Our model animals will learn to perform touchscreen tasks that involves flexible decision making, motivated by a tasty treat of their choice. The tasks will be the same or equivalent to those administered to human participants. Neural activities will be recorded through wireless devices, which allows the animals to move freely; and their behaviours will be monitored and quantified with state-of-the-art algorithms. This approach allows us to study the neural basis of cognitive flexibility, and to establish the causal link between neural activity and behaviour through circuit manipulation techniques.
The job of a neuron is to process information, but it does not do it alone. Using advanced data analysis techniques, we examine the dynamics at the level of neuronal ensembles, the interaction between different brain regions, and how these processes contribute to flexible decision making. We do this using a combination of newly acquired and existing data.
How does the brain represent the value of a choice, update it based on both positive and negative feedback and use it to guide actions? To answer this question, we will establish a mathematical model for reinforcement learning (RL) behaviour in mice, identify neural correlates for key parameters in the model, and manipulate the neural correlates to establish its causal link with behaviour. The results are then used to improve our models and generate new testable hypotheses.