A proposal for the function of canonical microcircuits

André BastosJuly 5th, 2012

Free Energy Workshop

Outline• Review of canonical (cortical) microcircuitry (CMC)• Role of feedback connections– Driving or modulatory?– Excitatory or inhibitory?

• Recapitulation of free energy principle– Derive the predictive coding CMC

• Empirical vs. predictive coding CMC • Frequency dissociations in the CMC

What does a CMC need to do, in principle?

• Amplify weak inputs from thalamus or other cortical areas – LGN provides only 4% of all synapses in V1 granular layer

• Maintain a balance of excitation and inhibition• Select meaningful signals from a huge number of

inputs (on average 10,000 synapses onto a single PY cell)

• Segregate outputs from and inputs to a cortical column

A first proposal on the CMC

Douglas and Martin, 1991

• Amplify thalamic inputs throughrecurrent connections

• Maintain a balance of exc./inh.• Segregate super/deep

Quantitative study of C2 barrel cortex

Lefort et al., 2009

Information flow summarized

Lefort et al., 2009

www.brainmaps.org

Spread of feedforward activity through the CMC

L1

L6

L5

L2/3

4A/B

4Ca/B

Extrastriate (V2)

Pulvinar LGNLGN

Drivers vs. modulators

Sherman and Guillery, 1998, 2011

The corticogeniculate feedbackconnection displays modulatorysynaptic characteristics.

This suggested that cortico-cortical feedback is alsomodulatory…

The “straw man”• Feedforward connections are driving

– V1 projects monosynaptically to V2, V3, V3a, V4, and MT– In all cases, when V1 is reversibly inactivated, neural activity in

the recipient areas is strongly reduced or silenced (Girard and Bullier, 1989; Girard et al., 1991a, 1991b, 1992, Schmid et al., 2009)

• Feedback connections are modulatory– Synaptic characterization of Layer 6 -> LGN feedback

• Longstanding proposal: corticocortical feedback connections are also modulatory (not an unreasonable assumption)

At least some feedback connections are not just modulatory…

Feedforward connections A1->A2 Feedback connections A2->A1

De Pasquale and Sherman, 2011, Covic and Sherman, 2011

Feedback: inhibitory or excitatory?

• On theoretical grounds, we would predict inhibitory – Higher-order areas predict activity of lower areas.

When activity is predictable it evokes a weaker response due to inhibition induced by higher areas

• Neuroimaging studies (repetition suppression, fMRI, MMN) suggests inhibitory role for feedback

• Electrophysiology with cooling studies are mixed

Olsen et al., 2012

Inhibitory corticogeniculate and intrinsic feedbackStimulate V1 Silence V1

dLGN

Corticocortical feedback targets L1

Shipp, 2007

Inhibitory “hot spot” in L1

Meyer et al., 2011

L1 cells are functionally active and inhibit PY cells in L2/3 and L5/6

Shlosberg et al., 2006

L1

L6

L5

L2/3

4A/B

4Ca/B

www.brainmaps.orgLGN

Higher-order cortex

Spread of feedback activity through the CMC

Anatomical and functional constraints

Predictive coding constraints

??? canonical microcircuit for predictive coding ???

The Free Energy Principle, summarized

• Biological systems are homoeostatic– They minimise the entropy of their states

• Entropy is the average of surprise over time– Biological systems must minimise the surprise associated with their

sensory states at each point in time• In statistics, surprise is the negative logarithm of Bayesian

model evidence– The brain must continually maximise the Bayesian evidence for its

generative model of sensory inputs• Maximising Bayesian model evidence corresponds to Bayesian

filtering of sensory inputs– This is also known as predictive coding

Hierarchical Dynamical Causal Models

Output

Inputs

Observation noise

State noiseHidden states

What generative model does the brain use???

Advantage: Extremely general models that grandfather most parametric modelsin statistics and machine learning (e.g., PCA/ICA/State-space models)

Friston, 2008

Sensations are caused by a complex world with deep hierarchical structure

v2 x2 v1 x1 s

(state) (state)(cause) (cause) (sensation)

input

�̇�1= 𝑓 (𝑥1 ,𝑣1 )+𝑤2𝑣1=𝑔 (𝑥2,𝑣2 )+𝑧 2 𝑠=…

Level 1 Level 0Level 0

A simple example: visual occlusion

A simple example: visual occlusion

Hierarchical causes on sensory data

v2 x2 v1 x1 s

(state) (state)(cause) (cause) (sensation)

input

�̇�2= 𝑓 (𝑥2 ,𝑣2 )+𝑤2𝑣1=𝑔 (𝑥2 ,𝑣2 )+𝑧 2

Hierarchical generative model

Perception entails model inversionRecognition Dynamics

( ) ( ) ( ) ( ) ( 1)

( ) ( ) ( ) ( )

( ) ( ) ( 1) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( ) ( )

( ( , ))

( ( , ))

i i i i iv v v v

i i i ix x x

i i i i i iv v v x v

i i i i i ix x x x v

g

f

DD

D

Expectations:

Prediction errors:

(1)x(1)v(2)x(2)v

(2)x(3)

v (2)v (1)

x (1)v

(0)v

Hierarchical generation

(1)x

(1)x(1)v(2)x(2)v

(2)x(3)

v (2)v (1)

x (1)v

(0)v (1)x (1)

v (2)x (2)

v

(2)x

(3)v

(2)v

(1)x

(1)v

(0)v

Top-down predictions

Bottom-upprediction errors

Hierarchical generation

(1)x

Mind meets matter…Hierarchical generative

modelHierarchical predictive

coding

~𝑠=~𝑣❑(0 )=~𝜇𝑣

(0)

( )iv

( 1)iv

( )iv

( , )v i

( 1)iv

( 1)ig

Backward predictions

Forward prediction error

Backward predictions

Forward prediction error

( )if

( )ig

( )ix

( )iv

( )ix

( )ix

( ) ( ) ( ) ( ) ( 1)

( ) ( ) ( ) ( )

( ) ( ) ( 1) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( ) ( )

( ( , ))

( ( , ))

i i i i iv v v v

i i i ix x x

i i i i i iv v v x v

i i i i i ix x x x v

g

f

DD

D

Expectations:

Prediction errors:

Recognition Dynamics Canonical microcircuit for predictive coding

Haeusler and Maass (2006)

Canonical microcircuit from predictive coding

( )iv

( 1)iv

( )iv

( , )v i

( 1)iv

( 1)ig

Backward predictions

Forward prediction error

Backward predictions

Forward prediction error

( )if

( )ig

( )ix

( )iv

( )ix

( )ix

Bastos et al., (in review)

Canonical microcircuit from anatomy

Spectral asymmetries between superficial and deep cells

Rate of changeof units encodingexpectation (send feedback)

Fourier transform

Prediction errorunits (send feed-forward messages)

0 20 40 60 80 100 1200

0.05

0.1

0.15

0.2

0.25

0.3

frequency (Hz)

0 20 40 60 80 100 1200

1

2

x 10-4

frequency (Hz)

2 2( ) ( 1)2

1( ) ( )i iv v

2( 1) ( )iv

2

1

superficial

deep

( )ix

( )ix

( )ix

( )iv

( )iv

( )iv

( 1)iv

Different oscillatory modes for different layers

Buffalo, Fries, et al., (2011)

V1 V2 V4

Unpublished data We apologize, but cannot share this slide at this point

Unpublished data We apologize, but cannot share this slide at this point

Unpublished data We apologize, but cannot share this slide at this point

Unpublished data We apologize, but cannot share this slide at this point

alpha/betagamma

Integration of top-down and bottom-up through oscillatory modes?

???

prediction error precision state higher-level prediction

𝜉𝑣(𝑖+1)=Π𝑣

❑(𝑖+1 )(𝝁𝒗❑( 𝒊 )−𝒈( 𝒊+𝟏 ))

???

Integration of top-down and bottom-up streams

( )iv

( 1)iv

( )iv

( , )v i

( 1)iv

( 1)ig

Backward predictions

Forward prediction error

Backward predictions

Forward prediction error

( )if

( )ig

( )ix

( )iv

( )ix

( )ix

prediction error precision state higher-level prediction

𝜉𝑣(𝑖+1)=Π𝑣

❑(𝑖+1 )(𝜇𝑣❑(𝑖 )−𝑔 (𝑖+1))

Canonical microcircuits and DCM

Feedback connectionsFeedforward connectionsIntrinsic connections

V1 (primary visual cortex)

( )v( )s

( )x

( )v

( )x

( )v

( )v( )x

local fluctuations local fluctuations

V4 (extrastriate visual area)

Unpublished data We apologize, but cannot share this slide at this point

Unpublished data We apologize, but cannot share this slide at this point

Conclusions• Repeating aspects of cortical circuitry suggest a “canonical microcircuit”

exists to perform generic tasks that are invariant across cortex• Traditional roles for feedback pathways are being challenged by newer

data• Predictive coding offers a clear hypothesis for the role of feedback and

feedforward pathways• Predicts spectral asymmetries which may be important for how areas

communicate• In short: the function of CMCs may be to implement predictive coding in

the brain• These predictions might soon be testable with more biologically

informed (CMC) DCMs

Acknowledgements

• Julien Vezoli• Conrado Bosman, Jan-Mathijs Schoffelen,

Robert Oostenveld• Martin Usrey, Ron Mangun• Pascal Fries• Rosalyn Moran, Vladimir Litvak• Karl Friston

Behaviors of a realistic model for oscillations

• Laminar segregation and independence of gamma and beta rhythms

Roopun 2008

Where do HDMs come from?

Friston 2008