Detail Page
Prof. Dr. sc.nat. Jörg Conradt
Contact Information
E-mail: 
Phone: +49-89-289-26925
Room: CCRL-II, 3021
Location: Karlstr. 45, 80333 München
 
Other Affiliation
 
Short Biography

as of Oct. 2009   Assistant-Professor for Neuroscientific System Theory, CoTeSys, TU München, Germany

May 2008   Ph.D. (Dr. sc.nat.) in Neuroinformatics, ETH Zürich, Switzerland.

Feb. 2001   Diploma in Computer Engineering, TU-Berlin, Germany.

May 2000   Master of Science in Computer Science with specialization in ‘Robotics and Automation’, University of Southern California, USA.

 

Research Interests

Current research projects: Neuroscientific System Theory

How does thinking work? How do we interpret what we see, hear, smell, and touch? – and how do we decide what we do and how we do it in the world around us? This – I believe – is one of today's greatest mysteries in science.

Looking at small animals with tiny brains, we get the impression that they act effortlessly in the world, foraging for food and returning home safely. In contrast, today's carefully hand-designed computers and robots with all available sensors and processing power are hardly able to successfully perform such simple behaviors. The world is too complex and too ambiguous to get interpreted reliably with contemporary algorithms. So in which fundamental principles does information processing in brains differ from information processing performed by current computing algorithms?

Probably the most fundamental difference is already established by the design of the elementary unit that performs computation: today’s engineered systems typically rely on relatively few but powerful and cautiously hand-designed processing cores (CPUs) – even high-end machines typically have no more than four CPUs in a system. Brains, in contrast, are composed of a large number of relatively simple processing units (neurons) – ranging in count from a few hundred in the simplest worms up to several 1011 neurons in a mammalian brain. Each such neuron operates with relatively low speed, but all of them work in parallel, forming a large, self-grown, recurrently interconnected network of “computing machines”, each contributing to the overall task. No neuron – and no group of neurons – has access to global information, as CPUs do in our computers.

This difference in computing hardware imposes severe constraints for computing algorithms, that today to a large extent are completely unaddressed. How can a distributed system with only local knowledge perform globally consistent actions? How does such a system build itself – starting from a nucleus – with only local knowledge and no global supervisor? Why is such a large network of neurons relatively insensitive to changes in the connectivity pattern and to defective computing units? How does such a deep network learn?

In my research I am addressing such questions by applying neuronal-style algorithmic primitives to artificial engineered systems that interact intelligently with the real world – thereby working towards understanding how brains perform computation, and ultimately gaining insight in why such systems outperform contemporary algorithms.
Working Fields

Neuronal-Style Information Processing in Closed-Control-Loop Systems

  •   Distributed Local Information Processing
  •   Growing and Adaptive Networks of Computational Units
  •   Neuromorphic Sensor Fusion and Distributed Actuator Networks
  •   Event-Based Perception, Cognition, and Action

 

Teaching

SS 2010:

Computational Intelligence (new)

WS 2010/11:


Computational Intelligence
Projektpraktikum "Neuro-Robotics" (new)
Hauptseminar "Kognitive Robotik und Regelung" (new)

SS 2011:


Neuromorphic Engineering for Cognitive Systems (new)
Projektpraktikum "Neuro-Robotics"
Hauptseminar "Kognitive Robotik und Regelung"

WS 2011/12:


Computational Intelligence
Projektpraktikum "Neuro-Robotics"
Hauptseminar "Kognitive Robotik und Regelung"

 

Publications
Journal Articles
1. W. Einhäuser, GU. Moeller, F. Schumann, J. Conradt, J. Vockeroth, K. Bartl, E. Schneider, P. König , Eye-head coordination during free exploration in human and cat , Annals of the New York Academy of Sciences , 1164 (2009) , 353-366 .
2. JA. Birdwell, JH. Solomon, M. Thajchayapong, MA. Taylor, M. Cheely, RB. Towal, J. Conradt, MJZ. Hartmann , Biomechanical Models for Radial Distance Determination by the Rat Vibrissal System , J. Neurophysiology , 98 (2007) , no. 4 , 2439-55 .
3. J. Hipp, E. Arabzadeh, E. Zorzin, J. Conradt, C. Kayser, ME. Diamond, P. König , Texture signals in whisker vibrations , J. Neurophysiology , 95 (2006) , no. 3 , 1792-9 .
4. J. Conradt , Helping Neuromorphic sensors leave the designer's desk , The Neuromorphic Engineer , 2 (2005) , no. 1 , 8-9 .
5. J. Hipp, W. Einhäuser, J. Conradt, P. König , Learning of somatosensory representations for texture discrimination using a temporal coherence principle , Network: Computation in Neural Systems , 16 (2005) , no. 2-3 , 223-38 .
6. S. Vijayakumar, A. D'Souza, T. Shibata, J. Conradt, S. Schaal , Statistical Learning for Humanoid Robots , Autonomous Robotics , 12 (2002) , no. 1 , 55-69 .
7. T. Shibata, S. Vijayakumar, J. Conradt, S. Schaal , Biomimetic Oculomotor Control , Adaptive Behaviour - Special Issue on Biologically Inspired and Biomimetic Systems , 9 (2001) , no. 3-4 , 189-208 .
Conferences
8. GR. Müller, T. Villgrattner, J. Conradt , Schnell sehen, erkennen und reagieren - Technik nach Vorbild des menschlichen Auges , 2011 .
9. J. Conradt, RJ. Douglas , A Distributed Cognitive Map for Spatial Navigation , 2011 .
10. J. Conradt, R. Berner, P. Lichtsteiner, RJ. Douglas, T. Delbruck, M. Cook , Balancing Pencils using Spike-Based Vision Sensors , 2009 , Best Live Demonstration Award .
11. J. Conradt, P. Lichtsteiner, R. Berner, T. Delbruck, RJ. Douglas, M. Cook , High Speed Pole Balancing with Only Spike-based Visual Input , 2008 .
12. J. Conradt, P. Varshavskaya, K. Preuschoff, RJ. Douglas , A CPG-driven Autonomous Robot , 2003 .
13. S. Bermudez Badia, P. Pyk, J. Conradt, PFMJ. Verschure , Artificial Moth (Insect-based Neuronal Models of Course Stabilization and Obstacle Avoidance applied to a Flying Robot) , 2003 .
Peer Reviewed Conference Papers
14. GR. Müller, J. Conradt , A Miniature Low-Power Sensor System for Real Time 2D Visual Tracking of LED Markers , Proceedings of the IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO), Phuket, Thailand , 2011 .
15. R. Berner, C. Braendli, M. Yang, S.C. Liu, J. Conradt, M. Cook, M. Pfeiffer, T. Delbruck , Event-Based Vision with Dynamic Vision Sensors , IEEE Swiss Image and Vision Sensors Workshop 2011 (SIVS 2011), Zurich, Switzerland , 2011 .
16. J. Conradt, R. Berner, M. Cook, T. Delbruck , An Embedded AER Dynamic Vision Sensor for Low-Latency Pole Balancing , Proceedings of the IEEE Workshop on Embedded Computer Vision (ECV09), Kyoto, Japan. , 2009 .
17. J. Conradt, M. Cook, R. Berner, P. Lichtsteiner, RJ. Douglas, T. Delbruck , A Pencil Balancing Robot using a Pair of AER Dynamic Vision Sensors , International Conference on Circuits and Systems (ISCAS), Taipei, Taiwan , 2009 , p. 781-785 .
18. J. Conradt, P. Varshavskaya , Distributed Central Pattern Generator Control for a Serpentine Robot , Proceedings of the Joint International Conference on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP), Istanbul, Turkey , 2003 , p. 338-341 .
19. J. Conradt, M. Pescatore, S. Pascal, PFMJ Verschure , Saliency Maps Operating on Stereo Images Detect Landmarks and their Distance , Proceedings of the International Conference on Artificial Neural Networks (ICANN), Madrid, Spain , 2002 , p. 795-800 .
20. C. Planta, J. Conradt, A. Jencik, PFMJ Verschure , A Neural Model of the Fly Visual System Applied to Navigational Tasks , International Conference on Artificial Neural Networks (ICANN), Madrid, Spain , 2002 , p. 1268-1274 .
21. T. Shibata, S. Vijayakumar, J. Conradt, S. Schaal , Humanoid Oculomotor Control Based on Concepts of Computational Neuroscience , Proceedings of Humanoids 2001, Second IEEE-RAS Intl. Conf. on Humanoid Robots, Waseda Univ., Japan , 2001 .
22. S. Vijayakumar, J. Conradt, T. Shibata, S. Schaal , Overt Visual Attention for a Humanoid Robot , International Conference on Intelligent Robots and Systems (IROS 2001). Maui, Hawaii , 2001 .
23. J. Conradt, G. Tevatia, S. Vijayakumar, S. Schaal , On-line Learning for Humanoid Robot Systems , Proceedings of the 17th International Conference on Machine Learning (ICML2000), Stanford, CA, USA , 2000 , p. 191-198 .
Master-/Diploma Thesis
24. J. Conradt , Online-Learning in Humanoid Robots , 2001 , Erwin-Stephan Price for outstanding Masters Degree .
PhD thesis
25. J. Conradt , A Distributed Cognitive Map for Spatial Navigation Based on Graphically Organized Place Agents , 2008 , ETH Medal for an outstanding Thesis .