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The Anatomy of Active Inference

The Anatomy of Active Inference. Free Energy Workshop WTCN, July 2012 Rick Adams. What kind of architecture does predictive coding need? Does the cortex have that architecture?. What kind of architecture does predictive coding need? Does the cortex have that architecture?.

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The Anatomy of Active Inference

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  1. The Anatomy of Active Inference Free Energy Workshop WTCN, July 2012 Rick Adams

  2. What kind of architecture does predictive coding need? Does the cortex have that architecture?

  3. What kind of architecture does predictive coding need? Does the cortex have that architecture?

  4. The functional architecture of predictive coding Purves et al (2001)

  5. The functional architecture of predictive coding superficial pyramidal cells double bouquet cells SG spiny stellate cells L4 IG deep pyramidal cells

  6. The functional architecture of predictive coding Friston (2005), Mesulam (1998) superficial pyramidal cells double bouquet cells spiny stellate cells deep pyramidal cells

  7. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical organisation: To invert this generative model, priors are required. These must be learned and adapted, using empirical Bayes, in which state estimates at one level become priors for the level below. Forward prediction error SG L4 IG Backward predictions Friston (2005)

  8. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical organisation with reciprocal connections: Forward prediction error SG L4 IG Backward predictions Friston (2005)

  9. The functional architecture of predictive coding superficial pyramidal cells double bouquet cells spiny stellate cells deep pyramidal cells

  10. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical organisation with reciprocal connections • >Divergent backward (predictive) connections:Free energy = Complexity - Accuracy

  11. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical organisation with reciprocal connections • >Divergent backward (predictive) connections:Free energy = Complexity - Accuracy Forward prediction error SG L4 IG Backward predictions Friston (2005)

  12. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical organisation with reciprocal connections • >Divergent backward (predictive) connections • Functionally asymmetrical: causes interact non-linearly to generate data Forward prediction error SG L4 IG Backward predictions Friston (2005)

  13. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical organisation with reciprocal connections • >Divergent backward (predictive) connections • Functionally asymmetrical: causes interact non-linearly to generate data

  14. The functional architecture of predictive coding superficial pyramidal cells double bouquet cells spiny stellate cells deep pyramidal cells

  15. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical & ReciprocalLaminar & Hierarchical • >Divergent backward connections Topographic • Functionally asymmetricalPharmacological & Physiological Forward prediction error SG L4 IG Backward predictions Friston (2005)

  16. Laminar & Hierarchical properties Rockland & Pandya (1979) Felleman & van Essen (1991) Shipp (2005)

  17. Laminar & Hierarchical properties Adams, Shipp & Friston (2012) Felleman & van Essen (1991) Only 5/305 were critically assessed as unreciprocated

  18. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical & ReciprocalLaminar & Hierarchical • >Divergent backward connections Topographic • Functionally asymmetricalPharmacological & Physiological Forward prediction error SG L4 IG Backward predictions Friston (2005)

  19. Topographic properties Rockland & Drash (1996) • Forward connections: • <3% Area 17 neurons projecting to areas 18, 19, etc bifurcate • Backward connections: • 20-30% axons projecting to Areas 17 & 18 bifurcate Forward connections: Delimited arbors (0.25mm) of <400 terminals 1-3 arbors per axon (over max 3mm) Subset of backward connections: Widely distributed wand-like array of synapses

  20. Topographic properties Lemon & Porter (1976) Level 1 Level 2 Adapted from Zeki & Shipp (1988) Shinoda et al (1981)

  21. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical & ReciprocalLaminar & Hierarchical • >Divergent backward connections Topographic • Functionally asymmetricalPharmacological & Physiological Forward prediction error SG L4 IG Backward predictions Friston (2005)

  22. The functional architecture of predictive coding A predictive coding scheme must have certain properties • Hierarchical & ReciprocalLaminar & Hierarchical • >Divergent backward connections Topographic • Functionally asymmetricalPharmacological & Physiological Forward prediction error SG L4 IG Backward predictions Backward precision Friston (2005)

  23. Pharmacological properties Traynelis et al (2010) Voglis & Tavernarakis (2006) Benarroch (2008)

  24. Pharmacological properties Zilles et al (1995) Voglis & Tavernarakis (2006) Zilles et al (2004)

  25. Pharmacological properties As prediction errors M2 predictions M1 S2 Somatosensory prediction CNQX – anti-AMPA/KA APV – anti-NMDA S1 Proprioceptive prediction Somatosensory information Primary sensory afferent Alpha motor neurons report prediction errors that are quashed by movement (gamma motor neurons set gain) Shima & Tanji (1998)

  26. Pharmacological properties M2 predictions M1 prediction errors CNQX – anti-AMPA/KA APV – anti-NMDA S1 Shima & Tanji (1998)

  27. Physiological properties Larkum et al (2009) Quis – AMPA-R agonist NMDA – NMDA-R agonist Fox et al (1990)

  28. Physiological properties V3 V5 Angelucci & Bullier, 2003 Hupé et al (1998)

  29. Physiological properties V3 V5 Angelucci & Bullier, 2003 Hupé et al (1998)

  30. Physiological properties V3 V5 Angelucci & Bullier, 2003 Hupé et al (1998)

  31. Physiological properties Hupé et al (1998) Olsen et al (2012)

  32. The functional architecture of predictive coding What kind of architecture does predictive coding need? Does the cortex have that architecture? • Hierarchy & reciprocity • Topography • Functional asymmetry of prediction/PE connections

  33. The functional architecture of predictive coding What kind of architecture does predictive coding need? Does the cortex have that architecture? • Hierarchy & reciprocity • Topography • Functional asymmetry of prediction/PE connections • Encoding of precision • Hierarchy of time scales • Neuronal responses • Oscillations • Associative plasticity

  34. Future questions • Do functional DCM hierarchies cohere with anatomical hierarchical predictions? • What about subcortical architecture? • Can prediction/precision roles be divided between NMDA-R/neuromodulators & oscillations or are roles more blurred?i.e. how might NMDA-R pathology affect priors, precisions, and inference?

  35. Acknowledgements Karl Friston Stewart Shipp Klaas Stephan Harriet Brown Andre Bastos

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