Scientific Focus
I am trying to understand how cognitive phenomena can arise from the collective interactions of relatively simple neural elements. In particular, my group investigates how networks of nerve cells give rise to intelligent perception and action by developing computational models of these processes. The long term goal of our research is the development of an embodied computational account of the developing human visual system ― a system that autonomously learns to perceive, understand, and interact with its physical and social environment. By studying the brain’s information processing through computational modeling, we further our understanding of brain function and make progress toward a new generation of intelligent robots with potentially profound social and economic implications. We complement this research by studying visual perception in controlled experiments with human subjects.
Our approach to the computational modeling of brain function has three important thrusts. First, we model the brain mostly at the network and systems levels, because this is where our understanding of the brain still has the most glaring gaps. Our ultimate goal must be to explain observed behavior in terms of neural information processing and it becomes increasingly clear that practically all cognitive phenomena depend on widely distributed networks of neural structures. Second, we study the brain from a developmental perspective. The complexity of the adult brain emerges during its development and it seems likely that understanding the mechanisms that structure the developing brain will be easier than trying to understand the final product. By developing computational models of the developing brain we try to gain insights into how nature and nurture shape our brains, eventually allowing us to make sense of the world around us. Third, the computational models we build are often embodied, i.e., they interact with the real world through sensors and actuators. This has two reasons: a) the developing brain is not only shaped by the self-organization of its elements but also by the bodily interactions with a highly structured environment. We simply cannot understand the brain without taking into account the world in which it develops and learns and how it interacts with this world during the process; b) computational models of brain function should ultimately be able to solve the same problems that real brains have to solve. Only then it is plausible that the brain may indeed work this way.
Methods
· Computational Neuroscience: computational models of nervous system function at different levels of abstraction ranging from single cell to system level models
· Computer Vision: object recognition, object tracking, scene analysis
· Machine Learning: pattern recognition, information theory, reinforcement learning
· Analysis of Human Behavior: eye tracking, virtual reality, haptic force feedback
Selected Publications
Synergies Between Intrinsic and Synaptic Plasticity
Mechanisms. J. Triesch. Neural Computation, vol. 19, p. 885-909, 2007.
Gaze Following: why (not) learn it? J. Triesch, C. Teuscher, G. Deak, and E. Carlson. Developmental Science, vol. 9, no. 2, p. 125-147, 2006.
Task Demands Control Acquisition and Maintenance of Visual Information. Jason A. Droll, Mary M. Hayhoe, J. Triesch, and Brian T. Sullivan. J. Exp. Psychology: Human Perception and Performance, vol. 31, no. 6, p. 1416-1438, 2005.
What you see is what you need. J. Triesch, D.H. Ballard, M.M. Hayhoe, and B.T. Sullivan. Journal of Vision, vol. 3, p. 86-94, 2003.
Democratic Integration: Self-Organized Integration of Adaptive Cues. J. Triesch and C. von der Malsburg. Neural Computation, vol. 13, no. 9, p. 2049-2074, 2001.
A System for Person-Independent Hand Posture Recognition against Complex Backgrounds. J. Triesch and C. von der Malsburg. IEEE PAMI, vol. 23, no. 12, p. 1449-1453, 2001.
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