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Stefan Kiebel

Multi-scale perception and action based on the free-energy principle. Stefan Kiebel. Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany. Wellcome Trust Centre for Neuroimaging , UCL, London, UK. My motivation. Model neuroscience data.

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Stefan Kiebel

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  1. Multi-scale perception and action based on the free-energy principle Stefan Kiebel Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany Wellcome Trust Centre for Neuroimaging, UCL, London, UK

  2. My motivation • Model neuroscience data Unifyingtheoryforbrainfunction Real lifeapplications?

  3. Control of dynamical system control state reward action dynamical system Dr. Palm

  4. Lesscontrol, noreward Agent with sensoryexpectations Sensory input reward Action Dynamic environment

  5. The free-energy principle • Hypothesis: brain minimizes surprise about its sensory input. Energy - entropy Divergence + surprise Sensation External states Internal states Action Friston et al. (2006), The free-energy principle

  6. Dynamic recognition and action Construction Coupled environment and agent 1st order differential equations 1st order differential equations Environment Environment Variational Bayes Mathematical operation 1st order differential equations 1st order differential equations Agent Agent Kiebelet al. (2009), Frontiers in neuroinformatics

  7. Hierarchical model Active inference: Mathematical model Bottom-up messages Top-down messages Action • Friston (2008), PLoSCompBiol

  8. Slow time-scales Single time-scale Multiple time-scales e1 e2 e3 e4 e1 e2 e3 e4 Fast s1 s2 Slow • Recognition: • non-robust • no higher level representation • Recognition: • robust by more constraints • higher level representation

  9. Kiebel et al. (2009), PLoSCompBiol Hierarchy of sequences Phonemes Hidden states e e e Level 1 a a a i i i o o o Syllables Hidden states 2 Level 2 1 3

  10. Recognition of sequences Recognition system Environment Phonemes Hidden states Phonemes Hidden states Syllables Syllables Hidden states Hidden states

  11. Action 1: Oculomotordynamics displacement time Friston et al. (2010), BiolCybernetics

  12. Visual stimulus Sensory channels Observed movement sensory prediction and error hidden states (location) 1 2 0.8 1 0.6 0.4 0 0.2 0 -1 -0.2 -0.4 -2 10 20 30 40 50 60 10 20 30 40 50 60 time time Active inference under flat priors (movement with percept) cause (perturbing force) perturbation and action 1.2 1.2 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 -0.2 -0.2 10 20 30 40 50 60 10 20 30 40 50 60 time time

  13. Action explains away object movement sensory prediction and error hidden states (location) 2 1 1 0.5 0 0 -1 -2 10 20 30 40 50 60 10 20 30 40 50 60 time time Active inference under tight priors (no movement or percept) cause (perturbing force) perturbation and action 1.2 1 1 0.8 0.5 0.6 0 0.4 -0.5 0.2 0 -1 -0.2 10 20 30 40 50 60 10 20 30 40 50 60 time time

  14. Self-induced action Action and perceived movement cause Stimulus movement trajectory Self-generated movements Induced by priors Time Displacement Robust to perturbation Change in motor gain Time Displacement

  15. Action 2: Cued motor command Belief Vision Move fingertowards targetwhenlit. Proprioception Jointed arm

  16. Conclusions • Agents online, dynamic and robust Coupled environment and agent Generates dynamic sensory input Environment Actions areconsequenceof (multi-scale) sensoryexpectations Neuroscientifically plausible Dynamicrecognition and action Agent

  17. Thanks to Karl Friston Jean Daunizeau Katharina von Kriegstein

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