The Anatomy of Active Inference

Free Energy WorkshopWTCN, 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?

The functional architecture of predictive coding

Purves et al (2001)

The functional architecture of predictive coding

spiny stellate cells

superficial pyramidal cells

double bouquet cells

deep pyramidal cells

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Friston (2005), Mesulam (1998)

The functional architecture of predictive coding

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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.

Backward predictions

Forward prediction error

L4

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IG

Friston (2005)

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The functional architecture of predictive coding

A predictive coding scheme must have certain properties– Hierarchical organisation with reciprocal connections:

Backward predictions

Forward prediction error

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The functional architecture of predictive coding

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double bouquet cells

deep pyramidal cells

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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

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

Backward predictions

Forward prediction error

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Friston (2005)

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

Backward predictions

Forward prediction error

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Friston (2005)

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

The functional architecture of predictive coding

spiny stellate cells

superficial pyramidal cells

double bouquet cells

deep pyramidal cells

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The functional architecture of predictive coding

A predictive coding scheme must have certain properties– Hierarchical & Reciprocal Laminar & Hierarchical– >Divergent backward connections Topographic– Functionally asymmetrical Pharmacological &

Physiological

Backward predictions

Forward prediction error

SG

IG

L4

Friston (2005)

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Laminar & Hierarchical properties

Rockland & Pandya (1979)

Shipp (2005) Felleman & van Essen (1991)

Laminar & Hierarchical properties

Adams, Shipp & Friston (2012)

Only 5/305 were critically assessed as unreciprocated

Felleman & van Essen (1991)

The functional architecture of predictive coding

A predictive coding scheme must have certain properties– Hierarchical & Reciprocal Laminar & Hierarchical– >Divergent backward connections Topographic– Functionally asymmetrical Pharmacological &

Physiological

Backward predictions

Forward prediction error

SG

IG

L4

Friston (2005)

( ,1)x

( ,1)x

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Topographic propertiesRockland & 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 terminals1-3 arbors per axon (over max 3mm)

Subset of backward connections:Widely distributed wand-like array of synapses

Topographic properties

Level 1 Level 2

Adapted from Zeki & Shipp (1988)

Lemon & Porter (1976)

Shinoda et al (1981)

The functional architecture of predictive coding

A predictive coding scheme must have certain properties– Hierarchical & Reciprocal Laminar & Hierarchical– >Divergent backward connections Topographic– Functionally asymmetrical Pharmacological &

Physiological

Backward predictions

Forward prediction error

SG

IG

L4

Friston (2005)

( ,1)x

( ,1)x

( ,1)v

( ,2)v

( )s t

( ,1)v( ,2)x

( ,2)x

( ,2)v

( ,3)v

The functional architecture of predictive coding

A predictive coding scheme must have certain properties– Hierarchical & Reciprocal Laminar & Hierarchical– >Divergent backward connections Topographic– Functionally asymmetrical Pharmacological &

Physiological

Backward predictions

Forward prediction error

SG

IG

L4

Friston (2005)

( ,1)x

( ,1)x

( ,1)v

( ,2)v

( )s t

( ,1)v( ,2)x

( ,2)x

( ,2)v

( ,3)v

Backward precision

Pharmacological properties

Voglis & Tavernarakis (2006)

Traynelis et al (2010)

Benarroch (2008)

Pharmacological properties

Voglis & Tavernarakis (2006)

Zilles et al (2004)

Zilles et al (1995)

Pharmacological properties

Shima & Tanji (1998)

As

S2

Proprioceptive prediction

Alpha motor neurons report prediction errors that are quashed by movement (gamma motor neurons set gain)

Somatosensory information

Primary sensory afferent

M1

M2

S1

Somatosensory prediction

prediction errors

predictions

CNQX – anti-AMPA/KAAPV – anti-NMDA

Pharmacological properties

Shima & Tanji (1998)

M1

M2

S1

predictions

prediction errors

CNQX – anti-AMPA/KAAPV – anti-NMDA

Physiological properties

Fox et al (1990)

Quis – AMPA-R agonistNMDA – NMDA-R agonist

Larkum et al (2009)

Physiological properties

V3 V5

Hupé et al (1998)

Angelucci & Bullier, 2003

Physiological properties

Hupé et al (1998)

Angelucci & Bullier, 2003

V3 V5

Physiological properties

Hupé et al (1998)

Angelucci & Bullier, 2003

V3 V5

Physiological properties

Olsen et al (2012)

Hupé et al (1998)

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

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

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?

Acknowledgements

Karl FristonStewart ShippKlaas StephanHarriet BrownAndre Bastos