Content of the seminar: Brain-Inspired Artificial Intelligence

Within the last decade, deep learning has revolutionized Artificial Intelligence research. Deep learning is based on neural networks, which are themselves to some extent a brain-inspired model. However, in order to increase their capabilities, neural networks have often been extended using ideas that were inspired from our knowledge of the brain.

In this Seminar, we will discuss scientific literature on brain-inspired artificial intelligence.

Students are expected to have visited the courses Neural Networks or Principles of Brain Computations (or similar courses) prior to this Seminar. In parallel, there will be a Seminar/Project) which will tackle the same topic, being a good complement to this Seminar.

Topics for Talks include (but are not limited to):

• Rapid learning and task solving with neural networks
• Deep Reinforcement Learning
• Neural Networks with brain-inspired memory-components
• Spiking encoder/decoder architectures
• Variational autoencoders for unsupervised learning
• Learning from imitation
• Spiking convolutional networks
• Energy-based models

Talks will be assigned at the first seminar meeting. Students are requested to have a quick glance at the topics prior to this meeting in order to determine their preferences (papers will be posted soon). Note that the number of participants for this seminar will be limited.

General rules: