Rosalyn J. Moran Wellcome Trust Centre for Neuroimaging

1. Precision in Cortical Message Passing Rosalyn J. Moran WellcomeTrust Centre for Neuroimaging 1st Workshop on the Free Energy Principle, ION, UCL, July 5th 2012.

2. Outline Predicting & Estimating Precision under the Free Energy Principle - Laplace and Mean Field Assumptions Hypothesised Neuronal Implementation & the role of Neuromodulators - Gain effects on primary neurotransmission Cholinergic Neuromodulation& Certainty Effects on Auditory mismatch negativity - Theoretical simulation of perception Testing Cholinergic Neuromodulation - DCM characterization of Event Related Responses

3. Outline Predicting & Estimating Precision under the Free Energy Principle - Laplace and Mean Field Assumptions Hypothesised Neuronal Implementation & the role of Neuromodulators - Gain effects on primary neurotransmission Cholinergic Neuromodulation& Certainty Effects on Auditory mismatch negativity - Theoretical simulation of perception Testing Cholinergic Neuromodulation - DCM characterization of Event Related Responses

4. Predicting & Estimating Precision under the Free Energy Principle Hierarchical, Dynamic & Uncertain causes in the environment generate sensory signals Different Levels of the hierarchy and/or different sensory signals may confer more precise Information

5. The Environment Hierarchical, Dynamic

6. The Environment Hierarchical, Dynamic & Uncertain causes generate sensory signals y y

7. The Inversion Estimate: Hierarchical, Dynamic & Uncertainty of sensory signals to minimise the surprise of the sensory signals Minimise Free Energy Minimise Surprise Time averaged Surprise(Ergodicity) MinimiseF at every point in time States, parameters & noise The Brain’s Response to y … A Tractable Problem y y

8. Outline Predicting & Estimating Precision under the Free Energy Principle - Laplace and Mean Field Assumptions Hypothesised Neuronal Implementation & the role of Neuromodulators - Gain effects on primary neurotransmission Cholinergic Neuromodulation& Certainty Effects on Auditory mismatch negativity - Theoretical simulation of perception Testing Cholinergic Neuromodulation - DCM characterization of Event Related Responses

9. Minimising Free Energy The Laplace Assumption: The brain assumes gaussianrandom fluctuations 20 25 1 5 10 15 Smooth noise correlations within levels Markov properties between levels y 0 0 20 20 1 1 5 5 10 10 25 25 15 15 Gradients a function of error terms weighted by the precisions at each level: How might precisions be encoded?

10. Superficial pyramidal cells Deep pyramidal cells Gradients of Free Energy Precision Dependent Backward predictions Forward prediction error Perceiving multiple hierarchical levels together: errors can have a greater or lesser effect y A multiplicative term that stays within levels: Candidate mechanisms: local lateral inhibition & neuromodulators

11. Gain control at superficial pyramidal cells Neuromodulators: Anatomically deployed to provide input in multiple regions EgSarter et al. 2009 Local Glutamate & GABA Long Range Glutamate Diffuse projections Neuromodulators Acetylcholine Dopamine y

12. Gain control at superficial pyramidal cells Neuromodulators: Physiologically equipped to provide gain control Dopaminergic Projections from VTA/SNc Cholinergic Projections from Basal Forebrain Activity at D1 receptors stimulates adenylyl cyclase modulating postsynaptic currents Activity at muscarinic receptors enhances EPSPs through K-current modulation y

13. Dendritic spine Presynaptic terminals Gain control at superficial pyramidal cells Neuromodulators: Physiologically equipped to provide gain control Dopaminergic Projections from VTA/SNc Cholinergic Projections from Basal Forebrain Excitatory (AMPA) receptors Modulatory receptor Inhibitory (GABAA) receptors y error precision Precision-weighted error

14. Outline Predicting & Estimating Precision under the Free Energy Principle - Laplace and Mean Field Assumptions Hypothesised Neuronal Implementation & the role of Neuromodulators - Gain effects on primary neurotransmission Cholinergic Neuromodulation& Certainty Effects on Auditory mismatch negativity - Theoretical simulation of perception Testing Cholinergic Neuromodulation - DCM characterization of Event Related Responses

15. Testing error precision modulation by Acetylcholine:The Framework Simulate Experiment 7 Auditory Stimuli: Pure tones presented in mini-blocks Recognition Dynamics Under Placebo & Cholinergic Enhancement Freq time Mismatch Negativity ~150 ms

16. Testing error precision modulation by Acetylcholine:The Sensory Data Recognition Dynamics There was a particular sound v1 The sound has dynamics determined by properties, Frequency and Amplitude x1 x2 Sensations

17. Testing error precision modulation by Acetylcholine:The Sensory Data C =4 A two level hierarchy Freq time v1 x1 x2 Sensations

18. Testing error precision modulation by Acetylcholine:The Sensory Data A two level hierarchy C = 2 Freq time v1 x1 x2 Sensations

19. Testing error precision modulation by Acetylcholine:The Inversion: assume different precision estimates Freq time Sensations Placebo ACh

20. Testing error precision modulation by Acetylcholine:The Recognition Dynamics under different precision estimates 80 80 d1 d2 d10 60 60 40 40 20 20 time 0 0 Freq -20 -20 Placebo -40 -40 Sensations ACh -60 -60 -80 -80 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Simulated ERP ACh Simulated ERP Placebo Precision weighted PE Time (msec) Time (msec)

21. Testing error precision modulation by Acetylcholine:The MMN itself under different precision estimates 80 80 d1 d2 d10 60 60 40 40 More Certain Environment Until oddball 20 20 Certain Environment Until oddball 0 0 -20 -20 -40 -40 -60 -60 -80 -80 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Simulated ERP ACh Simulated ERP Placebo 25 20 Precision weighted PE 15 Simulated MMN Placebo Simulated MMN ACh(more Precision) 10 Precision weighted PE 5 0 -5 Tone is predicted Tone is predicted Time (msec) Time (msec)

22. Outline Predicting & Estimating Precision under the Free Energy Principle - Laplace and Mean Field Assumptions Hypothesised Neuronal Implementation & the role of Neuromodulators - Gain effects on primary neurotransmission Cholinergic Neuromodulation& Certainty Effects on Auditory mismatch negativity - Theoretical simulation of perception Testing Cholinergic Neuromodulation - DCM characterization of Event Related Responses

23. Testing error precision modulation by Acetylcholine: Real Experiment 7 Auditory Stimuli: Pure tones presented in mini-blocks Under Placebo & Cholinergic Enhancement Freq time Mismatch Negativity ~150 ms

24. Scalp Effects: MMN Simulated MMN Galantamine (more Precision) Simulated MMN Placebo More Certain Environment Until oddball Certain Environment Until oddball Tone is predicted Tone is predicted 2.5 2 1.5 25 1 20 Precision weighted PE Recorded MMN Galantamine Recorded MMN Placebo 0.5 15 * 10 0 * 5 -0.5 channel C21 0 -1 -5 -1.5

25. Superficial pyramidal cells Deep pyramidal cells Physiological & Hierarchical PredictionsRecall: Backward predictions Forward prediction error A multiplicative term that stays within levels: Candidate mechanisms: neuromodulators

26. Gain Modulation at Supragranular Pyramidal Cells Acetylcholine: Where does it affect network processing? What region? What layer? Inhibitory interneuron Superficial pyramidal Forward (Bottom-up) Connection Backward (Top-Down) Connection Gain Modulation at Deep Pyramidal Cells Spiny stellate Deep pyramidal Backward connections IFG IFG MTG MTG A1 A1 Forward connections

27. Acetylcholine: Where does it affect network processing? What region? What layer? Electromagnetic forward model:neural activityEEGMEG LFP Time Domain ERP Data … Hemodynamicforward model:neural activityBOLD Time Domain Data DCM Forward (Bottom-up) Connection Backward (Top-Down) Connection Forward (Bottom-up) Connection Backward (Top-Down) Connection IFG IFG IFG IFG MTG MTG A1 MTG MTG A1 A1 A1 Neural state equation: EEG/MEG fMRI Neural Mass Model complicated neuronal model Fast time scale simple neuronal model Slow time scale

28. Acetylcholine: Where does it affect network processing? DCM for ERPs : Canonical Microcircuit What region? What layer? Inhibitory interneuron Superficial pyramidal Forward (Bottom-up) Connection Backward (Top-Down) Connection Forward (Bottom-up) Connection Backward (Top-Down) Connection Spiny stellate Deep pyramidal Backward connections IFG IFG IFG IFG MTG MTG A1 MTG MTG A1 A1 A1 Forward connections

29. Acetylcholine: Bayesian Model Selection IFG IFG IFG IFG IFG IFG IFG IFG IFG IFG IFG IFG IFG IFG IFG 1000 MTG MTG MTG MTG MTG MTG MTG ∆F = 153 MTG MTG MTG MTG MTG MTG MTG A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 800 600 Intrinsic Modulation (models 1-6); Extrinsic Modulation (models 7-10) Relative Log Model Evidence Model 1 Model 3 400 Model 4 IFG IFG IFG IFG IFG IFG Model 3 200 MTG MTG MTG MTG MTG MTG 0 A1 A1 1A M10 M8 A1 A1 M9 M7 M1 M2 M3 M4 M5 M6 A1 Model 5 Model 6 Model 7 Model 8 MTG MTG MTG MTG A1 A1 Model 2 Model 10 Model 9 Forward Connection Backward Connection

30. Gain Modulation at Supragranular Pyramidal Cells Acetylcholine: Direction of Gain Modulation In A1 Inhibitory interneuron Superficial pyramidal Superficial Pyramidal Cell Gain Spiny stellate 0.06 * Deep pyramidal 0.05 0.04 Backward connections Modulatory Effect of Galantamine 0.03 0.02 0.01 Forward connections Placebo Baseline Galantamine Placebo ACh

31. Summary • Precision estimates enable Bayes optimal perception • Hierarchical inference enables different precision effects at different levels • Precision estimates control the impact of errors in Free Energy minimisation under the Laplace Assumption • Neuromodulators are anatomically & physiologically equipped to signal precision in this scheme • Neuromodulatory systems could control precision at different hierarchical levels • Cholinergic Neuromodulation controls gain in superficial pyramidal cells in early sensory regions; conforming to Free Energy Predictions of enhanced precision on sensory prediction errors

32. Thank You Acknowledgments Karl Friston Ray Dolan KlaasEnno Stephan MkaelSymmonds Nicholas Wright Pablo Campo Methods Group Emotion Group