Precision in Cortical Message Passing
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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

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

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


Outline1

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

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

The Environment

Hierarchical, Dynamic


The environment1

The Environment

Hierarchical, Dynamic & Uncertain causes generate sensory signals

y

y


The inversion

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


Outline2

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

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?


Gradients of free energy precision dependent

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

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 cells1

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


Gain control at superficial pyramidal cells2

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


Outline3

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

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

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


Rosalyn j moran wellcome trust centre for neuroimaging

Testing error precision modulation by Acetylcholine:The Sensory Data

C =4

A two level hierarchy

Freq

time

v1

x1

x2

Sensations


Rosalyn j moran wellcome trust centre for neuroimaging

Testing error precision modulation by Acetylcholine:The Sensory Data

A two level hierarchy

C = 2

Freq

time

v1

x1

x2

Sensations


Rosalyn j moran wellcome trust centre for neuroimaging

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

Freq

time

Sensations

Placebo

ACh


Rosalyn j moran wellcome trust centre for neuroimaging

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)


Rosalyn j moran wellcome trust centre for neuroimaging

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)


Outline4

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

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

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


Physiological hierarchical predictions recall

Superficial pyramidal cells

Deep pyramidal cells

Physiological & Hierarchical PredictionsRecall:

Backward predictions

Forward prediction error

A multiplicative term that stays within levels:

Candidate mechanisms: neuromodulators


Acetylcholine where does it affect n etwork processing

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

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 processing1

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

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


Acetylcholine direction of gain modulation

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


Rosalyn j moran wellcome trust centre for neuroimaging

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

    Thank You

    Acknowledgments

    Karl Friston

    Ray Dolan

    KlaasEnno Stephan

    MkaelSymmonds

    Nicholas Wright

    Pablo Campo

    Methods Group

    Emotion Group


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