A proposal for the function of canonical microcircuits

1. A proposal for the function of canonical microcircuits André Bastos July 5th, 2012 Free Energy Workshop

2. Outline • Review of canonical (cortical) microcircuitry (CMC) • Role of feedback connections • Driving or modulatory? • Excitatory or inhibitory? • Recapitulation of free energy principle • Derive the predictive coding CMC • Empirical vs. predictive coding CMC • Frequency dissociations in the CMC

3. What does a CMC need to do, in principle? • Amplify weak inputs from thalamus or other cortical areas • LGN provides only 4% of all synapses in V1 granular layer • Maintain a balance of excitation and inhibition • Select meaningful signals from a huge number of inputs (on average 10,000 synapses onto a single PY cell) • Segregate outputs from and inputs to a cortical column

4. A first proposal on the CMC • Amplify thalamic inputs throughrecurrent connections • Maintain a balance of exc./inh. • Segregate super/deep Douglas and Martin, 1991

5. Quantitative study of C2 barrel cortex Lefortet al., 2009

7. Information flow summarized Lefortet al., 2009

8. Spread of feedforward activity through the CMC L1 Extrastriate (V2) L2/3 4A/B 4Ca/B L5 L6 LGN Pulvinar LGN www.brainmaps.org

9. Drivers vs. modulators The corticogeniculate feedbackconnection displays modulatorysynaptic characteristics. This suggested that cortico-cortical feedback is alsomodulatory… Sherman and Guillery, 1998, 2011

10. The “straw man” • Feedforward connections are driving • V1 projects monosynaptically to V2, V3, V3a, V4, and MT • In all cases, when V1 is reversibly inactivated, neural activity in the recipient areas is strongly reduced or silenced (Girard and Bullier, 1989; Girard et al., 1991a, 1991b, 1992, Schmid et al., 2009) • Feedback connections are modulatory • Synaptic characterization of Layer 6 -> LGN feedback • Longstanding proposal: corticocortical feedback connections are also modulatory (not an unreasonable assumption)

11. At least some feedback connections are not just modulatory… Feedforward connections A1->A2 Feedback connections A2->A1 De Pasquale and Sherman, 2011, Covic and Sherman, 2011

12. Feedback: inhibitory or excitatory? • On theoretical grounds, we would predict inhibitory • Higher-order areas predict activity of lower areas. When activity is predictable it evokes a weaker response due to inhibition induced by higher areas • Neuroimaging studies (repetition suppression, fMRI, MMN) suggests inhibitory role for feedback • Electrophysiology with cooling studies are mixed

13. Inhibitory corticogeniculate and intrinsic feedback Silence V1 Stimulate V1 dLGN Olsen et al., 2012

14. Corticocortical feedback targets L1 Shipp, 2007

15. Inhibitory “hot spot” in L1 Meyer et al., 2011

16. L1 cells are functionally active and inhibit PY cells in L2/3 and L5/6 Shlosberg et al., 2006

17. Spread of feedback activity through the CMC L1 Higher-order cortex L2/3 4A/B 4Ca/B L5 L6 LGN www.brainmaps.org

18. Anatomical and functional constraints ??? canonical microcircuit for predictive coding ??? Predictive coding constraints

19. The Free Energy Principle, summarized • Biological systems are homoeostatic • They minimise the entropy of their states • Entropy is the average of surprise over time • Biological systems must minimise the surprise associated with their sensory states at each point in time • In statistics, surprise is the negative logarithm of Bayesian model evidence • The brain must continually maximise the Bayesian evidence for its generative model of sensory inputs • Maximising Bayesian model evidence corresponds to Bayesian filtering of sensory inputs • This is also known as predictive coding

20. Hierarchical Dynamical Causal Models What generative model does the brain use??? Advantage: Extremely general models that grandfather most parametric modelsin statistics and machine learning (e.g., PCA/ICA/State-space models) Observation noise Output Hidden states State noise Inputs Friston, 2008

21. Sensations are caused by a complex world with deep hierarchical structure input Level 0 Level 1 Level 0 (cause) (state) (cause) (state) (sensation)

22. A simple example: visual occlusion

23. A simple example: visual occlusion

24. Hierarchical causes on sensory data input (cause) (state) (cause) (state) (sensation)

25. Perception entails model inversion Hierarchical generative model Recognition Dynamics Expectations: Prediction errors: Hierarchical generation

26. Mind meets matter… Hierarchical generative model Hierarchical predictive coding Bottom-up prediction errors Hierarchical generation Top-down predictions

27. Canonical microcircuit for predictive coding Recognition Dynamics Forward prediction error Backward predictions Expectations: Forward prediction error Prediction errors: Backward predictions

28. Canonical microcircuit from anatomy Canonical microcircuit from predictive coding Forward prediction error Backward predictions Forward prediction error Backward predictions Haeusler and Maass (2006) Bastos et al., (in review)

29. Spectral asymmetries between superficial and deep cells Rate of changeof units encodingexpectation (send feedback) Prediction errorunits (send feed-forward messages) 0.3 0.25 0.2 superficial 0.15 0.1 0.05 0 0 20 40 60 80 100 120 frequency (Hz) -4 x 10 Fourier transform 2 deep 1 0 0 20 40 60 80 100 120 frequency (Hz)

30. Different oscillatory modes for different layers V1 V2 V4 Buffalo, Fries, et al., (2011)

31. Unpublished data We apologize, but cannot share this slide at this point

32. Unpublished data We apologize, but cannot share this slide at this point

33. Unpublished data We apologize, but cannot share this slide at this point

34. Unpublished data We apologize, but cannot share this slide at this point

35. Integration of top-down and bottom-up through oscillatory modes? gamma alpha/beta ??? ??? prediction error precision state higher-level prediction

36. Integration of top-down and bottom-up streams Forward prediction error Backward predictions Forward prediction error Backward predictions prediction error precision state higher-level prediction

37. Canonical microcircuits and DCM V1 (primary visual cortex) V4 (extrastriate visual area) Feedback connections Feedforward connections Intrinsic connections local fluctuations local fluctuations

38. Unpublished data We apologize, but cannot share this slide at this point

39. Unpublished data We apologize, but cannot share this slide at this point

40. Conclusions • Repeating aspects of cortical circuitry suggest a “canonical microcircuit” exists to perform generic tasks that are invariant across cortex • Traditional roles for feedback pathways are being challenged by newer data • Predictive coding offers a clear hypothesis for the role of feedback and feedforward pathways • Predicts spectral asymmetries which may be important for how areas communicate • In short: the function of CMCs may be to implement predictive coding in the brain • These predictions might soon be testable with more biologically informed (CMC) DCMs

41. Acknowledgements • Julien Vezoli • Conrado Bosman, Jan-Mathijs Schoffelen, Robert Oostenveld • Martin Usrey, Ron Mangun • Pascal Fries • Rosalyn Moran, Vladimir Litvak • Karl Friston

42. Behaviors of a realistic model for oscillations • Laminar segregation and independence of gamma and beta rhythms Roopun 2008

43. Where do HDMs come from? Friston 2008