192.037 Seminar in Artificial Intelligence : Neuroscience-based Artificial Intelligence
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2024S, SE, 2.0h, 3.0EC


  • Semesterwochenstunden: 2.0
  • ECTS: 3.0
  • Typ: SE Seminar
  • Format der Abhaltung: Blended Learning


Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage 

  • to name relevant aspects about neuroscience-based AI,
  • to describe the current research for a chosen topic from the area of neuroscience-based AI, and
  • to find relevant literature for a topic from research about neuroscience-based AI.

At the end of the course, the students will be able to read and critique scientific papers. They will be able to analyse the methods proposed in the field of neuroscience-based AI, and evaluate their strengths and weaknesses. They will be able to prepare an oral presentation of their findings and discuss the studied methods in detail. Finally, they will be able to assess their peers’ work.

Inhalt der Lehrveranstaltung

The seminar will cover topics belonging to a relevant subfield of machine learning and artificial intelligence (AI): neuroscience-inspired AI.

The course will explore the intersection of computational neuroscience and machine learning, examining how they can help advance AI. Computational neuroscience uses mathematical models of the brain to understand the principles that govern neural systems. Combined with machine learning, it enables the development of algorithms that can model neural processing, offering insights into both neuroscience and AI. Typical problems include decoding the computation behind neural activity or deriving learning mechanisms that are biologically plausible. Key areas of focus for this seminar include:

  • Biologically plausible learning methods: Explore alternatives to backpropagation like predictive coding, Hebbian learning, and spike-timing-dependent plasticity.
  • Bridging the gap between machine learning and neuroscience: Learn about the fundamental differences between artificial neural networks and biological neural networks as well as the similarities.
  • Computational models for brain function: Examine models that decode how the brain processes information, stores memories, and makes decisions.


Die Studierenden müssen

  • eine Literaturrecherche zu einem gewählten Thema durchführen,
  • ein Vortragskonzept zum gewählten Thema erstellen, und
  • einen 25 min Vortrag halten.

Students attend the kick-off meeting. In the kick-off meeting, each student is allocated to a topic and a paper. After the kick-off meeting, each student is expected to read the assigned paper and the papers belonging to their topic, and to perform a literature review of the related work. Four weeks after the kick-off meeting, each student is expected to schedule a meeting with their advisor to discuss their findings. In June, there will be a presentation day. During the presentation day, each student is expected to prepare a 25 min presentation where their findings are summarised (which is followed by a 5-10 min discussion). After the presentation, students are given feedback on their presentation and seminar paper. Each student will write a seminar paper (6-7 pages + references) on their topic, where the central ideas and methods presented in the talk are summarised. 



Weitere Informationen

Beachten Sie beim Verfassen der Ausarbeitung bitte die Richtlinie der TU Wien zum Umgang mit Plagiaten: Leitfaden zum Umgang mit Plagiaten (PDF)

Vortragende Personen


LVA Termine

Mi.14:00 - 15:0013.03.2024 https://tuwien.zoom.us/j/8104603001?pwd=WTlBMERDRkJFMVZNU2Z6aHRDUk9Zdz09Kickoff Meeting
Di.09:00 - 14:0025.06.2024 Seminarraum 192-07Presentations




Von Bis Abmeldung bis
15.02.2024 10:00 11.03.2024 22:00 08.03.2024 22:00


066 931 Logic and Computation Gebundenes Wahlfach
066 936 Medizinische Informatik Gebundenes Wahlfach


Es wird kein Skriptum zur Lehrveranstaltung angeboten.