Empowering the Future of Neuroscience

Empowering the Future of Neurosceince

Bach Group

Our mission is to unpack the parallel computing structure of the human mind, how this software architecture is implemented in the brain, and where it fails in mental illness.

We bring the mind to its computational limits by putting it in its natural environment, where it needs to cope with survival threats. This requires rapid actions with utmost precision and continuous updating.

Avoidance actions in the absence of real threat are a hallmark of several psychiatric conditions, such as anxiety disorder or post-traumatic stress disorder. We seek to unpack the underlying mechanisms and develop novel treatments.

Our framework is based on the key idea of multiple controllers: systems that flexibly deal with limited parts of the environment, and compete for action output. Collectively these systems can perform approximate Bayesian decision-making. To probe these systems, we bring the mind into those niches of the environment that require forecasting threat and responding to it. Experimentally, this approach is based on virtual reality, serious games, and associative learning.

Discover our homepage here.


  • immersive virtual reality in humans
  • markerless and marker-based motion capture
  • machine-learning based motion sequencing
  • wearable magnetoencephalography (OPM-MEG)
  • psychophysiology
  • computational modelling of behaviour, biosignals, and neural systems
  • psychopharmacology

5 selected publications

  1. Bach DR (2021). Cross-species anxiety tests in psychiatry: pitfalls and promises. Molecular Psychiatry, in press.
  2. Bach DR, Melinščak F, Fleming SM, Voelkle M (2020). Calibrating the experimental measurement of psychological attributes. Nature Human Behaviour, 4, 1229-1235.
  3. Castegnetti G, Tzovara A, Khemka S, Melinščak F, Barnes GR, Dolan RJ, Bach DR (2020). Representation of probabilistic outcomes during risky decision-making. Nature Communications, 11, 2419.
  4. Korn CW & Bach DR (2018). Heuristic and optimal policy computations in the human brain during sequential decision-making. Nature Communications, 9, 325.
  5. Bach DR & Dayan, P (2017). Algorithms for survival: a comparative perspective on emotions. Nature Reviews Neuroscience, 18, 311-319.