Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.
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Transcript of Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.
![Page 1: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/1.jpg)
Cortical circuits for vision
Jamie Mazer
Neurobiology of Cortical Systems
Lecture 7
March 12, 2012
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Readings for Thursday
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How much of cortex is visual? (in primates)
Van Essen flat mapof macaque cortex
Primates are likely an extremeexample or an upper bound..
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How much of cortex is visual?
Van Essen flat mapof macaque cortex
“simplified” Felleman & Van Essen hierarchy
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Key concepts
• phenomenon vs. implementation vs. function
• “centrally synthesized maps”– everything we perceive must be encoded by the retina
– if so, what’s all that visual cortex doing?
– generating explicit sensory representations
– “emergent” properties seem to be a key feature of high-level sensory cortical function
– Question: Is cortex required to generate explicit or abstract properties?
– Answer: What’s emergent in the retina? What about animals with not cortex, like birds and fish?
• are there common “motifs” across sensory modalities?– computational maps in other modalites?
– what about other species? are they unique to cortex?
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Retinal bipolar cells receptive fields
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Retinal ganglion cell RFs (only retinal output)
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Receptive fields and center-surround opponency
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Receptive fields and center-surround opponency
Center-surround organization Observed phenomenon? Implementation? Function?
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Receptive fields and center-surround opponency
Center-surround organization Observed phenomenon? Characteristic RF structure Implementation? Lateral inhibition Function? Spatial derivative; contrast enhancement
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Behavioral consequences of center surround organization
herring gridmach bands
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Behavioral consequences of center surround organization
herring gridmach bands
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Thalamus: dLGN
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What changes between the photoreceptors and LGN?
• transition from receptor potentials to spiking• center-surround spatial receptive fields• “color opponency” (B-Y/R-G) instead of simple cone-
based wavelength tuning• segregation into parallel processing streams
– sustained and transient– fast and slow– on and off channels– color and luminance
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Which brings us to primary visual cortex (BA 17; V1)
m
visualassociation
primaryvisual
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Topographic organization of V1
- retinotopy- orientation columns- occular dominance columns- non-oriented blobs (L2)- orientation topography
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Thalamocortical projections and the canonical microcircuit
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Primary visual cortex: simple cell orientation tuning
hubel & wiesel 1968
orientation tuned V1 neuron
MOVIE
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Primary visual cortex: simple cell orientation tuning
hubel & wiesel 1968
orientation tuned V1 neuronhubel & wiesel model
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Primary visual cortex: simple cell orientation tuning
hubel & wiesel 1968
orientation tuned V1 neuronhubel & wiesel model
Key failures for the feedforward model?
- contrast invariant orientation tuning
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Primary visual cortex: simple cell orientation tuning
hubel & wiesel 1968
orientation tuned V1 neuronhubel & wiesel model
Hubel & Wiesel Interpretation Observed phenomenon?
preferred orientation Implementation?
linear summation of LGN cells Function?
feature detectors for edges
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Primary visual cortex: simple cell orientation tuning
hubel & wiesel 1968
orientation tuned V1 neuron hubel & wiesel model
Spatial Vision Interpretation Observed phenomenon?
preferred orientation Implementation?
quasi-linear combination of LGN cells Function?
spatiotemporal filtering
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• cells prefer light increments or decrements
• cells have orientation tuning
• cells have a width tuning
• cells have length tuning
• cells have speed tuning
• cells are feature detectors where the feature is a bar of a particular orientation, size and speed
• intuitively obvious, simple to understand, seems to imply obvious behavioral function
• cells prefer light increments or decrements
• cells have orientation tuning
• cells have spatial frequency tuning
• cells have temporal frequency tuning
• cells are half-wave rectified spatiotemporal filters (Gabors)
• requires some math chops to understand, but has predictive power
Feature detector model “spatial vision” model
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Primary visual cortex: spatial frequency tuning
Robson, DeValois, Maffei etc..
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Feature detector model “spatial vision” model
• cells prefer light increments or decrements
• cells have orientation tuning
• cells have a width tuning
• cells have length tuning
• cells have speed tuning
• cells are feature detectors where the feature is a bar of a particular orientation, size and speed
• intuitively obvious, simple to understand, seems to imply obvious behavioral function
• cells prefer light increments or decrements
• cells have orientation tuning
• cells have spatial frequency tuning
• cells have temporal frequency tuning
• cells are half-wave rectified spatiotemporal filters
• requires some math chops to understand, but has predictive power
![Page 26: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/26.jpg)
Primary visual cortex: simple complex
hubel & wiesel 1968
simple
complex
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Primary visual cortex: simple complex
hubel & wiesel 1968
simple
complex
MOVIE
![Page 28: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/28.jpg)
Primary visual cortex: simple complex
hubel & wiesel 1968
simple
complex
hypercomplex+length tuning+length tuning+length tuning
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Primary visual cortex: simple complex
hubel & wiesel 1968
“simple cells”pool center-surround neuronsto form orientation selectivity
“complex cells”pool simple cells to becomeposition or phase invariant.
and turtles all the way down…
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Complex cells and the F1/F0 ratio
cats
monkeys
Skottun et al, 1991
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What’s the spatial vision model got to say?
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Complex cells and the F1/F0 ratio
Skottun et al, 1991
cats
monkeys
Mechler & Ringach, 2002
is this all an artifact?
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Reverse correlation and the spike triggered average
Jones & Palmer, 1987
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Reverse correlation and the spike triggered average
Jones & Palmer, 1987
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Reverse correlation and the spike triggered average
Jones & Palmer, 1987
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V1 neurons are Gabor’s and Gabor’s are optimal…
Daugman, 1985
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V1 neurons are Gabor’s and Gabor’s are optimal…
Daugman, 1985
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Where do Gabor’s come from and the efficient coding hypothesis
Barlow, 1972
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Where do Gabor’s come from and the efficient coding hypothesis
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Where do Gabor’s come from and the efficient coding hypothesis
Vinje & Gallant, 2000
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Where do Gabor’s come from and the efficient coding hypothesis
Haider et al, 2010
![Page 42: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/42.jpg)
What have we established?• simple cells
– simple cells are partially assembled from LGN afferents
– one basic flavor: Gabor
• they are bar-detectors as well (glass half empty), but
• the Gabor-model seems like a more compact framework
• complex cells– complex cells are assembled from simple cells
– strict dichotomy not likely, more likely is,
• thalamocortical direct recipient simple cells, and,
• cells that are a combination of simple and non-simple innputs
• coding in V1– sparseness is a hallmark of an efficient code
– simple cells can be learned by maximizing sparseness
– sparseness in V1 is based on center-surround (intracortical) inhibitory interations
– the neural representation is awful close to what the computer vision people call a wavelet or multiscale pyramid and is the basis for things like MPG and JPG compression…
• perhaps we need more data from more complex stimuli?
![Page 43: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/43.jpg)
Reverse correlation, complex cells and natural scenes
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Reverse correlation, complex cells and natural scenes
Problems:1. STA doesn’t really work for
natural (non-white) stimuli2. the STA is just plain “wrong”
for complex cells
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Linear receptive field maps in early vision
DeAngelis et al, 1995
still orientation tuned!where’s it coming from?
![Page 46: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/46.jpg)
Reverse correlation, complex cells and natural scenes
Problems:1. STA doesn’t really work for
natural (non-white) stimuli2. the STA is just plain “wrong”
for complex cells
![Page 47: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/47.jpg)
Reverse correlation, complex cells and natural scenes
Problems:1. STA doesn’t really work for
natural (non-white) stimuli2. the STA is just plain “wrong”
for complex cells
Spike Triggered Covariance (STC)
![Page 48: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/48.jpg)
What have we established?• simple cells
– simple cells are partially assembled from LGN afferents
– one basic flavor: Gabor
• they are bar-detectors as well (glass half empty), but
• the Gabor-model seems like a more compact framework
• complex cells– complex cells are assembled from simple cells
– strict dichotomy not likely, more likely is,
• thalamocortical direct recipient simple cells, and,
• cells that are a combination of simple and non-simple innputs
• coding in V1– sparseness is a hallmark of an efficient code
– simple cells can be learned by maximizing sparseness
– sparseness in V1 is based on center-surround (intracortical) inhibitory interations
– the neural representation is awful close to what the computer vision people call a wavelet or multiscale pyramid and is the basis for things like MPG and JPG compression…
• perhaps we need more data from more complex stimuli?– STRFs, STC, regression analysis, MII etc all provide new tools could complex cells and complex stimuli…
• what did I not talk about??
![Page 49: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/49.jpg)
Direction selectivity
hubel&wiesel 1968
MOVIE
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Direction selectivity is Gabor-ish too (vs. Reichardt Detector)
DeAngelis et al, 1993. 1995
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Disparity/depth tuning
focal plane
near
far
foveae G. Poggio et al
MOVIE
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Disparity too fits in the spatial vision view…
Note: Complex cells see anti-correlated bars differently than correlated, not true for perception…
Ohzawa et al, 1997
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Is spatial vision everything?
• the high-dimensional Gabor filter model explains a lot of the neurophysiological and psychophysical data, but..– finding the right dimensions is non-trivial as we’ll see next week.
– even when the dimensions are likely identified, it’s essentially a linear or quasi-linear model and doesn’t explain a range of observed phenomena, even in V1…
![Page 54: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/54.jpg)
Center-surround interactions in V1 – generally NOT accounted for by the standard spatial vision model.
• end-stopping, length-tuning, “hypercomplexity” (H&W)• cross-orientation inhibition (Silito et al)• divisive gain control (Carandini paper!)• curvature processing (Dobbins & Zucker)• target pop-out (Knierim & Van Essen)• attention and/or figure segmentation (Lamme et al)
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What are the emergent properties of V1?
• new features extracted– orientation– binocular disparity (depth)– direction selectivity– spatial frequency– color (really “transformed”)
• new maps– orientation (columns)– ocular dominance– segregation of color info in blobs
![Page 56: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/56.jpg)
What have we established?• simple cells
– simple cells are partially assembled from LGN afferents
– one basic flavor: Gabor
• they are bar-detectors as well (glass half empty), but
• the Gabor-model seems like a more compact framework
• complex cells– complex cells are assembled from simple cells
– strict dichotomy not likely, more likely is,
• thalamocortical direct recipient simple cells
• cells that are a combination of simple and non-simple innputs
• coding in V1– sparseness is a hallmark of an efficient code
– simple cells can be learned by maximizing sparseness
– sparseness in V1 is based on center-surround (intracortical) inhibitory interations
– the neural representation is awful close to what the computer vision people call a wavelet or multiscale pyramid and is the basis for things like MPG and JPG compression…
• perhaps we need more data from more complex stimuli?– STRFs, STC, regression analysis, MII etc all provide new tools could complex cells and complex stimuli…
• V1 exhibits multiple emergent properties
• What happens when you lose V1??
• How much of this interpretation is primate-centric?
![Page 57: Cortical circuits for vision Jamie Mazer Neurobiology of Cortical Systems Lecture 7 March 12, 2012.](https://reader036.fdocuments.us/reader036/viewer/2022081512/5518a1f9550346a61f8b48e6/html5/thumbnails/57.jpg)
Primate-centric view?
Andermann et al., 2011
Neill & Stryker, 2010
Shuler & Bear, 2006
- Rodents have striate and extrastriate analogues or homologues- Tuning is similar, but not identical- “Extra-retinal” effects seem more pronounced
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Readings for Thursday