Sciencey: 

The Ventral Tegmental Area (VTA) is a highly heterogeneous structure. By utilising this heterogeneity, the VTA can encode highly complex signals about reward, expectation, uncertainty and behaviour. 

A critical, but often under-investigated aspect of the VTA's function is the role of its diverse inhibitory neuronal populations. Multiple sub-populations of GABAergic neurons exist within the VTA, and the functional significance of this heterogeneity remains largely unexplored. 

I use a combination of physiological, behavioural and machine learning techniques to study the VTA's heterogeneity, and its role in reward learning, with a special focus on GABAergic neurones. 

Beyond this, I am also interested in: 

• Pathophysiology of addiction
• Pathophysiology of depression
• How the brain encodes time and uses it to form expectations about the environment

I hope to use my research into reward, and the VTA, to further explore these topics. 

 

Not so sciencey: 

The brain processes information about the world we live in, and we use this information to make predictions about the future. These predictions are often about rewards. For example, when we hear the sound of an ice cream van through the window, we predict it will have ice cream.

What happens, though, if these predictions are wrong? 

It is important that animals (and people) are able to update their predictions about the world, based on new information. Let's use the ice cream van example. If we go to the van expecting an ice cream, and recieve a brussel sprout, we need to learn that this ice cream van is LESS rewarding than predicted. Alternatively, if we go to the van expecting an ice cream, and we find out they give out an extra free ice cream for every ice cream you buy, we neeed to learn that this ice cream van is MORE rewarding than we predicted. 

The brain updates these predictions through a process called reward learning. Neuroscientists believe this occurs in a part of the brain called the Ventral Tegmental Area (VTA). It is a dopamine centre, and it processes a lot of information about reward. This includes the likelihood of reward, the size of reward, the expectation of reward, and the actions necesary to recieve reward. 

The brain cells (neurons) in the VTA are highly diverse. This means that different types of VTA neurons can carry different types of information. These different types of neuron work together to control the process of reward learning. It is my job to work out what each one does, why and how. 

To do this, I study brain activity in mice as they do different behavioural tasks. I also create Artificial Intelligence models of what we think the brain does during this reward learning process. By comibining the information I get from these two methods, I try to get a clearer idea of the brain processes underlying reward learning. 

In short: I study learning in mice and machines.