How much about our interaction with – and experience of – our world can be deduced from

1. Predictive coding and repetition suppression Karl Friston, University College London 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 our world. In brief, one simple driving force appears to explain nearly every aspect of our behaviour and experience. 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, simple reflexes can be seen as suppressing proprioceptive prediction errors. We will look at some of the phenomena that emerge from this formulation, such as hierarchical message passing in the brain and the perceptual inference that ensues. I hope to illustrate these points using simple simulations of auditory processing, with a special focus on repetition suppression in the context of the mismatch negativity and omission related responses.

2. Overview The anatomy of inference predictive coding graphical models canonical microcircuits Birdsong perceptual categorization repetition suppression omission related responses sensory attenuation a birdsong duet

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 Hermann von Helmholtz Richard Gregory Geoffrey Hinton The Helmholtz machine and the Bayesian brain Thomas Bayes Richard Feynman

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 sensory impressions… Hermann von Helmholtz Richard Gregory Plato: The Republic (514a-520a)

5. Bayesian filtering and predictive coding changes in expectations are predicted changes and (prediction error) corrections prediction error

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

7. Generative models what where A simple hierarchy Sensory fluctuations

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

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

10. David Mumford Perception Prediction error (superficial pyramidal cells) Higher vocal centre Expectations (deep pyramidal cells) Area X Thalamus Hypoglossal Nucleus Action

11. 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

12. Hermann von Helmholtz “It is the theory of the sensations of hearing to which the theory of music has to look for the foundation of its structure." (Helmholtz, 1877 p.4) ‘Helmholtz, H. (1877). “On the Sensations of Tone as a Physiological Basis for the Theory of Music", Fourth German edition,; translated, revised, corrected with notes and additional appendix by Alexander J. Ellis. Reprint: New York, Dover Publications Inc.,1954

13. Overview The anatomy of inference predictive coding graphical models canonical microcircuits Birdsong perceptual categorization repetition suppression omission related responses sensory attenuation a birdsong duet

14. Generating bird songs with attractors Higher vocal center Syrinx Sonogram Frequency 0.5 1 1.5 Hidden causes Hidden states time (sec)

15. prediction and error 20 15 Predictive coding and message passing 10 5 0 -5 10 20 30 40 50 60 causal states Descending predictions 20 15 stimulus 10 5000 5 4500 Ascending prediction error 0 4000 -5 3500 -10 10 20 30 40 50 60 3000 hidden states 20 2500 2000 15 0.2 0.4 0.6 0.8 time (seconds) 10 5 0 -5 10 20 30 40 50 60

16. Perceptual categorization Song a Song b Song c Frequency (Hz) time (seconds)

17. hidden states percept prediction error 5 5000 10 4000 Simulating ERPs to repeated chirps 0 LFP (micro-volts) Frequency (Hz) 0 3000 -10 2000 -5 0 0.2 0.4 0.1 0.2 0.3 100 200 300 5 10 4000 0 LFP (micro-volts) Frequency (Hz) 0 -10 2000 -5 0 0.2 0.4 0.1 0.2 0.3 100 200 300 5 10 4000 Perceptual inference: suppressing error over peristimulus time Perceptual learning: suppression over repetitions 0 LFP (micro-volts) Frequency (Hz) 0 -10 2000 -5 0 0.2 0.4 0.1 0.2 0.3 100 200 300 5 10 4000 0 LFP (micro-volts) Frequency (Hz) 0 -10 2000 -5 0 0.2 0.4 0.1 0.2 0.3 100 200 300 5 10 4000 0 LFP (micro-volts) Frequency (Hz) 0 -10 2000 -5 0 0.2 0.4 0.1 0.2 0.3 100 200 300 5 10 4000 0 LFP (micro-volts) 0 Frequency (Hz) -10 2000 -5 0 0.2 0.4 0.1 0.2 0.3 100 200 300 Time (sec) peristimulus time (ms) Time (sec)

18. Synaptic efficacy Synaptic gain 3 6 Synthetic MMN 2.5 5 2 4 1.5 3 changes in parameters hyperparameters 1 2 0.5 1 0 0 1 2 3 4 5 1 2 3 4 5 presentation presentation 10 10 10 10 10 Sensory prediction error 0 0 0 0 0 -10 -10 -10 -10 -10 First presentation (before learning) Last presentation (after learning) 100 200 300 100 200 300 100 200 300 100 200 300 100 200 300 0.2 0.2 0.2 0.2 0.2 0 0 0 0 0 Extrasensory prediction error -0.2 -0.2 -0.2 -0.2 -0.2 -0.4 -0.4 -0.4 -0.4 -0.4 100 200 300 100 200 300 100 200 300 100 200 300 100 200 300 primary level (N1/P1) secondary level (MMN) 20 0.4 15 0.2 10 0 5 Difference waveform Difference waveform 0 -0.2 -5 -0.4 -10 -15 -0.6 0 100 200 300 400 0 100 200 300 400 peristimulus time (ms) peristimulus time (ms)

19. STG STG A1 A1 subcortical input Extrinsic connections Synaptic efficacy Synaptic gain Intrinsic connections 200 200 3 6 180 180 2.5 5 160 160 140 140 2 4 120 120 1.5 3 changes in parameters hyperparameters 100 100 80 80 1 2 60 60 40 40 0.5 1 20 20 0 0 0 0 5 1 2 3 4 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 presentation presentation presentation presentation Synthetic and real ERPs

20. Overview The anatomy of inference predictive coding graphical models canonical microcircuits Birdsong perceptual categorization repetition suppression omission related responses sensory attenuation a birdsong duet

21. Sequences of sequences Higher vocal center Area X Syrinx Sonogram Frequency (KHz) 0.5 1 1.5 Time (sec)

22. 4500 4000 3500 Frequency (Hz) 3000 stimulus (sonogram) without last syllable 2500 omission and violation of predictions 4500 4000 3500 Frequency (Hz) 3000 percept percept 2500 0.5 1 1.5 0.5 1 1.5 Time (sec) Time (sec) ERP (prediction error) with omission 100 100 Stimulus but no percept 50 50 0 0 LFP (micro-volts) LFP (micro-volts) Percept but no stimulus -50 -50 -100 -100 500 1000 1500 2000 500 1000 1500 2000 peristimulus time (ms) peristimulus time (ms)

23. Overview The anatomy of inference predictive coding graphical models canonical microcircuits Birdsong perceptual categorization repetition suppression omission related responses sensory attenuation a birdsong duet

24. Active inference: creating your own sensations Higher vocal centre Corollary discharge (exteroceptive predictions) Motor commands (proprioceptive predictions) Area X Hypoglossal Nucleus Thalamus

25. Active inference and sensory attenuation

26. Active inference and sensory attenuation Mirror neuron system

27. percept 5000 4500 4000 Frequency (Hz) 3500 3000 2500 1 2 3 4 5 6 7 time (sec) First level expectations (hidden states) 100 50 0 -50 0 1 2 3 4 5 6 7 8 time (seconds) Second level expectations (hidden states) 80 60 40 20 0 -20 -40 0 1 2 3 4 5 6 7 8 time (seconds)

28. percept 5000 4500 4000 Frequency (Hz) 3500 Active inference and communication 3000 2500 1 2 3 4 5 6 7 time (sec) First level expectations (hidden states) 100 50 0 -50 0 1 2 3 4 5 6 7 8 time (seconds) Second level expectations (hidden states) 80 60 40 20 0 -20 -40 0 1 2 3 4 5 6 7 8 time (seconds)

29. Mutual prediction and synchronization of chaos No synchronization Synchronization 50 60 40 50 30 40 20 30 second level expectations (second bird) second level expectations (second bird) 10 20 0 10 -10 0 -20 -10 -30 -20 -20 -10 0 10 20 30 40 50 60 -20 -10 0 10 20 30 40 50 60 second level expectations (first bird) second level expectations (first bird) synchronization manifold

30. Hermann von Helmholtz "There is nothing in the nature of music itself to determine the pitch of the tonic of any composition...In short, the pitch of the tonic must be chosen so as to bring the compass of the tones of the piece within the compass of the executants, vocal or instrumental.” (Helmholtz, 1877 p. 310) ‘Helmholtz, H. (1877). “On the Sensations of Tone as a Physiological Basis for the Theory of Music", Fourth German edition,; translated, revised, corrected with notes and additional appendix by Alexander J. Ellis. Reprint: New York, Dover Publications Inc.,1954

31. 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