Bridging the gap between biological plausibility and function in models of prefrontal cortex

Our prefrontal cortex (PFC) plays a fundamental role when we make complex decisions, such as choosing a mode of transport in a foreign city on a rainy day. Recently, several computer-based, neural-network models have been developed to aid our understanding of PFC. However, model neural activity is often drastically different from real neural recordings and most studies consider rather rigid, unrealistic decision-making scenarios. 

We will combine computer modelling with real neural recordings to understand how PFC supports complex, naturalistic decision making. We will study how networks in PFC emerge naturally and progressively through reward-based learning. We will use new experimental recordings to study how PFC may enable rapid learning of new similar tasks, for example, when navigating a subway in an unfamiliar city, we use past experiences from previously used subways. 

Our approach will provide new experimentally validated mechanisms underlying complex cognition for a large variety of decision-making tasks.