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Precision in Cortical Message Passing. Rosalyn J. Moran Wellcome Trust Centre for Neuroimaging 1 st Workshop on the Free Energy Principle, ION, UCL, July 5 th 2012. Outline. Predicting & Estimating Precision under the Free Energy Principle - Laplace and Mean Field Assumptions

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Rosalyn J. Moran Wellcome Trust Centre for Neuroimaging

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Precision in Cortical Message Passing

Rosalyn J. Moran

WellcomeTrust Centre for Neuroimaging

1st Workshop on the Free Energy Principle, ION, UCL, July 5th 2012.


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


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


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


The Environment

Hierarchical, Dynamic


The Environment

Hierarchical, Dynamic & Uncertain causes generate sensory signals

y

y


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


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


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?


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


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


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


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


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


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


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


Testing error precision modulation by Acetylcholine:The Sensory Data

C =4

A two level hierarchy

Freq

time

v1

x1

x2

Sensations


Testing error precision modulation by Acetylcholine:The Sensory Data

A two level hierarchy

C = 2

Freq

time

v1

x1

x2

Sensations


Testing error precision modulation by Acetylcholine:The Inversion: assume different precision estimates

Freq

time

Sensations

Placebo

ACh


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)


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)


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


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


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


Superficial pyramidal cells

Deep pyramidal cells

Physiological & Hierarchical PredictionsRecall:

Backward predictions

Forward prediction error

A multiplicative term that stays within levels:

Candidate mechanisms: neuromodulators


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


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


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


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


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


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


  • Thank You

    Acknowledgments

    Karl Friston

    Ray Dolan

    KlaasEnno Stephan

    MkaelSymmonds

    Nicholas Wright

    Pablo Campo

    Methods Group

    Emotion Group


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