Computational Neuroscience

Computational Neuroscience

Decision-making in the brain

I build mathematical and computational models of neural processing, with a particular emphasis on representation and learning. The main focus of my group is on reinforcement learning and unsupervised learning, covering the ways that animals come to choose appropriate actions in the face of rewards and punishments, and the ways and goals of the process by which they come to form neural representations of the world. The models are informed and constrained by neurobiological, psychological and ethological data.

My department works in the general field of neural reinforcement learning and decision-making. This covers the ways that humans, other animals, and artificial systems should and do optimize their actions and cognition in the light of rewards and punishments. We also study the consequences for neurological and psychiatric disease when these mechanisms, or their embedding in their environment, break down. These areas are developing and expanding very quickly because of technical innovations in cognitive science, artificial intelligence, neuroscience, psychiatry and in large-scale natural experiments. We develop both theories and behavioural and analytical methodologies in rich collaborations across questions and model systems.

There are three main current directions for our work, neuromodulation, meta-control and computational psychiatry.

Neuromodulation: The neuromodulators dopamine, serotonin, acetylcholine and norepinephrine powerfully regulate a host of critical functions in the brain, and are involved or therapeutically manipulated in many diseases. Ideas abound about the functional associates of these substances -- their representation of computationally meaningful quantities such as predictions of, and prediction errors for, affectively important outcomes, and different sorts of uncertainty. New technologies for recording and manipulating these neuromodulators are being developed; we exploit these to address critical questions about their function.

Meta-control: Meta-control is the control of control - the way that decision-making systems in the brain are themselves regulated in order to generate adaptive behaviour in the face of substantial real and opportunity costs for the engagement of the neural machinery involved. We develop new, computationally-informed, behavioural tasks, and adopt new imaging analysis methods to examine these processes in functional and temporal detail.

Computational Psychiatry: Our understanding of decision-making in the healthy population has now advanced to the point that we can use it to investigate characteristic modes of failure. This provides precise, process- and circuit-oriented hypotheses for understanding symptoms and causes of mental dysfunction. In turn, this richly couples notionally organic and psychological concerns, and provides new methods for classification and prediction. In turn, as we deepen our understanding of failure modes, an important window is opened onto normal cognition.

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