Neuromodeling; Computational Disease Modeling; DEFINITION

Computational Psychiatry is a heterogeneous field at the intersection of computational neuro-science and psychiatry. Incorporating methods from psychiatry, psychology, neuroscience, be-havioural economics and machine learning, computational psychiatry focuses on building math-ematical models of neural or cognitive phenomena relevant to psychiatric diseases. The models span a wide range- from biologically detailed models of neurons or networks to abstract models describing high-level cognitive abilities of an organism. Psychiatric diseases are conceptualized as either an extreme of normal function, or as a consequence of alterations in parts of the model. As in computational neuroscience more generally, the building of models forces key concepts to be made concrete and hidden assumptions to be made explicit. One critical functions of these models in the setting of psychiatry are their ability to bridge between low-level biological and high-level cognitive features. While many neurobiological alterations are known, the exclusively atheoretical focus of standard psychiatric nosology on high-level symptoms has as yet prevented an integration of these bodies of knowledge. David Marr pointed out that models at different levels of description may be independent (Marr, 1982). Nevertheless, algorithmic details may constrain functions at the computational level. The models used in computational psychiatry make these constraint