2. Abstract: How much about our interactions 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 free energy, surprise or prediction error that – in the context of perception – corresponds to Bayes-optimal predictive coding. We will look at some of the phenomena that emerge from this principle; such as hierarchical message passing in the brain and the perceptual inference that ensues. I hope to illustrate the ensuing dynamics using generalised synchrony with as a model of communication (bird song). This provides a nice example of how dynamics can be exploited by the brain to represent and predict the sensorium that is – in many instances – generated by ourselves. I hope to conclude with an illustration of the same principles at a microscopic level, using morphogenesis as an example. Key words : active inference ∙ autopoiesis ∙ cognitive ∙ dynamics ∙ free energy ∙ epistemic value ∙ self-organization. Free energy and self organisation Karl Friston, University College London

3. Overview Prediction and life Markov blankets and ergodic systems Simulations of a primordial soup The anatomy of inference Graphical models and predictive coding Canonical microcircuits Action and perception Knowing your place Multi-agent prediction Morphogenesis

4. “How can the events in space and time which take place within the spatial boundary of a living organism be accounted for by physics and chemistry?” (Erwin Schrödinger 1943) The Markov blanket as a statistical boundary (parents, children and parents of children) Internal states External states Sensory states Active states

5. The Markov blanket in biotic systems Active states External states Internal states Sensory states

6. The Fokker-Planck equation And its solution in terms of curl-free and divergence-free components lemma : any (ergodic random) dynamical system ( m ) that possesses a Markov blanket will appear to engage in active inference

7. But what about the Markov blanket? Reinforcement learning, optimal control and expected utility theory Information theory and minimum redundancy Self-organisation, synergetics and homoeostasis Bayesian brain, active inference and predictive coding Value Surprise Entropy Model evidence Pavlov Haken Helmholtz Barlow Perception Action

8. Overview Prediction and life Markov blankets and ergodic systems Simulations of a primordial soup The anatomy of inference Graphical models and predictive coding Canonical microcircuits – in the brain Saccadic searches and salience Knowing your place Multi-agent prediction Morphogenesis – in other animals

9. Position Simulations of a (prebiotic) primordial soup Weak electrochemical attraction Strong repulsion Short-range forces

10. Element Adjacency matrix 20406080100120 20 40 60 80 100 120 Markov Blanket Hidden states Sensory states Active states Internal states Markov Blanket = [ B · [eig(B) > τ]] Markov blanket matrix: encoding the children, parents and parents of children Finding the (principal) Markov blanket A Does action maintain the structural and functional integrity of the Markov blanket ( autopoiesis ) ? Do internal states appear to infer the hidden causes of sensory states ( active inference ) ?

11. Autopoiesis, oscillator death and simulated brain lesions

12. Decoding through the Markov blanket and simulated brain activation Christiaan Huygens

13. The existence of a Markov blanket necessarily implies a partition of states into internal states, their Markov blanket (sensory and active states) and external or hidden states. Because active states change – but are not changed by – external states they minimize the entropy of internal states and their Markov blanket. This means action will appear to maintain the structural and functional integrity of the Markov blanket ( autopoiesis ). Internal states appear to infer the hidden causes of sensory states (by maximizing Bayesian evidence) and influence those causes though action ( active inference ) Interim summary

14. Overview Prediction and life Markov blankets and ergodic systems Simulations of a primordial soup The anatomy of inference Graphical models and predictive coding Canonical microcircuits Action and perception Knowing your place Multi-agent prediction Morphogenesis

15. “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

16. “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 Impressions on the Markov blanket…

17. Bayesian filtering and predictive coding prediction update prediction error

18. Making our own sensations Changing sensations sensations – predictions Prediction error Changing predictions Action Perception

19. the Descending predictions Descending predictions Ascending prediction errors A simple hierarchy whatwhere Sensory fluctuations Hierarchical generative models

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

21. Biological agents minimize their average surprise (entropy) They minimize surprise by suppressing prediction error Prediction error can be reduced by changing predictions (perception) Prediction error can be reduced by changing sensations (action) Perception entails recurrent message passing to optimize predictions Action makes predictions come true (and minimizes surprise) Interim summary

22. Overview Prediction and life Markov blankets and ergodic systems Simulations of a primordial soup The anatomy of inference Graphical models and predictive coding Canonical microcircuits – in the brain Action and perception Knowing your place Multi-agent prediction Morphogenesis – in other animals

23. Thalamus Area X Higher vocal centre Hypoglossal Nucleus creating your own sensations: Listening time (sec) Frequency (Hz) sensations 0.20.40.60.811.21.41.61.8 2500 3000 3500 4000 4500 5000

24. Thalamus Area X Higher vocal centre Hypoglossal Nucleus creating your own sensations: Singing Motor commands (proprioceptive predictions) Corollary discharge ( exteroceptive predictions) time (sec) Frequency (Hz) sensations 0.20.40.60.811.21.41.61.8 2500 3000 3500 4000 4500 5000

25. time (sec) Frequency (Hz) percept 1234567 2500 3000 3500 4000 4500 5000 012345678 -50 0 50 100 time (seconds) First level expectations (hidden states) 012345678 -40 -20 0 20 40 60 80 time (seconds) Second level expectations (hidden states) Singing alone

27. Mutual prediction and synchronization of chaos synchronization manifold

28. Overview Prediction and life Markov blankets and ergodic systems Simulations of a primordial soup The anatomy of inference Graphical models and predictive coding Canonical microcircuits Action and perception Knowing your place Multi-agent prediction Morphogenesis

29. Generative model Intercellular signaling Intrinsic signals Exogenous signals Endogenous signals Knowing your place: multi-agent games and morphogenesis

30. -4-2024 -4 -3 -2 0 1 2 3 4 Extracellular target signal position (Genetic) encoding of target morphology Position (place code) Signal expression (genetic code) -4-2024 -4 -3 -2 0 1 2 3 4 Target form position

31. 51015202530 -1.5 -0.5 0 0.5 1 1.5 2 2.5 3 Expectations time 51015202530 -2 -1.5 -0.5 0 0.5 1 1.5 2 2.5 time Action Morphogenesis

32. Dysmorphogenesis Gradient IntrinsicExtrinsic TARGET

33. Regeneration

34. “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

35. And thanks to collaborators: Rick Adams Ryszard Auksztulewicz Andre Bastos Sven Bestmann Harriet Brown Jean Daunizeau Mark Edwards Chris Frith Thomas FitzGerald Xiaosi Gu Stefan Kiebel James Kilner Christoph Mathys Jérémie Mattout Rosalyn Moran Dimitri Ognibene Sasha Ondobaka Will Penny Giovanni Pezzulo Lisa Quattrocki Knight Francesco Rigoli Klaas Stephan Philipp Schwartenbeck And colleagues: Micah Allen Felix Blankenburg Andy Clark Peter Dayan Ray Dolan Allan Hobson Paul Fletcher Pascal Fries Geoffrey Hinton James Hopkins Jakob Hohwy Mateus Joffily Henry Kennedy Simon McGregor Read Montague Tobias Nolte Anil Seth Mark Solms Paul Verschure And many others Thank you