Archive

Content Neuroscience has played a key role in the history of artificial intelligence (AI), and has been an inspiration for building human-like AI, i.e. to design AI systems that emulate human intelligence. Neuroscience provides a vast number of methods to decipher the representational and computational principles of biological neural networks, which can in turn be used to understand artificial neural networks and help to solve the so called black box problem. This endeavour is called neuroscience 2.0 or machine behaviour. In addition, transferring design and processing principles from biology to computer science promises novel solutions for contemporary challenges in the field of machine learning. This research direction is called neuroscience-inspired artificial intelligence. The course will cover the most important works which provide the cornerstone knowledge to understand the biological foundations of cognition and AI, and applications in the areas of AI-based modelling of brain function, neuroscience-inspired AI and reverse-engineering of artificial neural networks. References Gazzaniga, Michael. Cognitive Neuroscience - The Biology of the Mind. W. W. Norton & Company, 2018. Ward, Jamie. The Student's Guide to Cognitive Neuroscience. Taylor & Francis Ltd., 2019. Bermúdez, José Luis. Cognitive Science: An Introduction to the Science of the Mind. Cambridge University Press, 2014. Friedenberg, Jay D., and Silverman, Gordon W. Cognitive Science: An Introduction to the Study of Mind. SAGE Publications, Inc., 2015. Gerstner, Wulfram, et al. Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press, 2014.

Vorlesung, CNAID, 4 SWS, Englisch

Vorlesung mit Übung, DSSS, 4 SWS, Englisch

2 SWS Lecture, 2 SWS Exercises; 5 ECTS; Lectures: Prof. Dr. Andreas M. Kist; Exercises: M.Sc. René Groh, Hernan Aguilera. Lectures (Mo 10-12)/Exercises (Fr 14-16): Room 04.023 , Cauerstr. 7 (Turm B) @ TechFak

In DSSS, we teach the basics of data science and highlight important skills for data scientists. We start with the hardware, glance at Machine and Deep Learning, and inform about code parallelization and graphical user interfaces. We show also data visuali…

In the first Zoom-Meeting, we show various biomedical datasets, where to find them and decide who will present one in a short talk. Aim of that seminar is that students gain an understanding how data is acquired, what is contained in a biomedical dataset a…

In the first Zoom-Meeting, we show various biomedical datasets, where to find them and decide who will present one in a short talk. Aim of that seminar is that students gain an understanding how data is acquired, what is contained in a biomedical dataset a…

In FANDAT, our aim is that students gain an understanding of how data is acquired, what is contained in a biomedical dataset, and what are the advantages and disadvantages of these particular datasets. We learn about the FAIR criteria and best practices fo…

In this seminar, you will learn and train an algorithm to track a randomly moving "animal" - a hexbug. Your aim is to develop a fast, yet accurate algorithm, and provide together with fellow students an ensemble solution. Teams compete with each other to f…

Hauptseminar, TRACO, 4 SWS, English - EQUAL CHANCES UNTIL APRIL 9th. If you register for the seminar, we expect that you COMMIT to attend the seminar in case you are accepted.