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https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/550
Title: | Advances in the computational understanding of mental illness |
Authors: | Browning, Michael |
Issue Date: | Jul-2020 |
Citation: | Quentin J M Huys, Michael Browning, Martin Paulus, Michael J Frank. Advances in the computational understanding of mental illness. Neuropsychopharmacology . 2020 Jul 3 |
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. |
URI: | https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/550 |
ISSN: | 1740-634X |
Appears in Collections: | Mental Disorders (General) |
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