Jan 16: The Visual System
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1 Geometry of Neuroscience Matilde Marcolli & Doris Tsao Jan 16: The Visual System
2 References for this lecture 1977 Hubel, D. H., Wiesel, T. N., Ferrier lecture 2010 Freiwald, W., Tsao, DY. Functional compartmentalization and viewpoint generalization in the macaque face patch system 2013 Nature Mante, Susillo, Shenoy, Newsome, Context-dependent computation by recurrent dynamics in prefrontal cortex.
3 Santiago Ramon y Cajal (1 May October 1934) Neuron doctrine: The brain is made up of discrete individual cells. Vision is explicable in terms of the firing patterns that emerge from the interactions among many individual neurons. Characterizing functional transformations along anatomically-connected pathways Characterizing population dynamics among large groups of neurons
4 Retina
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6 Visual processing in the retina Stephen Kuffler David Hubel, Eye, Brain, Vision
7 LGN
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11 6 K 5 K 4 3 K 2 K K 1 K 1-2: Magno fast and transient, large RF 2-6: Parvo slow and sustained, small RF K: innervate extrastriate cortex
12 V1 Primary Visual cortex Striate cortex Area 17
13 Hubel & Wiesel, 1959 (HMS)
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21 Hubel & Wiesel, 1962
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24 Functional Architecture
25 Monkey Human
26 Critical Period
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29 Sparse noise stimulus RF of V1 simple cell Livingstone and Conway, 2003
30 Ohki et al. Nature 2006
31 Features computed by V1 Orientation Color Motion Binocular Disparity
32 What happens to visual information after V1?
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35 What happens to visual information after V2?
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38 Criteria for Cortical Area Topographic map Unique connections Unique cytoarchitecture Unique function
39 Lewis and Van Essen 2000
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41 Felleman and Van Essen 1991
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43 Border ownership cell V1 Simple cell (Hubel & Wiesel) V2 Border-ownership cell (von der Heydt)
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45 What & Where Pathways Vision is knowing what is where by looking David Marr
46 The Ungerleider & Mishkin (1982) Experiment Task 1: Object discrimination study an object select the familiar object (reward) Task 2: Landmark discrimination select foodwell closest to the TOWER temporal lesions impair OBJECT TASK parietal lesions impair LANDMARK TASK
47 The macaque face patch system
48 Pathway for object representation Charles Gross V1 Inferotemporal Cortex (IT) V4 V3 V2 Object identification Image segmentation Discouraged by my inability to understand the frontal lobe I decided to turn my attention to the cortex on the inferior convexity of the temporal lobe Charles Gross, History of Neuroscience autobiography
49 Fusiform face area? Kanwisher, McDermott, & Chun, J. Neurosci, 1997
50 Face-selective regions exist in monkeys
51
52 Localization of face patches occipital frontal PL ML MF posterior AL AF middle anterior AM temporal Faces vs. objects Tsao et al., Nature Neuroscience 2002
53 FMRI response time course from middle face patch ML
54 What is the selectivity of single neurons in each patch?
55 What is the selectivity of single neurons in each patch?
56 What is the selectivity of single neurons in each patch?
57 Single cell from middle face patch
58 Tsao et al., Science, 2006
59 Cell number Face cells are clustered in face patches PL ML MF AL AF AM Tsao et al., Science 2006 Freiwald and Tsao, Science 2010
60 Face patches are strongly and specifically connected to each other PL ML MF AL AF AM Moeller et al. Science 2008 Grimaldi et al., Neuron, in press
61 Probing connectivity of face patches: Microstimulation combined with fmri Moeller et al., Science, 2008 Sebastian Möller
62 Stimulation site on the flatmap posterior ventral x Stimulation site
63 Microstimulation >> Blank posterior ventral x Stimulation site
64 ML projection map overlaps with other face patches posterior ventral x Stimulation site
65 Connections of ML
66 P value Connections within IT cortex largely confined to other face patches posterior ventral PL ML AL x MF AF 10-3 AM Grimaldi et al., Neuron 2016 Injection site in AL, retrograde tracer (Fast Blue) 66
67 Connections within IT cortex largely confined to other face patches posterior ventral PL ML AL x AM MF AF Cells / mm 3 Grimaldi et al., Neuron 2016 Injection site in AL, retrograde tracer (Fast Blue) 67
68 P value Connections within IT cortex largely confined to other face patches posterior ventral PL ML AL x AM MF AF Cells / mm Grimaldi et al., Neuron 2016 Injection site in AL, retrograde tracer (Fast Blue) 68
69 Face patch system
70 Experimental obstacles Huge cortical territory Huge parameter space
71 Experimental obstacles Face patches Faces
72 What are functional properties of cells in each patch?
73 Norm. resp. Cells are selective for the presence and shape of subsets of face features Cell 1 Cell NO HAIR HAIR Freiwald et al., NN 2009
74 Norm. resp. Cells are selective for the presence and appearance of subsets of face features Cell 1 Cell NO IRISES IRISES NO IRISES IRISES Freiwald et al., NN 2009
75 Increasing view invariance from ML/MF to AL to AM ML/MF AM AL
76 Anterior medial face patch (AM) neurons represents face-view invariant identity 0.1s Freiwald and Tsao, 2010
77 Middle face patch (MF/ML) neurons were tuned to head orientation 0.1s Freiwald and Tsao, 2010
78 Middle face patch (MF/ML) neurons were tuned to head orientation 0.1s Freiwald and Tsao, 2010
79 Santiago Ramon y Cajal (1 May October 1934) Neuron doctrine: The brain is made up of discrete individual cells. Vision is explicable in terms of the firing patterns that emerge from the interactions among many individual neurons. Characterizing functional transformations along anatomically-connected pathways Characterizing population dynamics among large groups of neurons
80 Understanding the mechanism for flexible behavior Mante and Newsome 2013
81 Step 1: Compute top 12 PCs of population data D = matrix of eigenvectors Step 2: Demixing (goal: plot neural state in meaningful coordinates) Step 3: Denoise the regression vectors (project onto 12 PCs)
82 Regression coefficients for individual neurons
83 Step 4: Orthogonalize regression vectors (Gramm Schmidt) b 2 Q 2 b 1 Q 1
84
85 So far, not THAT surprising (a fancy way of showing that (1) the monkey can do the task and the choice signal is also represented in PFC, and (2) both color and motion information are represented regardless of context,) What is the underlying mechanism?
86 Recurrent Neural Network x represents activation of neuron, r represents firing rate, J represents recurrent connections, u represents input, c represents offsets, rho represents noise J, b, c are modified through training
87 . Search for fixed points (where x = 0)
88 Red crosses: fixed points (same in both contexts)
89
90 Context-depending integration is explained by different neural dynamics in the two situations!
91 We conclude that the observed complexity and functional roles of single neurons are readily understood in the framework of a dynamical process unfolding at the level of a population.
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