Canonical circuits for predictive coding Karl Friston, University College London

1. Canonical circuits for predictive coding Karl Friston, University College London 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 surprise or prediction error that – in the context of perception – corresponds to Bayes-optimal predictive coding (that suppresses exteroceptive prediction errors) and – in the context of action – reduces to classical motor reflexes (that suppress proprioceptive prediction errors). We will look at some of the implications for the anatomy of this active inference, in terms of large-scale anatomical graphs and canonical microcircuits. Specifically, we will look at the functional and anatomical asymmetries in (extrinsic and intrinsic) connections and their implications for spectral responses.

2. Overview The free-energy principleaction and perception predictive coding with reflexes The anatomy of inference graphical models canonical microcircuits Functional asymmetriesextrinsic connections intrinsic connections

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 From the Helmholtz machine to the Bayesian brain and self-organization Thomas Bayes Richard Feynman Ross Ashby

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

5. Prediction errors – the ‘Bayesian thermostat’ Posterior distribution Prior distribution Likelihood distribution 20 40 60 80 100 120 temperature Perception Action

6. Overview The free-energy principleaction and perception predictive coding with reflexes The anatomy of inference graphical models canonical microcircuits Functional asymmetriesextrinsic connections intrinsic connections

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

11. Overview The free-energy principleaction and perception predictive coding with reflexes The anatomy of inference graphical models canonical microcircuits Functional asymmetriesextrinsic connections intrinsic connections

12. Errors (superficial pyramidal cells) Forward transfer function 14 12 10 Expectations (deep pyramidal cells) 8 spectral power 6 Andre Bastos 4 2 0 0 20 40 60 80 100 V4 V1 0.3 0.25 0.2 superficial 0.15 0.1 0.05 0 0 20 40 60 80 100 120 6 5 4 spectral power 3 2 2 1 deep 0 0 20 40 60 80 100 1 frequency (Hz) Backward transfer function 0 0 20 40 60 80 100 120 frequency (Hz)

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

14. Errors (superficial pyramidal cells) Expectations (deep pyramidal cells)  0.3 0.25 0.2 superficial 0.15 0.1 0.05 0 0 20 40 60 80 100 120  2 γ 2 1 deep 1 Nonlinear (cross frequency) coupling 0 0 20 40 60 80 100 120 frequency (Hz) 0 0 20 40 60 80 100 120 frequency (Hz)

15. Off dopamine On dopamine M1 M1   STN STN γ γ M1 M1 STN STN Bernadette Van Wijk

16. 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 cross frequency coupling in the brain.

17. Thank you And thanks to collaborators: Rick Adams Andre Bastos Sven Bestmann Harriet Brown CC Chen Pascal Fries Lee Harrison Stefan Kiebel James Kilner Andre Marreiros Jérémie Mattout Rosalyn Moran Will Penny Klaas Stephan Bernadette Van Wijk And many others