The Anatomy of Active Inference
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Transcript of The Anatomy of Active Inference
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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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 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
SG
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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Friston (2005)
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 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
( ,1)x
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( ,1)v
( ,2)v
( )s t
( ,1)v( ,2)x
( ,2)x
( ,2)v
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L4
SG
IG
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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L4
SG
IG
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
( , )i v
( , ) ( , ) ( 1, ) ( )
( , ) ( , ) ( , ) ( )
( )
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i v i v i v i
i x i x i x i
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( 1) ( 1)i iv
( , ) ( )( )i x iD f ( 1, ) ( )( )i v ig
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
( ,1)v
( ,2)v
( )s t
( ,1)v( ,2)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