Periodic Reporting for period 3 - ExpectBG (Elucidating the Basal Ganglia Circuits for Reward Expectation)
Berichtszeitraum: 2023-01-01 bis 2024-06-30
Predicting future outcomes is fundamental for adaptive behaviour. If we can predict the outcome of our actions we can choose the best course of action, if we can’t then then the outcome will be a surprise that we’ll use to learn for the future. Reward predictions for example are crucial for learning since they can be compared to actual outcomes to determine if an outcome was better or worse than expected. Even though reward expectation signals are observed in many areas of the brain how they are computed remains unknown. The main reason for lack of progress is the absence of a clear understanding of where expectation is generated, and which circuits are involved in its computation. Consequently, we are missing the prerequisite knowledge for determining where reward expectation arises, how it is computed, and how expectations are learnt. The aim of this work is to determine where expectations (predictions) are formed in the brain and determine what types of predictions are being stored. Knowing how the brain learns to make predictions is critical as these are processes that are disrupted in many psychiatric diseases such as depression and schizophrenia.
We have established the paradigms that will allow us to determine where different types of expectation are formed in the brain. So far, we have determined that in line with our hypothesis that expectations appear to form in the striatum, a region of the brain that receives dopaminergic neuromodulatory input. Furthermore, when investigating what information is conveyed by the dopaminergic input to the striatum, we have revealed that different dopaminergic neurons carry information about different kinds of prediction. One population encodes predictions about reward outcomes while another encodes predictions about what actions are about to be taken. Work is now underway to determine how these two types of predictions interact and drive learning.
We’ve discovered that separate dopaminergic populations encode different kinds of predictions. These findings are challenging how we think about learning. Previously we thought that dopamine contributed to learning by informing the brain whether an outcome of an action was better or worse than expected. In this way we can learn the value of actions. Now that we know that dopamine populations carry information about other types of prediction it will be important to understand how these different predictions drive learning. One possibility is that different types of prediction may support different types of learning for example flexible fast learning or slower habitual learning.