MIN Faculty
Department of Informatics
Knowledge Technology

Completed and awarded PhD projects

Pablo Vinicius Alves de Barros
Modeling Affection Mechanisms using Deep and Self-Organizing Neural Networks (2016)

One of the most important aspects of affective computing is how to make computational systems use emotion concepts in different situations. Although several types of research were done, we are still far away from having a system which can recognize and learn emotion concepts in a satisfactory way. In this thesis, we propose computational models which introduce a unified solution for emotional attention, recognition, and learning. These models are competitive in each of these tasks, and also provide an overview of a learning mechanism which adapts its knowledge according to a given situation.


Stefan Heinrich
Natural Language Acquisition in Recurrent Neural Architectures (2016)

Our understandings of the behavioural and mechanistic characteristics for natural language are still in its infancy and we need to bridge the gap between the insights from linguistics, neuroscience, and behavioural psychology. To contribute an understanding of the appropriate characteristics in a brain-inspired neural architecture that favour language acquisition, recurrent neural models have been developed for embodied and multi-modal language processing, embedded in a developmental robotics framework. In this dissertation the main contributions from the study of these models are reported.


Nicolás Navarro-Guerrero
Neurocomputational Mechanisms for Adaptive Self-Preservative Robot Behaviour (2016)

We believe that a deeper understanding of innate and learned defensive mechanisms could also be helpful in developing future robot generations, making them more adaptable and robust. Therefore, in this thesis, we study and develop three neuro-computational self-preservative mechanisms in the context of humanoid service robots to demonstrate the potential and feasibility of including bioinspired adaptive self-preservative mechanisms as part of real-world robotic systems. This thesis does not attempt to provide a comprehensive model of animal behaviour, but rather tries to draw attention to the need for it by presenting the potential of neglected aspects of animal behaviour such as self-preservative behaviour.


Wenjie Yan
Indoor Vision-based Robot Navigation: a Neural Probabilistic Approach (2016)

We present a neural probabilistic robot localization and navigation that provides a mere concept while enabling far-reaching functionality. Through emulating several basic functionalities of the brain the system is able to achieve complex tasks such as robust target tracking, environment learning through observation, and flexible robot navigation in a home-like environment. The concept of our work is implemented and evaluated using a robot platform in a home-like environment, and the results show that our neural system helps a robot to realize different functions successfully.


Johannes Bauer
One Computer Scientist's (Deep) Superior Colliculus. Modeling, understanding, and learning from a multisensory midbrain structure (2015)

The superior colliculus is a mid-brain region which integrates sensory input to localize mul-tisensory stimuli. This thesis aims to close the gap between models of SC physiology and system-level behavior, and development thereof. A new model of the SC based on self-organized statistical learning is proposed and it is shown to replicate a variety of important biological phenomena. It is then applied to a problem in robotics: binaural sound-source localization. We show that our algorithm can learn to per-form state-of-the-art sound-source localization.


Junpei Zhong
Artificial Neural Models for Feedback Pathways for Sensorimotor Integrations (2014)

The brain comprises hierarchical modules on various physiological levels. Neural feedback signals modulate the neural activities via inhibitory or excitatory connections within/between these levels. They have predictive and filtering functions on the neuronal population coding of the bottom-up sensory-driven signals in the perception-action system. In this thesis, we propose that the pre-dictive role of the feedback pathways at most levels of action and perception can be modelled by the recurrent connections in different artificial cognitive platforms. This will be examined by three recurrent neural network models. Furthermore, the three models and experiments with them show that the re-current neural networks are able to model feedback pathways and to exhibit the feedback-related sensorimotor predictive functions.


Jens Kleesiek
Action-driven perception – neural architectures based on sensorimotor principles (2012)

The active nature of perception and the intimate relation of action and cognition has been emphasized in philosophy and cognitive science for a long time. However, most of the current (computational) approaches do not consider the fundamental role of action for perception. Inspired by theories rooted in the research field of embodied cognition the scope of this thesis has been to design artificial neural architectures for the learning of sensorimotor laws. Guided by the core concept that an agent actually needs to act to perceive, a series of computational studies, including simulations and real-world robot experiments, have been conducted. It is shown that considering a set of common sensorimotor design principles results in goal-directed behavior of artificial agents. In this talk experiments as well as the underlying sensorimotor design principles will be presented and discussed.