1. How much about our interaction with – and experience of – our world can be deduced from basic principles? This talk reviews recent attempts to understand the self-organised behaviour of embodied agents, like ourselves, as satisfying basic imperatives for sustained exchanges with the environment. In brief, one simple driving force appears to explain many aspects of action and perception. This driving force is the minimisation of surprise or prediction error. In the context of perception, this corresponds to Bayes-optimal predictive coding that suppresses exteroceptive prediction errors. In the context of action, motor reflexes can be seen as suppressing proprioceptive prediction errors. We will look at some of the phenomena that emerge from this scheme, such as hierarchical message passing in the brain and the ensuing perceptual inference.. Does the human brain implement predictive coding? Karl Friston, University College London Dialogues on the role of top-down factors in sensory processing

2. Overview The anatomy of inference graphical models canonical microcircuits Functional asymmetries spectral connections modulatory connections Action and perception inference and uncertainty simulations of saccadic searches

3. “Objects are always imagined as being present in the field of vision as would have to be there in order to produce the same impression on the nervous mechanism” - von Helmholtz Thomas Bayes Geoffrey Hinton Richard Feynman The Helmholtz machine and the Bayesian brain Richard Gregory Hermann von Helmholtz

4. “Objects are always imagined as being present in the field of vision as would have to be there in order to produce the same impression on the nervous mechanism” - von Helmholtz Richard Gregory Hermann von Helmholtz sensory impressions… Plato: The Republic (514a-520a)

5. Bayesian filtering and predictive coding prediction update prediction error

6. Minimizing prediction error Change sensations sensations – predictions Prediction error Change predictions Action Perception

7. A simple hierarchy Generative models whatwhere Sensory fluctuations

8. Generative model Model inversion (inference) A simple hierarchy Descending predictions Descending predictions Ascending prediction errors From models to perception Expectations: Predictions: Prediction errors: Predictive coding

9. Haeusler and Maass: Cereb. Cortex 2006;17:149-162Bastos et al: Neuron 2012; 76:695-711 Canonical microcircuits for predictive coding

10. frontal eye fields geniculate visual cortex retinal input pons oculomotor signals Errors (superficial pyramidal cells) Expectations (deep pyramidal cells) Top-down or backward predictions Bottom-up or forward prediction error proprioceptive input reflex arc Perception David Mumford Predictive coding with reflexes Action

11. Overview The anatomy of inference graphical models canonical microcircuits Functional asymmetries spectral connections modulatory connections Action and perception inference and uncertainty simulations of saccadic searches

12. superficial deep Errors (superficial pyramidal cells) Expectations (deep pyramidal cells) 020406080100120 0 0.05 0.1 0.15 0.2 0.25 0.3 020406080100120 0 1 2 frequency (Hz) 020406080100 0 2 4 6 8 10 12 14 spectral power Forward transfer function 020406080100 0 1 2 3 4 5 6 frequency (Hz) spectral power Backward transfer function Andre Bastos V4V1

13. Errors (superficial pyramidal cells) Expectations (deep pyramidal cells) Linear or driving connections Nonlinear or modulatory connections superficial deep NMDA receptor density

14. Interim summary Hierarchical predictive coding is a neurobiological plausible scheme that the brain might use for (approximate) Bayesian inference about the causes of sensations Predictive coding requires the dual encoding of expectations and errors, with reciprocal (neuronal) message passing Much of the known neuroanatomy and neurophysiology of cortical architectures is consistent with the requisite message passing In particular, the functional asymmetries and laminar specificity of intrinsic and extrinsic connections provide a formal perspective on spectral asymmetries and nonlinear coupling in the brain.

15. Overview The anatomy of inference graphical models canonical microcircuits Functional asymmetries spectral connections modulatory connections Action and perception inference and uncertainty simulations of saccadic searches

16. Sampling the world to minimise uncertainty Free energy minimisationExpected uncertainty “I am [ergodic] therefore I think”  “I think therefore I am [ergodic]” LikelihoodWorld modelPrior beliefs

17. saliencevisual inputstimulussampling Perception as hypothesis testing – saccades as experiments Sampling the world to minimise uncertainty Free energy minimisationExpected uncertainty

18. Frontal eye fields Pulvinar salience map Fusiform (what) Superior colliculus Visual cortex oculomotor reflex arc Parietal (where)

19. Visual samples Conditional expectations about hidden (visual) states And corresponding percept Saccadic eye movements Hidden (oculomotor) states

20. “Each movement we make by which we alter the appearance of objects should be thought of as an experiment designed to test whether we have understood correctly the invariant relations of the phenomena before us, that is, their existence in definite spatial relations.” ‘The Facts of Perception’ (1878) in The Selected Writings of Hermann von Helmholtz, Ed. R. Karl, Middletown: Wesleyan University Press, 1971 p. 384 Hermann von Helmholtz

21. Thank you And thanks to collaborators: Rick Adams Andre Bastos Sven Bestmann Harriet Brown Jean Daunizeau Mark Edwards Xiaosi Gu Lee Harrison Stefan Kiebel James Kilner Jérémie Mattout Rosalyn Moran Will Penny Lisa Quattrocki Knight Klaas Stephan And colleagues: Andy Clark Peter Dayan Jörn Diedrichsen Paul Fletcher Pascal Fries Geoffrey Hinton James Hopkins Jakob Hohwy Henry Kennedy Paul Verschure Florentin Wörgötter And many others