Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods.

This course sensitises doctoral students to ethical issues that may occur during their doctorate. After an introduction to ethics and good scientific practice, students are familiarised with resources that can assist them with ethical decision-making. Students get the chance to apply their knowledge in a context specific to research in electrical engineering and information technology.

The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented.

Neural Systems 2026 links biophysical neuron models to computation and behavior. It covers action potentials and dynamical systems, population and neural-mass models, memory and associative networks, birdsong and language learning, predictive coding, Bayesian inference and information theory, dimensionality reduction, intrinsic motivation, and neuroeconomic decision making.

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