Prof. Dr. Laurenz Wiskott

Research interest

We are an interdisciplinary research group focusing on principles of self-organization in neural systems, ranging from artificial neural networks to the hippocampus/memory system. By bringing together machine learning and computational neuroscience we explore ways of extracting representations from data that are useful for goal-directed learning.

On the machine learning side our work is centered around reinforcement learning, where an agent learns to interact with its environment. In this context we investigate learning of representations for different kinds of data, such as visual data and graphs, by means of deep learning as well as classical unsupervised methods. Additionally, we research model-based agents that can remember their environment and are capable of planning ahead. On all these frontiers, we do not only seek to improve algorithmic performance but also to develop new ways of building more interpretable, explainable and human-friendly AI.

On the neuroscience side, our work focuses on computational modeling of brain functions concerned with encoding, storage and recall of memories. Through this we aim to understand how information is learned, represented within different types of memory and finally reconstructed from memory.

Methods

• System level models of brain functions (vision and memory)
• Slow Feature Analysis (SFA)
• deep representation learning methods – especially different flavors of variational autoencoders (VAEs)
• model based deep reinforcement learning algorithms for complex domains
• designing model architectures that qualify for efficient and precise planning
• advanced machine learning techniques
• generative models ranging from restricted boltzmann machines (RBM) to variational autoencoders (VAE) and vector quantized variational autoencoders (VQVAE) and also PixelCNN

Master theses

Suggestions for Master projects in the department Theory of Neural Systems (2023)

Examples of previously supervised Master theses:

• Interaction of Ensembling and Double Descent in Deep Neural Networks (2021)

Website

click here to find out more about the department of Theory of Neural Systems