Anatomical Background of Dynamic Causal Modeling and Effective Connectivity Analyses
|
|
- Leslie Gordon
- 5 years ago
- Views:
Transcription
1 Anatomical Background of Dynamic Causal Modeling and Effective Connectivity Analyses Jakob Heinzle Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering University and ETH Zürich HBM 24, Educational Course: Anatomy Outline Relation between structure and function Effective connectivity Dynamic causal modeling (DCM) Anatomical Background for DCM 2 Connectional fingerprints determine local function Unique anatomical connectivity patterns (connectional fingerprints) for cortical areas Families of cortical areas (clusters) with similar patterns Analogous results for electrophysiological response patterns Anatomical connectivity is the major determinant for the response profile of neuronal ensembles. Passingham et al., Nat Rev Neurosci, 22 Anatomical Background for DCM 3
2 Anatomical connections define processing hierarchies Markov & Kennedy, TICS, 24 Literature based database: Stephan et al., Phil. Trans. B, 2; Kötter, Neuroinformatics, 24 Felleman & Van Essen, Cereb Cortex, 99 Hilgetag et al, Phil Trans. B, 2 Anatomical Background for DCM 4 Weighted and directed connectivity matrix in macaque From area Fraction of labeled neurons per area weight Markov et al., Cereb Cortex, 22; J Comp Neurol 24; To area Anatomical Background for DCM 5 Activation, intrinsic functional connectivity and anatomy Spontaneous correlation pattern Evoked response pattern: eye movement task Anatomical connectivity pattern Vincent et al, Nature, 27 Anatomical Background for DCM 6 2
3 Tight link between functional and anatomical connectivity - human fmri Average CCRF Intrinsic functional connectivity in humans is visuotopically organized matches monkey anatomy! Heinzle et al, Neuroimage, 2 Anatomical Background for DCM 7 Some naming conventions Anatomical connectivity - Fibre bundles, Close Contacts, Synapses Functional connectivity - Statistical relation, e.g. Correlation, Mutual information Effective connectivity - Directed influence, e.g. DCM, Transfer Entropy, Granger Causality Anatomical Background for DCM 8 Outline Relation between structure and function Effective connectivity Dynamic causal modeling (DCM) Anatomical Background for DCM 9 3
4 Why effective connectivity? Anatomical connectivity is critical for understanding brain function but not sufficient on its own. Functional connections synapses Context dependent modulation of connection strengths, synaptic plasticity, neuronal adaptation mechanisms, etc. Anatomical Background for DCM Synaptic connections show plasticity Numerous mechanisms at different time scales (ms to days) incl. very rapid changes! Regulated in several ways (e.g. modulatory effects of DA) peak PSP (mv) Matsuzaki et al., Nature, 24 Reynolds et al., Nature, 2 Anatomical Background for DCM Connections are recruited in a contextdependent fashion Synaptic strengths are context-sensitive: They depend on the spatio-temporal distribution of presynaptic inputs and post-synaptic events. Anatomical Background for DCM 2 4
5 To understand brain (dys)function... we need models of effective connectivity that: incorporate anatomical and physiological principles connect these to computational mechanisms allow for inference on neuronal mechanisms (e.g., synaptic plasticity) from measured brain responses Anatomical Background for DCM 3 Outline Relation between structure and function Effective connectivity Dynamic causal modeling (DCM) Anatomical Background for DCM 4 Dynamic causal modeling (DCM) EEG, MEG fmri Forward model: Predicting measured activity given a putative neuronal state y g(x, ) Model inversion: Estimating neuronal mechanisms from brain activity measures dx f( x, u, ) dt David et al., Neuroimage, 26 Moran et al., NeuroImage, 28 Friston et al., Neuroimage, 23 Stephan et al., Neuroimage, 28 Stephan et al., Neuroimage, 2 Anatomical Background for DCM 5 5
6 Nonlinear DCM for fmri neural model u u 2 x 3 Neural population activity dx A dt m ( i) u B n x x 2 ( x D i j i j j) x Cu Weights are constrained / reflect by anatomy fmri signal change (%) Stephan et al., Neuroimage, 28 Anatomical Background for DCM 6 Hemodynamics DCM forward model neural state equation u stimulus functions t m dx ( j) A u jb x Cu dt j.4.2 vasodilatory signal s x s γ( f ) s f s flow induction (rcbf) f hemodynamic s f state equations Balloon model changes in volume v changes in dhb /α /α τv f v τq f E ( f,e ) qe v q/v v q RBM N, =.5 CBM N, =.5 RBM N, = CBM N, =.5 RBM N, = 2 CBM N, = S q ( q, v) V k q k2 k3 v S v k 4.3 ETE k2 r ETE BOLD signal k3 change equation Stephan et al., Neuroimage, 27 Anatomical Background for DCM 7 Conductance-based DCMs (for EEG) inhibitory interneurons pyramid al cells spiny stellate cells pyramid al cells pyramidal cells CV g ( VL V ) g ( VE V ) g f ( V )( VE V ) V L E NMDA Mg g E ( E) 2,3 V E E( ( VR g, ) ) E g NMDA ( I ) 2,3 V NMDA NMDA( ( VR g, ) ) NMDA ( I ) 3,2 () () () () () () ( V ) ( V ) ( V ) CV g V g V g V u V L L E E I I g ( E) () E(,3 ( V VR, ) ge ) () E E g ( E) () I(,2 ( V VR, ) gi ) () I I ( I ),2 ( E ),3 ( E ) 3, ( E ) 2,3 CV g V V i i i CV g ( VL V ) g ( VE V ) g ( VI V ) g f ( V )( VE V ) V L E I NMDA Mg g ( E) () () E( 3, ( V VR, ) ge ) E E g ( I ) I( 3,2( V VR, ) gi ) I I g NMDA ( I ) () () ( 3, ( V VR, ) g ) NMDA NMDA NMDA Marreiros et al., Neuroimage, 2 Moran et al., Neuroimage, 2 Anatomical Background for DCM 8 6
7 Conductance-based DCMs (for EEG) inhibitory interneurons pyramid al cells spiny stellate cells pyramid al cells pyramidal cells CV g ( VL V ) g ( VE V ) g f ( V )( VE V ) V L E NMDA Mg g E ( E) 2,3 V E E( ( VR g, ) ) E g NMDA ( I ) 2,3 V NMDA NMDA( ( VR g, ) ) NMDA ( I ) 3,2 () () () () () () ( V ) ( V ) ( V ) CV g V g V g V u V L L E E I I g ( E) () E(,3 ( V VR, ) ge ) () E E g ( E) () I(,2 ( V VR, ) gi ) () I I ( I ),2 ( E ),3 ( E ) 3, ( E ) 2,3 CV g V V i i i CV g ( VL V ) g ( VE V ) g ( VI V ) g f ( V )( VE V ) V L E I NMDA Mg g ( E) () () E( 3, ( V VR, ) ge ) E E g ( I ) I( 3,2( V VR, ) gi ) I I g NMDA ( I ) () () ( 3, ( V VR, ) g ) NMDA NMDA NMDA Weights are constrained by anatomy Marreiros et al., Neuroimage, 2 Moran et al., Neuroimage, 2 Anatomical Background for DCM 9 Generative models, model selection and model validation Any given DCM = a particular generative model of how the measured data (may) have been caused Model selection = hypothesis testing = comparing competing models (i.e. different ideas about mechanisms underlying observed data) Evaluate the relative plausibility of competing explanations for an established effect (e.g., activation) Careful definition of model (hypothesis) space crucial! model selection model validation! Model validation requires external criteria (external to the measured data) Anatomical Background for DCM 2 Bayesian model selection (BMS) Model evidence: p( ym ) py (, mp ) ( md ) Gharamani, 24 log pym ( ) log py (, m),,, KLq p m KLq p ym p(y m) y all possible datasets accounts for both accuracy and complexity of the model a measure of how well the model generalizes Various approximations, e.g.: - negative free energy, AIC, BIC McKay, Neural Comp, 992 Penny et al., Neuroimage, 24 Anatomical Background for DCM 2 7
8 m: a=-32,b=-32.5 m: a=-8,b=-24.5 m9: a=-4,b=-2.5 m28: a=,b=-6.5 m37: a=,b=2.5 m46: a=4,b=6.5 m55: a=8,b= % v % v % v % v m 2: a=-6,b=-32.5 m : a=-8,b=-2.5 m 2: a=-4,b=-8.5 m 29: a=,b=-2.5 m 38: a=,b=24.5 m 47: a=4,b=2.5 m 56: a=8,b= m3: a=-6,b=-28.5 m2: a=-8,b=-6.5 m 2: a=-4,b=-4.5 m 3: a=,b=-8.5 m39: a=,b=28.5 m48: a=4,b=24.5 m 57: a=2,b= m 4: a=-2,b=-32.5 m3: a=-8,b=-2.5 m 22: a=-4,b=.5 m 3: a=,b=-4.5 m 4: a=,b=32.5 m 49: a=4,b=28.5 m58: a=2,b= m 5: a=-2,b=-28.5 m 4: a=-4,b=-32.5 m 23: a=-4,b=4.5 m 32: a=,b=.5 m 4: a=4,b=-32.5 m5: a=4,b=32.5 m 59: a=2,b= m 6: a=-2,b=-24.5 m5: a=-4,b=-28.5 m 24: a=,b=-32.5 m 33: a=,b=4.5 m 42: a=4,b=.5 m 5: a=8,b=2.5 m6: a=2,b= m7: a=-2,b=-2.5 m 6: a=-4,b=-24.5 m 25: a=,b=-28.5 m34: a=,b=8.5 m43: a=4,b=4.5 m 52: a=8,b=6.5 m 6: a=6,b= m8: a=-8,b=-32.5 m 7: a=-4,b=-2.5 m 26: a=,b=-24.5 m 35: a=,b=2.5 m44: a=4,b=8.5 m 53: a=8,b=2.5 m62: a=6,b= m9: a=-8,b=-28.5 m 8: a=-4,b=-6.5 m 27: a=,b=-2.5 m 36: a=,b=6.5 m 45: a=4,b=2.5 m 54: a=8,b=24.5 m 63 & m 64.5 Examples for the use of DCM Anatomical priors for DCM for fmri Modulation of connectivity by prediction errors Conductance based DCM if time permits: DCM validation in patients or layered DCM Anatomical Background for DCM 22 Anatomically informed priors DCM FG FG probabilistic tractography FG left % FG right 3 5.7% % LG LG LG left % LG right connectionspecific priors for coupling parameters anatomical probability Stephan et al., Neuroimage, 29 Anatomical Background for DCM 23 Anatomically informed priors R R R 2 R 2 Models with anatomically informed priors were clearly superior : Bayes Factor > 9 Posterior model probabilities prior variance anatomical probability Stephan et al., Neuroimage, 29 Stephan et al., Neuroimage, 29 Anatomical Background for DCM 24 8
9 Prediction errors drive synaptic plasticity PE(t) R x 3 a McLaren 989 x x 2 t t k k t w w PE w a PE( ) d synaptic plasticity during learning = f (prediction error) t Anatomical Background for DCM 25 Learning of dynamic audio-visual associations Model behavior with a hierarchical Bayesian Model estimate of prediction errors PE (Behrens et al., Nat Neurosci, 27) den Ouden et al., J Neurosci, 2 Anatomical Background for DCM 26 Prediction error (PE) activity in the putamen PE during active sensory learning PE during incidental sensory learning p <.5 (SVC) den Ouden et al., Cereb Cortex, 29 PE during RF learning O'Doherty et al., Science, 24 PE = teaching signal for synaptic plasticity during learning Could the putamen be regulating trial-by-trial changes of task-relevant connections? Anatomical Background for DCM 27 9
10 PE control plasticity during adaptive cognition Hierarchical Bayesian learning model Influence of visual areas on premotor cortex: stronger for surprising stimuli weaker for expected stimuli PUT p =. p =.7 PMd PPA FFA den Ouden et al., J Neurosci, 2 ongoing pharmacological and genetic studies Anatomical Background for DCM 28 What is a good model? essentially, all models are wrong, but some are useful. George E.P. Box Anatomical Background for DCM 29 Strategies for model validation in silico humans animals & humans patients numerical analysis & simulation studies experiments of known cognitive/neurophys. processes experimentally controlled system perturbations clinical facts Examples: Validation of DCM in animal studies: infers site of seizure origin (David et al. 28) infers primary recipient of vagal nerve stimulation (Reyt et al. 2) infers synaptic changes as predicted by microdialysis (Moran et al. 28) infers fear conditioning induced plasticity in amygdala (Moran et al. 29) tracks anaesthesia levels (Moran et al. 2) predicts sensory stimulation (Brodersen et al. 2) Anatomical Background for DCM 3
11 Dopaminergic modulation of AMPA/NMDA receptors a AMPA NMDA GABA exogenous inputs to layer IV NMDA inputs to layer III pyramidal cells and interneurons Moran et al., Curr Biol, 2 Anatomical Background for DCM 3 Estimates of AMPA/NMDA correlate with behaviour Moran et al., Curr Biol, 2 Anatomical Background for DCM 32 Validating models against clinical facts model of neuronal (patho)physiology individual diagnosis predicting individual outcome individual therapeutic response Brodersen et al., PLoS Comp Biol, 2; Brodersen et al, Neuroimage Clin. 23 Anatomical Background for DCM 33
12 Summary Anatomical connectivity information is important, but not everything Models of effective connectivity neural system mechanisms can be inferred from neuroimaging data DCM is one (not the only) method for this: Neuronal interactions are modeled at the hidden neuronal level Bayesian system identification method Key role for model selection Can be integrated with measures of anatomical connectivity Can be integrated with computational models Validation is critical (for any modeling approach) Anatomical Background for DCM 34 Acknowledgments TNU-Team E. Aponte T. Baumgartner D. Cole A. Diaconescu S. Grässli H. Haker Rössler Q. Huys S. Iglesias L. Kasper E. Lomakina C. Mathys S. Paliwal F. Petzschner S. Princz S. Raman D. Renz G. Stefanics K.E. Stephan L. Weber T. Wentz S. Wilde Collaborators J.-D. Haynes, Berlin T. Kahnt, Chicago H. den Ouden, Nijmegen P. Koopmans, Oxford K.A.C. Martin, Zürich Many thanks to K.E. Stephan for sharing slides. Translational Neuromodeling Unit Anatomical Background for DCM 35 2
Dynamic causal modeling for fmri
Dynamic causal modeling for fmri Methods and Models for fmri, HS 2015 Jakob Heinzle Structural, functional & effective connectivity Sporns 2007, Scholarpedia anatomical/structural connectivity - presence
More informationEffective Connectivity & Dynamic Causal Modelling
Effective Connectivity & Dynamic Causal Modelling Hanneke den Ouden Donders Centre for Cognitive Neuroimaging Radboud University Nijmegen Advanced SPM course Zurich, Februari 13-14, 2014 Functional Specialisation
More informationDynamic Causal Modelling for EEG/MEG: principles J. Daunizeau
Dynamic Causal Modelling for EEG/MEG: principles J. Daunizeau Motivation, Brain and Behaviour group, ICM, Paris, France Overview 1 DCM: introduction 2 Dynamical systems theory 3 Neural states dynamics
More informationDynamic causal modeling for fmri
Dynamic causal modeling for fmri Methods and Models for fmri, HS 2016 Jakob Heinzle Structural, functional & effective connectivity Sporns 2007, Scholarpedia anatomical/structural connectivity - presence
More informationDynamic Causal Modelling for fmri
Dynamic Causal Modelling for fmri André Marreiros Friday 22 nd Oct. 2 SPM fmri course Wellcome Trust Centre for Neuroimaging London Overview Brain connectivity: types & definitions Anatomical connectivity
More informationDCM for fmri Advanced topics. Klaas Enno Stephan
DCM for fmri Advanced topics Klaas Enno Stephan Overview DCM: basic concepts Evolution of DCM for fmri Bayesian model selection (BMS) Translational Neuromodeling Dynamic causal modeling (DCM) EEG, MEG
More informationDCM: Advanced topics. Klaas Enno Stephan. SPM Course Zurich 06 February 2015
DCM: Advanced topics Klaas Enno Stephan SPM Course Zurich 06 February 205 Overview DCM a generative model Evolution of DCM for fmri Bayesian model selection (BMS) Translational Neuromodeling Generative
More informationDynamic Causal Modelling for EEG and MEG. Stefan Kiebel
Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Overview 1 M/EEG analysis 2 Dynamic Causal Modelling Motivation 3 Dynamic Causal Modelling Generative model 4 Bayesian inference 5 Applications Overview
More informationDynamic Causal Modelling for EEG and MEG
Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Ma Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany Overview 1 M/EEG analysis 2 Dynamic Causal Modelling Motivation 3 Dynamic
More informationExperimental design of fmri studies
Experimental design of fmri studies Zurich SPM Course 2016 Sandra Iglesias Translational Neuromodeling Unit (TNU) Institute for Biomedical Engineering (IBT) University and ETH Zürich With many thanks for
More informationDynamic Modeling of Brain Activity
0a Dynamic Modeling of Brain Activity EIN IIN PC Thomas R. Knösche, Leipzig Generative Models for M/EEG 4a Generative Models for M/EEG states x (e.g. dipole strengths) parameters parameters (source positions,
More informationWill Penny. 21st April The Macroscopic Brain. Will Penny. Cortical Unit. Spectral Responses. Macroscopic Models. Steady-State Responses
The The 21st April 2011 Jansen and Rit (1995), building on the work of Lopes Da Sliva and others, developed a biologically inspired model of EEG activity. It was originally developed to explain alpha activity
More informationDynamic Causal Modelling : Advanced Topics
Dynamic Causal Modelling : Advanced Topics Rosalyn Moran Virginia Tech Carilion Research Institute Department of Electrical & Computer Engineering, Virginia Tech Zurich SPM Course, Feb 19 th 2016 Overview
More informationDynamic Causal Modelling for evoked responses J. Daunizeau
Dynamic Causal Modelling for evoked responses J. Daunizeau Institute for Empirical Research in Economics, Zurich, Switzerland Brain and Spine Institute, Paris, France Overview 1 DCM: introduction 2 Neural
More informationExperimental design of fmri studies
Experimental design of fmri studies Sandra Iglesias Translational Neuromodeling Unit University of Zurich & ETH Zurich With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian
More informationExperimental design of fmri studies
Experimental design of fmri studies Sandra Iglesias With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian Ruff SPM Course 2015 Overview of SPM Image time-series Kernel
More informationDynamic causal models of neural system dynamics:current state and future extensions.
Dynamic causal models of neural system dynamics:current state and future extensions. Klaas Stephan, Lee Harrison, Stefan Kiebel, Olivier David, Will Penny, Karl Friston To cite this version: Klaas Stephan,
More informationDynamic Causal Models
Dynamic Causal Models V1 SPC Will Penny V1 SPC V5 V5 Olivier David, Karl Friston, Lee Harrison, Andrea Mechelli, Klaas Stephan Wellcome Department of Imaging Neuroscience, ION, UCL, UK. Mathematics in
More informationPrinciples of DCM. Will Penny. 26th May Principles of DCM. Will Penny. Introduction. Differential Equations. Bayesian Estimation.
26th May 2011 Dynamic Causal Modelling Dynamic Causal Modelling is a framework studying large scale brain connectivity by fitting differential equation models to brain imaging data. DCMs differ in their
More informationExperimental design of fmri studies & Resting-State fmri
Methods & Models for fmri Analysis 2016 Experimental design of fmri studies & Resting-State fmri Sandra Iglesias With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian
More informationHierarchy. Will Penny. 24th March Hierarchy. Will Penny. Linear Models. Convergence. Nonlinear Models. References
24th March 2011 Update Hierarchical Model Rao and Ballard (1999) presented a hierarchical model of visual cortex to show how classical and extra-classical Receptive Field (RF) effects could be explained
More informationCausal modeling of fmri: temporal precedence and spatial exploration
Causal modeling of fmri: temporal precedence and spatial exploration Alard Roebroeck Maastricht Brain Imaging Center (MBIC) Faculty of Psychology & Neuroscience Maastricht University Intro: What is Brain
More informationModels of effective connectivity & Dynamic Causal Modelling (DCM)
Models of effective connectivit & Dnamic Causal Modelling (DCM Presented b: Ariana Anderson Slides shared b: Karl Friston Functional Imaging Laborator (FIL Wellcome Trust Centre for Neuroimaging Universit
More informationExperimental design of fmri studies
Methods & Models for fmri Analysis 2017 Experimental design of fmri studies Sara Tomiello With many thanks for slides & images to: Sandra Iglesias, Klaas Enno Stephan, FIL Methods group, Christian Ruff
More informationNCNC FAU. Modeling the Network Architecture of the Human Brain
NCNC 2010 - FAU Modeling the Network Architecture of the Human Brain Olaf Sporns Department of Psychological and Brain Sciences Indiana University, Bloomington, IN 47405 http://www.indiana.edu/~cortex,
More informationStochastic Dynamic Causal Modelling for resting-state fmri
Stochastic Dynamic Causal Modelling for resting-state fmri Ged Ridgway, FIL, Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London Overview Connectivity in the brain Introduction to
More informationModels of effective connectivity & Dynamic Causal Modelling (DCM)
Models of effective connectivit & Dnamic Causal Modelling (DCM) Slides obtained from: Karl Friston Functional Imaging Laborator (FIL) Wellcome Trust Centre for Neuroimaging Universit College London Klaas
More informationModelling temporal structure (in noise and signal)
Modelling temporal structure (in noise and signal) Mark Woolrich, Christian Beckmann*, Salima Makni & Steve Smith FMRIB, Oxford *Imperial/FMRIB temporal noise: modelling temporal autocorrelation temporal
More informationDynamic causal models of neural system dynamics. current state and future extensions
Dynamic causal models of neural system dynamics: current state and future etensions Klaas E. Stephan 1, Lee M. Harrison 1, Stefan J. Kiebel 1, Olivier David, Will D. Penny 1 & Karl J. Friston 1 1 Wellcome
More informationMIXED EFFECTS MODELS FOR TIME SERIES
Outline MIXED EFFECTS MODELS FOR TIME SERIES Cristina Gorrostieta Hakmook Kang Hernando Ombao Brown University Biostatistics Section February 16, 2011 Outline OUTLINE OF TALK 1 SCIENTIFIC MOTIVATION 2
More informationClicker Question. Discussion Question
Connectomics Networks and Graph Theory A network is a set of nodes connected by edges The field of graph theory has developed a number of measures to analyze networks Mean shortest path length: the average
More informationDiscovering the Human Connectome
Networks and Complex Systems 2012 Discovering the Human Connectome Olaf Sporns Department of Psychological and Brain Sciences Indiana University, Bloomington, IN 47405 http://www.indiana.edu/~cortex, osporns@indiana.edu
More informationNeuroImage. Tractography-based priors for dynamic causal models
NeuroImage 47 (2009) 1628 1638 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Tractography-based priors for dynamic causal models Klaas Enno Stephan
More informationDecision-making and Weber s law: a neurophysiological model
European Journal of Neuroscience, Vol. 24, pp. 901 916, 2006 doi:10.1111/j.14-9568.2006.04940.x Decision-making and Weber s law: a neurophysiological model Gustavo Deco 1 and Edmund T. Rolls 2 1 Institucio
More informationLayer 3 patchy recurrent excitatory connections may determine the spatial organization of sustained activity in the primate prefrontal cortex
Neurocomputing 32}33 (2000) 391}400 Layer 3 patchy recurrent excitatory connections may determine the spatial organization of sustained activity in the primate prefrontal cortex Boris S. Gutkin *, G. Bard
More informationBayesian modeling of learning and decision-making in uncertain environments
Bayesian modeling of learning and decision-making in uncertain environments Christoph Mathys Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK Wellcome Trust Centre for
More informationSampling-based probabilistic inference through neural and synaptic dynamics
Sampling-based probabilistic inference through neural and synaptic dynamics Wolfgang Maass for Robert Legenstein Institute for Theoretical Computer Science Graz University of Technology, Austria Institute
More informationÂngelo Cardoso 27 May, Symbolic and Sub-Symbolic Learning Course Instituto Superior Técnico
BIOLOGICALLY INSPIRED COMPUTER MODELS FOR VISUAL RECOGNITION Ângelo Cardoso 27 May, 2010 Symbolic and Sub-Symbolic Learning Course Instituto Superior Técnico Index Human Vision Retinal Ganglion Cells Simple
More informationCausality and communities in neural networks
Causality and communities in neural networks Leonardo Angelini, Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia TIRES-Center for Signal Detection and Processing - Università di Bari, Bari, Italy
More informationProbabilistic Models in Theoretical Neuroscience
Probabilistic Models in Theoretical Neuroscience visible unit Boltzmann machine semi-restricted Boltzmann machine restricted Boltzmann machine hidden unit Neural models of probabilistic sampling: introduction
More informationDecoding conceptual representations
Decoding conceptual representations!!!! Marcel van Gerven! Computational Cognitive Neuroscience Lab (www.ccnlab.net) Artificial Intelligence Department Donders Centre for Cognition Donders Institute for
More informationThe Bayesian Brain. Robert Jacobs Department of Brain & Cognitive Sciences University of Rochester. May 11, 2017
The Bayesian Brain Robert Jacobs Department of Brain & Cognitive Sciences University of Rochester May 11, 2017 Bayesian Brain How do neurons represent the states of the world? How do neurons represent
More informationUncertainty, precision, prediction errors and their relevance to computational psychiatry
Uncertainty, precision, prediction errors and their relevance to computational psychiatry Christoph Mathys Wellcome Trust Centre for Neuroimaging at UCL, London, UK Max Planck UCL Centre for Computational
More informationNew Machine Learning Methods for Neuroimaging
New Machine Learning Methods for Neuroimaging Gatsby Computational Neuroscience Unit University College London, UK Dept of Computer Science University of Helsinki, Finland Outline Resting-state networks
More informationHow Brain Structure Constrains Brain Function. Olaf Sporns, PhD
Symposium Cognition and Connectomics - ICON 2014 How Brain Structure Constrains Brain Function Olaf Sporns, PhD Department of Psychological and Brain Sciences Indiana University, Bloomington, IN 47405
More informationModel Comparison. Course on Bayesian Inference, WTCN, UCL, February Model Comparison. Bayes rule for models. Linear Models. AIC and BIC.
Course on Bayesian Inference, WTCN, UCL, February 2013 A prior distribution over model space p(m) (or hypothesis space ) can be updated to a posterior distribution after observing data y. This is implemented
More informationATTRACTORS IN SONG. KARL FRISTON and STEFAN KIEBEL
Attractors in song: 1 New Mathematics and Natural Computation Vol. 5, No. 1 (9) 1-3 World Scientific Publishing Company ATTRACTORS IN SONG KARL FRISTON and STEFAN KIEBEL The Wellcome Trust Centre of Neuroimaging
More informationBayesian Inference Course, WTCN, UCL, March 2013
Bayesian Course, WTCN, UCL, March 2013 Shannon (1948) asked how much information is received when we observe a specific value of the variable x? If an unlikely event occurs then one would expect the information
More informationCommon Connectome Constraints: From C. elegans and Drosophila to Homo sapiens
Marcus Kaiser Common Connectome Constraints: From C. elegans and Drosophila to Homo sapiens Technical Report No. 4 Wednesday, 14 May 2014 Dynamic Connectome Lab http://www.biological-networks.org/ 1 Common
More informationConsider the following spike trains from two different neurons N1 and N2:
About synchrony and oscillations So far, our discussions have assumed that we are either observing a single neuron at a, or that neurons fire independent of each other. This assumption may be correct in
More informationCortical neural networks: light-microscopy-based anatomical reconstruction, numerical simulation and analysis
Cortical neural networks: light-microscopy-based anatomical reconstruction, numerical simulation and analysis Hans-Christian Hege Berlin Workshop on Statistics and Neuroimaging 2011, Weierstrass Institute,
More informationPart 2: Multivariate fmri analysis using a sparsifying spatio-temporal prior
Chalmers Machine Learning Summer School Approximate message passing and biomedicine Part 2: Multivariate fmri analysis using a sparsifying spatio-temporal prior Tom Heskes joint work with Marcel van Gerven
More informationAn Implementation of Dynamic Causal Modelling
An Implementation of Dynamic Causal Modelling Christian Himpe 24.01.2011 Overview Contents: 1 Intro 2 Model 3 Parameter Estimation 4 Examples Motivation Motivation: How are brain regions coupled? Motivation
More informationHGF Workshop. Christoph Mathys. Wellcome Trust Centre for Neuroimaging at UCL, Max Planck UCL Centre for Computational Psychiatry and Ageing Research
HGF Workshop Christoph Mathys Wellcome Trust Centre for Neuroimaging at UCL, Max Planck UCL Centre for Computational Psychiatry and Ageing Research Monash University, March 3, 2015 Systems theory places
More informationBayesian probability theory and generative models
Bayesian probability theory and generative models Bruno A. Olshausen November 8, 2006 Abstract Bayesian probability theory provides a mathematical framework for peforming inference, or reasoning, using
More informationGraph theory for complex Networks-I
Int. J. of Mathematical Sciences and Applications, Vol. 1, No. 3, September 2011 Copyright Mind Reader Publications www.journalshub.com Graph theory for complex Networks-I V.Yegnanarayanan 1 and G.K.Umamaheswari
More informationNeuroinformatics. Giorgio A. Ascoli. Erik De Schutter David N. Kennedy IN THIS ISSUE. Editors. Medline/Pubmed/Index Medicus Science Citation Index
ISSN 1539 2791 Volume 2 Number 3 2004 Neuroinformatics Editors IN THIS ISSUE Giorgio A. Ascoli Scaling Self-Organizing Maps to Model Large Cortical Networks Erik De Schutter David N. Kennedy Classification
More informationHuman Brain Networks. Aivoaakkoset BECS-C3001"
Human Brain Networks Aivoaakkoset BECS-C3001" Enrico Glerean (MSc), Brain & Mind Lab, BECS, Aalto University" www.glerean.com @eglerean becs.aalto.fi/bml enrico.glerean@aalto.fi" Why?" 1. WHY BRAIN NETWORKS?"
More informationWill Penny. SPM short course for M/EEG, London 2015
SPM short course for M/EEG, London 2015 Ten Simple Rules Stephan et al. Neuroimage, 2010 Model Structure The model evidence is given by integrating out the dependence on model parameters p(y m) = p(y,
More informationBiophysical models of fmri responses Klaas E Stephan, Lee M Harrison, Will D Penny and Karl J Friston
Biophysical models of fmri responses Klaas E Stephan, Lee M Harrison, Will D Penny and Karl J Friston Functional magnetic resonance imaging (fmri) is used to investigate where the neural implementation
More informationA prior distribution over model space p(m) (or hypothesis space ) can be updated to a posterior distribution after observing data y.
June 2nd 2011 A prior distribution over model space p(m) (or hypothesis space ) can be updated to a posterior distribution after observing data y. This is implemented using Bayes rule p(m y) = p(y m)p(m)
More informationHow to do backpropagation in a brain
How to do backpropagation in a brain Geoffrey Hinton Canadian Institute for Advanced Research & University of Toronto & Google Inc. Prelude I will start with three slides explaining a popular type of deep
More informationNeuroinformatics. Marcus Kaiser. Week 10: Cortical maps and competitive population coding (textbook chapter 7)!
0 Neuroinformatics Marcus Kaiser Week 10: Cortical maps and competitive population coding (textbook chapter 7)! Outline Topographic maps Self-organizing maps Willshaw & von der Malsburg Kohonen Dynamic
More informationStrategies for Discovering Mechanisms of Mind using fmri: 6 NUMBERS. Joseph Ramsey, Ruben Sanchez Romero and Clark Glymour
1 Strategies for Discovering Mechanisms of Mind using fmri: 6 NUMBERS Joseph Ramsey, Ruben Sanchez Romero and Clark Glymour 2 The Numbers 20 50 5024 9205 8388 500 3 fmri and Mechanism From indirect signals
More informationThe connected brain: Causality, models and intrinsic dynamics
The connected brain: Causality, models and intrinsic dynamics Adeel Razi + and Karl J. Friston The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG. +
More informationFunctional Causal Mediation Analysis with an Application to Brain Connectivity. Martin Lindquist Department of Biostatistics Johns Hopkins University
Functional Causal Mediation Analysis with an Application to Brain Connectivity Martin Lindquist Department of Biostatistics Johns Hopkins University Introduction Functional data analysis (FDA) and causal
More informationSpatial Source Filtering. Outline EEG/ERP. ERPs) Event-related Potentials (ERPs( EEG
Integration of /MEG/fMRI Vince D. Calhoun, Ph.D. Director, Image Analysis & MR Research The MIND Institute Outline fmri/ data Three Approaches to integration/fusion Prediction Constraints 2 nd Level Fusion
More informationWill Penny. SPM short course for M/EEG, London 2013
SPM short course for M/EEG, London 2013 Ten Simple Rules Stephan et al. Neuroimage, 2010 Model Structure Bayes rule for models A prior distribution over model space p(m) (or hypothesis space ) can be updated
More informationarxiv:physics/ v1 [physics.bio-ph] 19 Feb 1999
Odor recognition and segmentation by coupled olfactory bulb and cortical networks arxiv:physics/9902052v1 [physics.bioph] 19 Feb 1999 Abstract Zhaoping Li a,1 John Hertz b a CBCL, MIT, Cambridge MA 02139
More informationBayesian Treatments of. Neuroimaging Data Will Penny and Karl Friston. 5.1 Introduction
Bayesian Treatments of 5 Neuroimaging Data Will Penny and Karl Friston 5.1 Introduction In this chapter we discuss the application of Bayesian methods to neuroimaging data. This includes data from positron
More information+ + ( + ) = Linear recurrent networks. Simpler, much more amenable to analytic treatment E.g. by choosing
Linear recurrent networks Simpler, much more amenable to analytic treatment E.g. by choosing + ( + ) = Firing rates can be negative Approximates dynamics around fixed point Approximation often reasonable
More informationWellcome Trust Centre for Neuroimaging, UCL, UK.
Bayesian Inference Will Penny Wellcome Trust Centre for Neuroimaging, UCL, UK. SPM Course, Virginia Tech, January 2012 What is Bayesian Inference? (From Daniel Wolpert) Bayesian segmentation and normalisation
More informationSqueezing Every Ounce of Information from An Experiment: Adaptive Design Optimization
Squeezing Every Ounce of Information from An Experiment: Adaptive Design Optimization Jay Myung Department of Psychology Ohio State University UCI Department of Cognitive Sciences Colloquium (May 21, 2014)
More informationJan 16: The Visual System
Geometry of Neuroscience Matilde Marcolli & Doris Tsao Jan 16: The Visual System References for this lecture 1977 Hubel, D. H., Wiesel, T. N., Ferrier lecture 2010 Freiwald, W., Tsao, DY. Functional compartmentalization
More informationMULTISCALE MODULARITY IN BRAIN SYSTEMS
MULTISCALE MODULARITY IN BRAIN SYSTEMS Danielle S. Bassett University of California Santa Barbara Department of Physics The Brain: A Multiscale System Spatial Hierarchy: Temporal-Spatial Hierarchy: http://www.idac.tohoku.ac.jp/en/frontiers/column_070327/figi-i.gif
More informationDeep learning in the visual cortex
Deep learning in the visual cortex Thomas Serre Brown University. Fundamentals of primate vision. Computational mechanisms of rapid recognition and feedforward processing. Beyond feedforward processing:
More informationSUPPLEMENTARY INFORMATION
Supplementary discussion 1: Most excitatory and suppressive stimuli for model neurons The model allows us to determine, for each model neuron, the set of most excitatory and suppresive features. First,
More informationComparing hemodynamic models with DCM
www.elsevier.com/locate/ynimg NeuroImage 38 (2007) 387 401 Comparing hemodynamic models with DCM Klaas Enno Stephan, a, Nikolaus Weiskopf, a Peter M. Drysdale, b,c Peter A. Robinson, b,c,d and Karl J.
More informationThe General Linear Model (GLM)
he General Linear Model (GLM) Klaas Enno Stephan ranslational Neuromodeling Unit (NU) Institute for Biomedical Engineering University of Zurich & EH Zurich Wellcome rust Centre for Neuroimaging Institute
More informationA Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain
A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain Dr. Ali Yener Mutlu Department of Electrical and Electronics Engineering, Izmir Katip Celebi
More informationTuning tuning curves. So far: Receptive fields Representation of stimuli Population vectors. Today: Contrast enhancment, cortical processing
Tuning tuning curves So far: Receptive fields Representation of stimuli Population vectors Today: Contrast enhancment, cortical processing Firing frequency N 3 s max (N 1 ) = 40 o N4 N 1 N N 5 2 s max
More informationBayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity Bernhard Nessler 1 *, Michael Pfeiffer 1,2, Lars Buesing 1, Wolfgang Maass 1 1 Institute for Theoretical
More informationNonlinear reverse-correlation with synthesized naturalistic noise
Cognitive Science Online, Vol1, pp1 7, 2003 http://cogsci-onlineucsdedu Nonlinear reverse-correlation with synthesized naturalistic noise Hsin-Hao Yu Department of Cognitive Science University of California
More informationCSE/NB 528 Final Lecture: All Good Things Must. CSE/NB 528: Final Lecture
CSE/NB 528 Final Lecture: All Good Things Must 1 Course Summary Where have we been? Course Highlights Where do we go from here? Challenges and Open Problems Further Reading 2 What is the neural code? What
More informationNecessity and feasibility of brain-scale simulations at cellular and synaptic resolution
Necessity and feasibility of brain-scale simulations at cellular and synaptic resolution February 22-23 2016 6th AICS International Symposium RIKEN AICS, Kobe, Japan www.csn.fz-juelich.de www.nest-initiative.org
More informationAn introduction to Bayesian inference and model comparison J. Daunizeau
An introduction to Bayesian inference and model comparison J. Daunizeau ICM, Paris, France TNU, Zurich, Switzerland Overview of the talk An introduction to probabilistic modelling Bayesian model comparison
More informationComparing Dynamic Causal Models
Comparing Dynamic Causal Models W.D. Penny, K.E. Stephan, A. Mechelli and K.J. Friston, Wellcome Department of Imaging Neuroscience, University College London. October 17, 2003 Abstract This article describes
More informationFACULTY OF ELECTRONICS, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY. Ing. Nicolae-Cristian PAMPU. PhD. THESIS
Investeşte în oameni! FONDUL SOCIAL EUROPEAN Proiect cofinanţat din Fondul Social European prin Programul Operaţional Sectorial pentru Dezvoltarea Resurselor Umane 2007-2013 Axa prioritară 1 : Educaţia
More informationDynamic causal models of brain responses
Okinawa Computational Neuroscience Course; Thurs 13 th Nov. 2004 Dynamic causal models of brain responses Klaas E. Stephan, Lee M. Harrison, Will D. Penny, Karl J. Friston The Wellcome Dept. of Imaging
More informationFundamentals of Computational Neuroscience 2e
Fundamentals of Computational Neuroscience 2e January 1, 2010 Chapter 10: The cognitive brain Hierarchical maps and attentive vision A. Ventral visual pathway B. Layered cortical maps Receptive field size
More informationSpectral Interdependency Methods
Spectral Interdependency Methods Mukesh Dhamala* Department of Physics and Astronomy, Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA Synonyms Coherence and Granger causality spectral
More informationAdaptation in the Neural Code of the Retina
Adaptation in the Neural Code of the Retina Lens Retina Fovea Optic Nerve Optic Nerve Bottleneck Neurons Information Receptors: 108 95% Optic Nerve 106 5% After Polyak 1941 Visual Cortex ~1010 Mean Intensity
More informationNeuroImage. Nonlinear dynamic causal models for fmri
NeuroImage 42 (2008) 649 662 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Nonlinear dynamic causal models for fmri Klaas Enno Stephan a,b,, Lars
More informationBiosciences in the 21st century
Biosciences in the 21st century Lecture 1: Neurons, Synapses, and Signaling Dr. Michael Burger Outline: 1. Why neuroscience? 2. The neuron 3. Action potentials 4. Synapses 5. Organization of the nervous
More informationSpatial Representations in the Parietal Cortex May Use Basis Functions
Spatial Representations in the Parietal Cortex May Use Basis Functions Alexandre Pouget alex@salk.edu Terrence J. Sejnowski terry@salk.edu Howard Hughes Medical Institute The Salk Institute La Jolla, CA
More informationSynchrony in Neural Systems: a very brief, biased, basic view
Synchrony in Neural Systems: a very brief, biased, basic view Tim Lewis UC Davis NIMBIOS Workshop on Synchrony April 11, 2011 components of neuronal networks neurons synapses connectivity cell type - intrinsic
More informationLearning in Bayesian Networks
Learning in Bayesian Networks Florian Markowetz Max-Planck-Institute for Molecular Genetics Computational Molecular Biology Berlin Berlin: 20.06.2002 1 Overview 1. Bayesian Networks Stochastic Networks
More informationBrains and Computation
15-883: Computational Models of Neural Systems Lecture 1.1: Brains and Computation David S. Touretzky Computer Science Department Carnegie Mellon University 1 Models of the Nervous System Hydraulic network
More informationHow to make computers work like the brain
How to make computers work like the brain (without really solving the brain) Dileep George a single special machine can be made to do the work of all. It could in fact be made to work as a model of any
More informationEmpirical Bayes for DCM: A Group Inversion Scheme
METHODS published: 27 November 2015 doi: 10.3389/fnsys.2015.00164 : A Group Inversion Scheme Karl Friston*, Peter Zeidman and Vladimir Litvak The Wellcome Trust Centre for Neuroimaging, University College
More information