A proposal for the function of canonical microcircuits André Bastos July 5 th, 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 through recurrent 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

Spread of feedforward activity through the CMC L1 L6 L5 L2/3 4A/B 4Ca/B Extrastriate (V2) PulvinarLGN

Drivers vs. modulators Sherman and Guillery, 1998, 2011 The corticogeniculate feedback connection displays modulatory synaptic characteristics. This suggested that cortico- cortical feedback is also modulatory…

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->A2Feedback 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 feedback Stimulate 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 LGN 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 noise Hidden states What generative model does the brain use??? Advantage: Extremely general models that grandfather most parametric models in statistics and machine learning (e.g., PCA/ICA/State-space models) Friston, 2008

Sensations are caused by a complex world with deep hierarchical structure v2x2v1x1s (state) (cause) (sensation) input Level 1Level 0

A simple example: visual occlusion

Hierarchical causes on sensory data v2x2v1x1s (state) (cause) (sensation) input

Hierarchical generative model Perception entails model inversion Recognition Dynamics Expectations: Prediction errors: Hierarchical generation

Top-down predictions Bottom-up prediction errors Hierarchical generation Mind meets matter… Hierarchical generative modelHierarchical predictive coding

Backward predictions Forward prediction error Backward predictions Forward prediction error Expectations: Prediction errors: Recognition Dynamics Canonical microcircuit for predictive coding

Haeusler and Maass (2006) Canonical microcircuit from predictive coding Backward predictions Forward prediction error Backward predictions Forward prediction error Bastos et al., (in review) Canonical microcircuit from anatomy

Spectral asymmetries between superficial and deep cells Rate of change of units encoding expectation (send feedback) Fourier transform Prediction error units (send feed- forward messages) frequency (Hz) x frequency (Hz) superficial deep

Different oscillatory modes for different layers Buffalo, Fries, et al., (2011) V1 V2V4

Unpublished data We apologize, but cannot share this slide at this point

alpha/beta gamma Integration of top-down and bottom-up through oscillatory modes? ??? prediction errorprecision state higher-level prediction ???

Integration of top-down and bottom-up streams Backward predictions Forward prediction error Backward predictions Forward prediction error prediction errorprecision state higher-level prediction

Canonical microcircuits and DCM Feedback connections Feedforward connections Intrinsic connections V1 (primary visual cortex) local fluctuations V4 (extrastriate visual area)

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