A proposal for the function of canonical microcircuits André Bastos July 5 th, 2012 Free Energy...
-
Upload
fatima-rampley -
Category
Documents
-
view
213 -
download
0
Transcript of A proposal for the function of canonical microcircuits André Bastos July 5 th, 2012 Free Energy...
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
D
D
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
D
D
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 i
v 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