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At the end of the module “Neural Engineering: Implantate, Schnittstellen und Algorithmen” students are able to understand fundamental concepts of “Neural Engineering”, and to evaluate specific algorithms in the context of “Neural Prosthetics” and in autonomous technical systems.

Note: "Neural Engineering: Implants, Interfaces and Algorithms Neuromorphic" is the revised and expanded former "Engineering for Cognitive Systems". Neural engineering is an interdisciplinary research area that combines foundations of biology, physics, mathematics, computer science, psychology and engineering to design artificial “neural systems”, such as active body prostheses, or autonomous robots, whose design and functional principles are based on those of biological nervous systems. In this lecture students get introduced to basics of neural engineering, such as recordings from neuronal cells, brain-computer interfaces, interpretations of neuronal signals, setup and operational principles of biological and artificial neural networks, distributed system development and computation, event-based sensors or control, or analog low-power VLSI chip design for neural inspired sensors and computing units. Applications: design of (a) algorithms for sensory data processing and motor control in applications such as neuro-prosthesis, and (b) autonomous cognitive systems to interact in real-time in real-world scenarios.

Recommended lectures: Computational Intelligence (or similar, such as Introduction to Artificial Intelligence, Machine Learning). Basic programming knowledge in a language such as C, Java or an environment such as MatLab. No mandatory prerequisites.

written exam; if small student number individual oral exam

Michael Arbib, The Handbook of Brain Theory and Neural Networks, MIT press