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Research interests
Prof. Claudius Gros


Self-Organized Robotics

Robots are normally hard controlled with the aim to perform predefined movements. The same holds for genetically encoded locomotion. Alternatively one may consider motion to be generated by self-organizing principles within the sensory-motor loop. In this case the only sensory information available is the state of the actuator, such as the angle of a limb or a wheel.

We use simulated and small real-world robots to study how locomotion can be generated through the `donkey-and-carrot' principle, which state that the target state for the actuator is updated as soon as the state of the actuator changes. One finds that complex and reactive motion patterns may emerge, with the actual motion pattern realized being dependent on the initial conditions.

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Computational Neurosciences, AI and Machine Learning

We develop theories and working principles for neural models and synaptic learning. Our interest involve both autonomous systems with self-sustained neural activity, self-organized locomotion and the impact of emotional control.

Cognitive systems theory is located at the crossroad between computational neurosciences and machine learning, as it regards both biological and artificial intelligences (AI). One of the long-term goals is to formulate generating principles allowing to construct modular information processing cognitive systems which are, as far as possible, self-organizing.
  • transient state dynamics
    One of the aims of complex system theory is to generate and examine the state of dynamical systems, which may be chaotic, laminar or synchronized. We have studied dynamical principles, such as attractor relict networks, leading to transient state dynamics. Transient states of activity correspond, as observed in the brain, to semi-stable alternating attractors, which are thought to be at the foundation of neural decision processes.



  • guided self organization
    It is mandatory, for the development of complex cognitive systems, to develop principles on how to guide the self-organization using overriding principles. For this purpose we employ generating functionals for the derivation of the evolution equations and examine the competition between conflicting objective functions. In particular we investigate generating functionals based on the principle of homeostatic optimization; the system tries to achieve a time-average distribution of local activities as close as possible to a given target distribution determined through information theoretical principles.

  • emotional control & the time allocation problem
    Our brain is a cognitive system, an adapting information processing dynamical system having a task - to keep its support unit, the body, alive. The brain has many different working modi and these dynamical states are regulated by an elaborated diffusive control mechanism, the emotional control. It operates diffusively, influencing large areas of downstream neurons. Emotional control does not influence directly cognitive information processing, but regulates the adaption of slow parameters, a mechanism denoted meta-learning within dynamical systems theory. Of particular interest it the role of emotional weighting for the time allocation problem. In our studies we examine in particular if advanced AIs will need a a control system that is functionally equivalent to emotions.




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