Representational similarity analysis. Dr Ian Charest,
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1 Representational similarity analysis Dr Ian Charest, Edinburgh, April 219
2 A space for neuroimaging studies Pattern across subjects Cross-subject correlation studies (e.g. social neuroscience) Raizada et al. 21 Mur et al (single-image regional average activation) univariate regional-mean activation studies Pattern across Kanwisher et al.1997 (face area) Pattern across voxels Haxby et al. 21. (distinct category-average patterns) Charest & Kriegeskorte, 215 Kriegeskorte et al. 28 (single-image patterns cluster by category)
3 A space for neuroimaging studies Pattern across subjects univariate regional-mean activation studies Pattern across voxels Pattern across
4 A space for neuroimaging studies Pattern across subjects univariate regional-mean activation studies Pattern across voxels Pattern across Classical MVPA
5 A space for neuroimaging studies Pattern across subjects RSA univariate regional-mean activation studies Pattern across voxels Pattern across Classical MVPA
6 Representational similarity analysis representational dissimilarity matrices (RDMs) brain representation (e.g. fmri pattern dissimilarities) stimulus (e.g. images, sounds, other experimental conditions) representational pattern (e.g. voxels, neurons, model units) behaviour (e.g. dissimilarity judgments) dissimilarity activity representational dissimilarity stimulus description (e.g. pixel-based dissimilarity) computational model representation (e.g. face-detector model) Charest et al. 214, 215, Kriegeskorte & Kievit 213, see also: Edelman et al. 1998, Laakso & Cottrell 2, Op de Beeck et al. 21, Haxby et al. 21, Aguirre 27, Kriegeskorte et al. 28
7 Why investigate representational geometries?
8 downstream neurons can read out the same information from these codes neuron 1 neuron 3 same geometry same information same format neuron 2 neuron 1 neuron 3 neuron 2 Representational geometry The geometry of the points in a high-dimensional response pattern space, which are thought to represent particular. slide kindly provided by N. Kriegeskorte
9 category information...for linear readout...for nonlinear readout...inherently categorical Kriegeskorte & Kievit 213
10 Kriegeskorte & Kievit 213
11 How can we best measure representational distances?
12 Distance estimates are positively biased pattern2data noise distdata pattern2true noise disttrue neuron 2 pattern1true distdata pattern1data neuron 1 disttrue
13 Distances are positively biased just like training-set decoding accuracies!
14 Euclidean distance Straight-line distance between two patterns in Euclidean space Image from Alex Walther RSA workshop 215
15 Correlation distance 1 correlation Correlation = cosine of the angle between normalised patterns Image from Alex Walther RSA workshop 215
16 Linear discriminant contrast (LDc) The default distance measure used in the RSA toolbox (based on the Euclidean distance). It has two desired properties: 1. Multivariately noise normalised 2. Cross-validated
17 Noise normalisation Noise normalisation of the fmri response patterns increases the reliability of the estimated pattern distances. Univariate: Divide each voxel s beta weight by its standard deviation t value Multivariate: Multiply each pattern with the inverse of the (square-rooted) covariance matrix Mahalanobis distance
18 Cross-validated distance measures Noise distance measures are positively biased. Cross-validated distance measures are unbiased and have an interpretable zero point. d(a,b) LDc The cross-validated Mahalanobis distance B. data set 1 data set 2 Images (adopted) from Alex Walther RSA workshop 215
19 The representational similarity trick! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... brain experimental?... model...
20 The representational similarity trick! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... subject 1 experimental?... subject 2...
21 The representational similarity trick! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... brain experimental?... behaviour...
22 The representational similarity trick! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... region 1 experimental?... region 2...
23 The representational similarity trick! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... fmri experimental?... cell recording...
24 The representational similarity trick! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... fmri experimental?... MEG...
25 The RSA trick Kriegeskorte, 28
26 Comparing brain RDMs between people! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... subject1 experimental?... subject 2...
27 Stimuli bodies faces places objects animate inanimate Charest et al. 214 PNAS
28 Stimuli Objects from the subject s own photo-album bodies faces places objects animate inanimate Charest et al. 214 PNAS
29 Representational Dissimilarity Matrix (RDM) subject 1 (hit) bodies dissimilarity faces places objects Charest et al. 214 PNAS [ percentile of distance ] 1
30 Multi-dimensional scaling bodies faces places objects
31 Comparing brain RDMs between people day 1 day 2 subject 1 subject similarity matrix day 2 s1 subject 2 s1 day 1 s2 correlation between-subject (bs) within-subject (ws) Charest et al. 214 PNAS s4 s5 individuation index ( ws - bs ) s3? s 2 s2 s3 s4 s5 s 2
32
33 Representational geometries in human inferior temporal cortex Neurotypicals ASC
34 GLMdenoise improves MVPA analyses
35 GLMdenoise improves MVPA analyses Within-participant consistency is improved when using GLMdenoise
36 GLMdenoise improves MVPA analyses Between-participants consistency is improved when using GLMdenoise
37 Relating brain and model RDMs! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... brain experimental?... model...
38 Deep convolutional neural network state of the art in computer vision trained with stochastic gradient descent supervised with 1.2 million category-labeled images 6 million parameters and 65, neurons er lay 1 r2 e lay convolutional Krizhevsky et al. 212 r3 e lay Is this network functionally similar to the brain? r4 e lay r5 e lay er lay fully connected 6 r e lay
39 highest accuracy any model can achieve accuracy of human IT dissimilarity matrix prediction other subjects average as model [group-average of Kendall s τa] SE (stimulus bootstrap) im accuracy above chance p<.1 (subjects and as fixed effects) ies n or r 1 er 2 er 3 er 4 er 5 er 6 er 7 ce, a categ e lay lay lay lay lay lay lay fa all convolutional fully SVM connected discriminants weighted combination of fully connected layers and SVM discriminants Khaligh-Razavi & Kriegeskorte (214), Nili et al. 214 (RSA Toolbox)
40 model comparison (stimulus bootstrap) highest accuracy any model can achieve accuracy of human IT dissimilarity matrix prediction other subjects average as model [group-average of Kendall s τa] SE (stimulus bootstrap) im accuracy above chance p<.1 (subjects and as fixed effects) ies n or r 1 er 2 er 3 er 4 er 5 er 6 er 7 ce, a categ e lay lay lay lay lay lay lay fa all convolutional fully SVM connected discriminants weighted combination of fully connected layers and SVM discriminants Khaligh-Razavi & Kriegeskorte (214), Nili et al. 214 (RSA Toolbox)
41 Comparing brain RDMs and behavioural RDMs representational distance matrix (RDM)! dissimilarity (e.g. 1-correlation across space) activity patterns... brain experimental?... behaviour...
42 MEADOWS
43 MEADOWS
![distance] dissimilarity 1](/docs-images/95/123310930/images/44-4.jpg "unfamiliar images bodies")
44 Judgment RDM unfamiliar [percentile of Euclidean distance] dissimilarity 1 unfamiliar images bodies faces places objects
45 unfamiliar bodies faces places objects Similarity Judgements
46 hit bodies faces places objects
47
48 Comparing brain RDMs and behavioural RDMs brain behaviour subject 1 subject similarity matrix day 2 s1 subject 2 s1 day 1 s2 correlation between-subject (bs) within-subject (ws) Charest et al. 214 PNAS s4 s5 individuation index ( ws - bs ) s3? s 2 s2 s3 s4 s5 s 2
49
![Representational Dissimilarity Matrix (RDM) RSA [ percentile of distance ] dissimilarity 1 voxels compute the dissimilarity (e.g.](/docs-images/95/123310930/images/50-4.jpg "1 correlation) representational pattern (population code representation) human inferior temporal (hit)... experimental Charest et al.")
50 Representational Dissimilarity Matrix (RDM) RSA [ percentile of distance ] dissimilarity 1 voxels compute the dissimilarity (e.g. 1 correlation) representational pattern (population code representation) human inferior temporal (hit)... experimental Charest et al. 214 PNAS
51 Representational Dissimilarity Matrix (RDM) RSA [ percentile of distance ] dissimilarity 1 EEG Channel amplitudes compute the dissimilarity (e.g. 1 correlation) linear discriminant analysis representational pattern (population code representation) EEG activity-pattern at time t... experimental
52 RSA
53 EEG contains rich topographic information from which you can distinguish mental states
54 EEG contains rich topographic information from which you can distinguish mental states bodie s face s place s object s
55 bodie s face s place s object s
56 Object familiarity decoding from EEG activity patterns unfamiliar objects familiar objects signifiant above-chance decoding
57 Object familiarity decoding from EEG activity patterns
58 Object familiarity decoding from EEG activity patterns
59 Comparing RDMs between measurement modalities! representational distance matrix (RDM) dissimilarity (e.g. 1-correlation across space) activity patterns... fmri experimental?... MEG...
60 Similarity based fusion of M/EEEg and fmri Cichy et al. 214, 216
61 Similarity based fusion of M/EEEg and fmri Cichy et al. 214, 216
62 The spatio-temporal dynamics of personally meaningful objects
63 The spatio-temporal dynamics of personally meaningful objects
64 Cichy et al. 214 Nature Neuroscience
65 Key insights A1 Representational geometries encapsulate the content and format of brain representations. A2 Representational geometries can be characterised by representational dissimilarity matrices (RDMs). A3 RDMs can easily be compared between brains and models, individuals and species, different brain regions, different measurement modalities, and brain and behaviour. A4 We can statistically compare multiple computational/theoretical models and assess whether they fully explain the measured brain response patterns.
66
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