Computational Modelling

We use a variety of computational modelling techniques to describe neural activity in different parts of the brain and animal behaviour.

Network models

We simulate networks of neurons to study how the brain processes information. For example, we generate network models of the basal ganglia. This includes simulations of thousands of neurons with a connectivity and synaptic transmission that resembles specific basal ganglia subregions and cell types. In a network simulation of the globus pallidus and the subthalamic nucleus we have investigated how inputs from the striatum and the cortex can lead to activity patterns that were measured in rats performing a behavioural task (Mirzaei et al., 2017). These activity patterns included sensory and motor responses in single neurons, but also brief epochs of beta oscillations in the local field potential. Furthermore, we use large-scale network models thalamo-cortical basal ganglia circuits to study how actions can be promoted or suppressed in the context of inhibitory control.

Single neuron models

We simulate the activity of single neurons to study neurons respond to inputs that they receive from other neurons. This is important to understand how activity patterns change from one brain region to another and how motor and sensory signals affect animal behaviour. For example, we simulate thalamocortical neurons and study how they respond to inputs from the basal ganglia and cortex (Mohagheghi Nejad et al., 2018). In particular, we are interested in understanding changes between normal and impaired transmission of motor signals, e.g. in Parkinson’s disease.

Biophysical models

We use different biophysical models to study how neurons transmit signals. Currently, we focus on the dopaminergic system, trying to understand how dopamine is released in the striatum of the basal ganglia and how that affects neurons in the striatum. For example, we simulate the diffusion of dopamine in the striatal extracellular space and investigate kinetic models of dopamine receptors.