## Neural Data Science

We develop advanced analysis methods to apply them to neural and behavioural data, and combine them with computational models. In close collaboration with experimental research groups we analyse and model large data sets of electrophysiological recordings. Our goal is to identify the neural mechanisms underlying cognitive control, in particular in prefrontal and basal ganglia circuits, and to determine how the neuromodulator dopamine contributes to them. We use a variety of analysis and computational modelling techniques, such as machine learning approaches and numerical simulations of single neuron and network activity.

## Members

### Spatio-temporal dopamine signalling

Dopamine is a neurotransmitter with complex, not well-understood effects. In the basal ganglia dopamine modulates cortical input to the striatum. We study the functional role of dopamine with computational models ranging from low-level’ cellular models, over neural network models to high-level’ reinforcement learning models. The goal is to understand how dopamine contributions to learn selecting good action (e.g. via synaptic plasticity) as well as the actual execution of actions (e.g. by changing motivational aspects of behavior). We apply our research result to clinical scenarios including Parkinson’s disease to study how pathological dopamine levels affect behavior.