Research Group Leader 


What we doOur group develops (i) computational models for cognitive processes like decision making, and recognition and learning of complex spatiotemporal patterns, and (ii) neuronal models at several spatiotemporal scales, e.g. neural mass or single neuron models. For specific experimental neuroscience findings, we derive mathematical and functional mechanisms and aim at providing a mathematical framework for cognitive or neuronal processes.
MethodsAs a modelling framework, we mostly use Bayesian online inference for nonlinear dynamical systems. Using variational inference schemes, we construct neurobiologically plausible systems that receive dynamical sensory input and can learn and recognize this input and act accordingly, in an online fashion. This enables us to model brain function as predictive and selforganising dynamics operating at multiple timescales.
Areas of interestWe model phenomena in several neuroscience topics like perceptual and valuebased decision making, auditory communication, multisensory integration and dendritic computation.
ExperimentsWe perform our own experiments but also collaborate with several experimental groups to develop cognitive and neuronal models. Experiments are typically done using functional magnetic resonance imaging, electro/magnetoencephalography, psychophysics, and calcium imaging.
PositionsCurrently a PhD position and a Master thesis project is available.
Our Group
Current projects
CollaborationsKarl Friston Wellcome Trust Centre for Neuroimaging, London, UK.
Jean Daunizeau Brain and Spine Institute (ICM), Paris, France. Katharina von Kriegstein, Max Planck Institute for Cognitive and Neuroscience, Leipzig, Germany. Felix Blankenburg Bernstein Center for Computational Neuroscience, Berlin. John O'Doherty, California Institute of Technology, Pasadena, California, USA. Knut Holthoff, Universitätsklinikum Jena, Germany. References[1] Bitzer, S., Park, H., Blankenburg, F. & Kiebel, S. J. (2014) Perceptual decision making: Driftdiffusion model is equivalent to a Bayesian model Frontiers in Human Neuroscience, 8(102).
[2] Yildiz, I. B., von Kriegstein, K. & Kiebel, S. J. (2013) From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems PLoS Comput Biol, 9(9), e1003219
[3] Bitzer, S. & Kiebel, S. J. (2012) Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks Biological Cybernetics, 106(4), 201217
[4] Yildiz, I. B. & Kiebel, S. J. (2011) A hierarchical neuronal model for generation and online recognition of birdsongs PLoS Comput Biol, 7(12), e1002303
[5] Kiebel, S. J., von Kriegstein, K., Daunizeau, J., & Friston, K. J. (2009). Recognizing sequences of sequences PLoS Comp Biol, (5), e1000464.
[6] Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008). A hierarchy of timescales and the brain PLoS Computational Biology, 4(11), e1000209.
[7] Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2009). Perception and hierarchical dynamics Frontiers in Neuroinformatics, (3:20).
[8] Daunizeau J, den Ouden HEM, Pessiglione M, Kiebel SJ, Friston KJ, et al. Observing the Observer (II): Deciding When to Decide. PLoS ONE 5(12): e15555
[9] von Kriegstein K., Dogan O., Grüter M., Giraud A. L., Kell C. A., Grüter T., Kleinschmidt A., Kiebel S. J. (2008). Simulation of talking faces in the human brain improves auditory speech recognition Proc Natl Acad Sci U S A, 105(18):674752
