Meeting, Naples, FL, USA, 2010.

 

24
D. Endres and P. Földiák.
Interpreting the neural code with formal concept analysis.
In Perception ECVP suppl., volume 38, page 127, 2009.

 

25
D. Endres and P. Földiák.
Interpreting the neural code with formal concept analysis.
Presented at the Neural Information Processing Systems (NIPS) conference, Vancouver, British Columbia, Canada, 2008.

 

26
D. Endres, M. Oram, J. Schindelin, and P. Földiák.
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms.
Presented at the Neural Information Processing Systems (NIPS) conference, Vancouver, British Columbia, Canada, 2007.

 

27
M. Oram, D. Xiao, and D. Endres.
Stimulus induced decorrelation of neuronal activity in the visual system.
In Perception ECVP Abstract Supplement, volume 36, 2007.

 

28
D. Endres and P. Földiák.
Rapid presentation is efficient for testing visual neurons (area STSa): information rate peaks in the interval [9,24] stimuli/s.
Presented at the Computational Neuronscience Meeting (CNS), Toronto, Ontario, Canada, 2007.

 

29
D. Endres and P. Földiák.
Exact Bayesian binning and its application to neural response analysis.
Presented at the Computational and Systems Neuroscience meeting (COSYNE), Salt Lake City, Utah, USA, 2007.

Zuletzt aktualisiert: 27.06.2016 · Tobias Kästner

 
 
 
Fb. 04 - Psychologie

Fb. 04 - Psychologie, Gutenbergstraße 18, 35032 Marburg
Tel. +49 6421/28-23675, Fax +49 6421/28-26949, E-Mail: dekanpsy@staff.uni-marburg.de

URL dieser Seite: http://www.uni-marburg.de/fb04/team-endres

Impressum | Datenschutz