Research interests

My main research focus is on computational psychiatry (mainly on computational models of decision making in depression, anxiety and addiction).

Computational psychiatry

This approach is based around the fact that most psychiatric disorders involve neuromodulators and that recent work has yielded considerable insight into the normative function of these same neuromodulators. Thus, it is now possible to approach the dysfunctions in psychiatric disorders from a normative understanding of normal function, hence the term "computational psychiatry. See Huys et al. (2011) for a critical overview, Huys (2013a) for an overview of the field, and Huys et al., (2015a): Decision-theoretic psychiatry for an overview from the vantage point of decision theory, and Huys et al., (2015b): Depression: a decision-theoretic analysis for a specific application to depression.

We have applied the techniques mainly to depression (Huys and Dayan (2009), Huys et al., (2013), Webb et al., (2015), and Huys et al., (2015d)) and to addiction (Garbusow et al., (2015), Sebold et al., (2014)).

Pavlovian influences on Decision-making

This work examines how humans solve complex decision problems and what approximations they rely on. We have focused particularly on the influence of reflexive emotional (Pavlovian) processes on simple choices (e.g. PIT, Huys et al., 2011 and Guitart-Masip, Huys et al., 2012), but also how such influences arise within more complex decision-making processes (Huys et al., 2012 and Huys et al., 2015c).

Population coding

This work looked at population coding from an angle which has been surprisingly neglected: time. What happens to the structure of the code if stimuli are not static, but move around? We showed that interpretation of spikes in a dynamic, fast-changing world is impossible without a prior over how stimuli change. Depending on the nature of this prior, the code can be computationally very unwieldy, but it can still be handled approximately by recurrent neural networks.

Single-cell models

Building detailed, biophysically realistic single-cell models remains a major challenge. The nature of the parametrization of these models results in extremely complex, non-linear interactions between the various parameters. We here build on recent advances in imaging techniques, which will soon allow access to the transmembrane voltage at many points throughout a cell's dendritic arbor. Given this rich information, and some more constraints, it is possible to set many of the parameters of interests in an automatic way.