Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multi-layer perceptrons), support-vector machines, echo-state networks, non-supervised networks (competitive, Kohonen, Hebb), recurrent networks (Hopfield, CTRNNs - continuous-time recurrent neural networks), spiking neural networks, spike-time dependent plasticity, applications. Special consideration will be given to neural networks embedded in adaptive systems having to interact with the real world, such as embodied systems (in particular robots). Cooperation of neural control, morphology, materials, and environment. Evolutionary approaches to designing autonomous systems; interaction of learning and evolution. Network theory applied to brain networks; motifs.

Additional case studies will be discussed to deepen the understanding of neural networks, e.g. Neural interfacing - coupling neural systems with technology (in particular robotic devices), neural imaging studies, Adaptive Resonance Theory, models of categorization (ALCOVE, Edelman), Distributed Adaptive Control (DAC), neural gas and DRNNs - Dynamically Rearranging Neural Networks (neuro-modulator-based networks), neural network models of memory. Selected neural network and brain modeling projects.

This is an elementary, interdisciplinary introduction to neural networks, suited not only for computer scientists, but also for economists, biologists, psychologists, etc.

The lectures will be given in English.

This course is worth of 6 credit points.

Check out also the seminar web page at the University of St. Gallen for general information.