Activities at the Laboratory of Computational Neuroscience focus on the following questions centered around temporal aspects of information processing in the brain.

Networks of (Spiking) Neurons

Standard neural network theory describes the neuron as an input-output unit with a nonlinear transfer function. Real biological neurons are much more complicated than that.

Learning by Surprise and neoHebbian plasticity rules

Humans and animals learn even in the absence of rewards: e.g., tourists like to explore a new city and children like to explore a new toy. What is the drive for doing this and what is happening in the brain during reward-free learning?

Reinforcement Learning and the Brain

Humans and animals learn by trial-and-error to repeat rewarded behavior and avoid actions with unpleasant consequences. Reinforcement learning is a computational framework to study this kind of learning.