- 21.09 - Introduction. Brain - Deep Net Analogies. (Grewe)
- 28.09 - Plasticity in the Brain. (Grewe)
- 05.10 - Training Methods for Deep Artificial Nets. (Grewe)
- 12.10 - Learning Rules. (Grewe)
- 19.10 - Deep Reinforcement Learning and the Dopamine System. (Grewe)
- 26.10 - Neural Solutions for Unsupervised Learning. (Grewe)
- 02.11 - Continual learning in artificial and biological systems. (Sorbaro)
- 09.11 - Why spikes? Introduction to SNNs. (Grewe)
- 16.11 - Deep Learning with Spikes. (Zenke)
- 23.11 - Recurrent Neuronal Networks. (Aceituno)
- 01.12 - Predictive Coding. (Aceituno)
- 07.12 - Neuromorphic Systems. (Indiveri)
- 15.12 - Comparing Representations of Biological and Artificial Networks. (Grewe)
- 22.12 - Self Organization. (Malsburg)
What to Expect? What will you be able to do after the Lecture?
- Able to read and interpret systems neuroscience papers and to draw inspiration from natural network systems.
- Able to train biologically plausible neuronal networks (FA and spiking).
- Overview about biological learning rules and principles of learning.
- Know the relation of dopamine and RL.
- Understand the basic deep learning concepts of meta-learning, unsupervised learning and recurrent neuronal networks.
Examples of Deep Learning Applications
- DL Driving Driverless Cars.
- DL for Robotic Control ("Learning Agile and Dynamic Motor Skills for Legged Robots").
- DL for Speech Generation.
- DL for Text Generation (GPT-3).
- Image Recognition and Medical Diagnostics ("Using AI to predict breast cancer and personalize care").
- DL for Playing Games ("Innateness, AlphaZero and Artificial Intelligence")