Abstract

Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness.

It encompasses both data-driven and theory-driven efforts.

Here, recent advances in theory-driven work are reviewed.

We argue that the brain is a computational organ.

As such, an understanding of the illnesses arising from it will require a computational framework.

The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning.

We discuss both general and specific challenges for the field, and suggest ways forward.

Citations

Quentin J M Huys, Michael Browning, Martin Paulus, Michael J Frank. Advances in the computational understanding of mental illness. Neuropsychopharmacology . 2020 Jul 3

Sponsorship: Supported by the NIHR

DOI: https://DOI: 10.1038/s41386-020-0746-4

URI: https://www.oxfordhealth.nhs.uk/orka/title/advances-in-the-computational-understanding-of-mental-illness/