Research Group Modelling of Dynamic Perception and Action

What we do

Our 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.

Methods

As 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 self-organising dynamics operating at multiple time-scales.

Areas of interest

We model phenomena in several neuroscience topics like perceptual and value-based decision making, auditory communication, multisensory integration and dendritic computation.

Experiments

We 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.

Current projects

Perceptual Decision Making

We investigate the fundamental processes in the brain underlying the recognition of stimuli. In other words, what happens in the brain when you decide whether a presented stimulus represents, for example, a movement to the left, or to the right? In our research we focus on probabilistic models of perceptual decision making which explicitly handle changing stimuli. [more]

Value Based Decision Making

Understanding how inferences over decision structure are performed in a noisy and partially observable environment is a fundamental issue in the computational neurobiology of human decision. Here we investigate how these computations are performed in human brain with data driven (behavioral and neuroimaging data) probabilistic modeling; in collaboration with John O'Doherty, Peter Bossaerts and Jean Daunizeau. [more]

Recognizing Recurrent Neural Networks

Recurrent Neural Networks (RNNs) model dynamic processing in the brain. We reinterpret standard RNNs as generative models for dynamic stimuli and show that 'recognizing RNNs' resulting from Bayesian inversion of these models combine many important aspects of brain function. [more]

Free-energy and dendritic self-organisation

We pursue recent observations that, through selective dendritic filtering, single neurons respond to specific sequences of presynaptic inputs. We try to provide a principled and mechanistic account of this selectivity by applying the free energy principle to a dendrite that is immersed in its neuropil or environment. We assume that neurons self-organize to minimise a free energy bound on the self-information or surprise of presynaptic inputs that are sampled. In collaboration with Karl Friston. [more]

Generation and Recognition of Bird Songs

How do birds sing complex and elegant melodies? This question has kept researchers busy for decades and still we don't have a complete understanding of the mechanisms underlying birdsongs. In this project, we use dynamical systems theory to propose a biologically plausible, hierarchical model for birdsong generation and introduce a novel Bayesian inversion scheme for online recognition of birdsongs. [more]

Audiovisual speech perception

When we listen to a speaker we understand the conveyed message better when we also see the speaker's face. There is strong experimental evidence that this audiovisual integration of speech relies on specific brain mechanisms. However, there are currently no compelling computational models to explain key experimental findings. In this project we aim at developing a neurobiologically plausible model of audiovisual speech perception and testing its predictions using imaging techniques, in collaboration with the group of Katharina von Kriegstein [more]

Collaborations

Karl 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: Drift-diffusion 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), 201-217
[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 time-scales 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):6747-52