280 lines
12 KiB
Markdown
280 lines
12 KiB
Markdown
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## Brain damage and visual perception
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<div><img src="figs/ScreenShot2015-09-11at5.11.54PM_241cf9a.png" height="400px"><figcaption></figcaption></div>
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<div><img src="figs/2015-09-1116.47.12_3eb196a.png" height="400px"><figcaption></figcaption></div>
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Note:
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Let’s begin by going discussing one of the fantastic true stories told by the famous NYC neurologist, Oliver Sacks, who passed away just a few months ago and who weaved engaging clinical accounts and wrote a number of best selling books regarding cases of patients having extraordinary behaviors that resulted from strange or unknown neurological disorders including this one called The Man Who Mistook his Wife for a Hat. —>Indeed one of these accounts was about a man who actually mistook his wife’s face for a hat. This man, who was an accomplished musician and teacher at a school of music had developed trouble seeing faces and recognizing many types of objects in general as a result of degeneration in the visual system, likely from a stroke or something.
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…these types of stories summarize a large bit of what neuroscience is about— understanding fundamental circuits that comprise brain function and animal behavior as well as the dually fascinating and devastating consequences that occur when the formation of those fundamental circuits goes awry.
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---
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## Brain damage and visual perception
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<div style="font-size:0.8em;">
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<div></div>
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* The patient (‘Dr. P’):
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* good visual acuity & color vision
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* good recognition of abstract geometric objects (cubes, spheres, etc)
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* Trouble recognizing friends, family, pupils
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* Trouble recognizing complex objects
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* Describing a rose: “About six inches in length. A convoluted red form with a linear green attachment”
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* Describing a glove: “A continuous surface, infolded on itself. It appears to have five outpouchings”
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</div>
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👵🏻
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🎩
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Note:
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Let’s begin by going discussing one of the fantastic true stories told by the famous NYC neurologist, Oliver Sacks, who passed away just last summer and who weaved engaging clinical accounts and wrote a number of best selling books regarding cases of patients having extraordinary behaviors that resulted from strange or unknown neurological disorders including this one called The Man Who Mistook his Wife for a Hat. —>Indeed one of these accounts was about a man who actually mistook his wife’s face for a hat. This man, who was a well regarded and accomplished musician and teacher at a NY school of music had developed trouble seeing faces and recognizing many types of objects in general as a result of degeneration in the visual system, likely from a stroke.
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This patient (let’s call him Dr. P)… was cognitively sharp, had good vis…
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Hard time...
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visual agnosia, prospognosia, lesion somewhere in temporal lobe of the cerebral cortex for reasons we will hopefully discover partially by the end of today’s class.
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For him the visual world was a series of lifeless abstractions, seeing and describing the world almost the way a machine would see it without grasping the big picture.
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…these types of stories summarize a large bit of what neuroscience is about— understanding fundamental circuits that comprise brain function and animal behavior as well as the dually fascinating and devastating consequences that occur when the formation of those fundamental circuits goes awry.
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---
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## The visual pathway– retinotopy
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Hubel, 1988
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retina
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superior colliculus,
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dLGN,
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visual cortex
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<div><img src="figs/droppedImage1_951ed66.pdf" height="100px"><figcaption></figcaption></div>
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<div><img src="figs/retinotopic_mapping_80f68b4.jpg" height="100px"><figcaption></figcaption></div>
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Note:
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Neighboring retinal ganglion cells in the eye detect changes in contrast from similar portions of the visual field, thus forming a 2D map of visual space in the retina. This spatial representation of objects in the retina is then projected onto -->multiple down stream visual areas, so that maps of retinal topography, or retinotopy, are maintained at multiple levels in the visual system.
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Other visual functional organization that is present at birth includes maps of ocular dominance, where the responses of neuronal groups is dominated by that of one eye or the other and orientation selectivity where the responses of neighboring neurons is dominated by high contrast edges of particular orientation.
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---
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## Intrinsically photosensitive RGCs (containing melanopsin) are required for day/night activity cycles
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<div><img src="figs/image_3a9dca9.png" height="100px"><figcaption></figcaption></div>
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<div><img src="figs/image1_d74352b.png" height="100px"><figcaption></figcaption></div>
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---
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## The visual scene is inverted on the retina
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<div><img src="figs/image2_33f6059.png" height="100px"><figcaption></figcaption></div>
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<div><img src="figs/image3_aa4d6a4.png" height="100px"><figcaption></figcaption></div>
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---
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## Binocular visual field: species differences
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* At the optic chiasm, visual information from the two sides of the head cross
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* In animals with eyes on the sides of the head, the entire visual field for each side is sent to the opposite side of the brain (to the tectum)
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* In forward-looking animals, the visual image is split
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* An object on the right side of the visual field is seen by both left hemi-retinae (but not by the right hemi-retinae). The optic nerves leave the retinae, and at the optic chiasm, the two left hemi-retinae projections go left, while the two right hemi-retinae go right
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<div><img src="figs/16-2_ae0f019.jpg" height="100px"><figcaption>Fig 16-2 Neurobiology, G.G. Matthews, Blackwell Science</figcaption></div>
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---
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## P type RGCs are sensitive to color contrast
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<div><img src="figs/image10_9804c4d.png" height="100px"><figcaption></figcaption></div>
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Note:
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---
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## Projection to cortex
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* The visual field is projected in a retinotopic fashion
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* The right visual field is projected onto the left cortex, while the left visual field is represented on the right
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* The region of the fovea, because of its high sensitivity and density of cones, is represented by a huge amount of the cortex
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<div><img src="figs/Fig27-9_e1cd31a.png" height="100px"><figcaption></figcaption></div>
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Note:
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Incr representation sound familiar? think of hand and lip representation in human somatosensory cortex we discussed a couple classes ago…
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---
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## Information from multiple LGN inputs are used to make cortical neuron receptive fields
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* Filtering of info from multiple LGN cells is used to make simple and complex cells in visual cortex
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red dots inhibitory synapses
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[LGN on cell: http://www.youtube.com/watch?v=jIevCFZixIg](http://www.youtube.com/watch?v=jIevCFZixIg)
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[V1 simple cell: http://www.youtube.com/watch?v=Cw5PKV9Rj3o](http://www.youtube.com/watch?v=Cw5PKV9Rj3o)
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[Hubel: https://www.youtube.com/watch?v=y_l4kQ5wjiw](https://www.youtube.com/watch?v=y_l4kQ5wjiw)
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<div><img src="figs/image5_3ab5bfc.png" height="100px"><figcaption></figcaption></div>
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Note:
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other hubel vid I saw and marked times…
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* david hubel 1:24-2:18:
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* 125 million rods and cones in each eye.
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* misha pavel, sobel filter
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* try to build a robot to see and interpret images and it's hard.
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: 4:45 nice example of movement and perception of cat face
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---
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## Types of simple cell receptive fields
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<div><img src="figs/image6_8eafbb8.png" height="100px"><figcaption></figcaption></div>
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Note:
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---
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## Some cells are selective for the direction of movement
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We use multiple types of visual information for perception:
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[https://www.youtube.com/watch?v=y_l4kQ5wjiw](https://www.youtube.com/watch?v=y_l4kQ5wjiw)
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<div><img src="figs/image7_50fb6ef.png" height="100px"><figcaption></figcaption></div>
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Note:
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others selective for movement, disparity
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* david hubel 1:24-2:18:
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* 125 million rods and cones in each eye.
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* misha pavel, sobel filter
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* try to build a robot to see and interpret images and it's hard.
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: 4:45 nice example of movement and perception of cat face
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---
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## Maps in the visual system- ocular dominance columns and orientation selectivity in visual cortex
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Ocular
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dominance
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Orientation
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selectivity
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<div><img src="figs/kandelschwartz-fig27-17_3d15d30.pdf" height="100px"><figcaption></figcaption></div>
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<div><img src="figs/kandelschwartz-fig27-14_e52cde6.pdf" height="100px"><figcaption></figcaption></div>
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Note:
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The organization of connections from each eye is shown here where if we were to look at a chunk of primary visual cortex from ferrets, cats, or monkeys we would find ocular dominance columns where the response properties of neighboring cells is dominated by that of one eye or the other and which can be demonstrated by electrophysiological recordings or by histological staining for cytochrome oxidase.
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Overlaid on this map of alternating ocular dominance columns is a map of orientation pinwheels in visual cortex shown by the isocontour lines on the surface *here* and by the colored orientation map *here* -->where the colored map represents the preferred response of neighboring neurons to high contrast edges presented at different orientations in the visual field.
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---
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## Columnar organization of ocular dominance
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<div><img src="figs/PN12132_f1003e0.jpg" height="100px"><figcaption>Neuroscience 2e Sinauer 2001</figcaption></div>
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<div><img src="figs/PN12131_f44cd94.jpg" height="100px"><figcaption>Neuroscience 2e Sinauer 2001</figcaption></div>
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Note:
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---
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## Localization of multiple visual areas in the human brain using fMRI
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<figure><img src="figs/Neuroscience5e-Fig-12.17-1R_copy_1a1c7c2.jpg" height="100px"><figcaption>Neuroscience 5e Fig. 12.17</figcaption></figure>
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<figure><img src="figs/Neuroscience5e-Fig-12.17-2R_copy_7168914.jpg" height="100px"><figcaption>Neuroscience 5e Fig. 12.17</figcaption></figure>
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Note:
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---
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## Subdivisions of the extrastriate cortex in the macaque monkey
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Van Essen 1992
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<div><img src="figs/ScreenShot2015-09-13at12.34.35PM_8ea399c.png" height="100px"><figcaption></figcaption></div>
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<div><img src="figs/ScreenShot2015-09-13at12.33.31PM_63619a9.png" height="100px"><figcaption></figcaption></div>
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Note:
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but he also emphasized that “the brain is a complex of widely and reciprocally interconnected systems and that the dynamic interplay of neural activity within and between systems is the very essence of brain function”. And indeed if you look at this—> anatomical wiring diagram for different visual areas represented by different colors you will notice that we use an organized constellation of brain regions to process and route different types of visual information and each one of these brain regions consists of many thousands of these basic cortical column building blocks described on the previous slide.
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---
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## Grandmother neurons in the human brain?
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[http://www.youtube.com/watch?v=Y7BZlDfVR6k](http://www.youtube.com/watch?v=Y7BZlDfVR6k)
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Quiroga et al., Nature 2005
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<div><img src="figs/ScreenShot2015-11-02at12.57.02PM_d992132.png" height="100px"><figcaption></figcaption></div>
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Note:
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Invariant visual representation by single neurons in the human brain. Quiroga et al., Nature 2005
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Recordings were made in medial temporal lobe of the cerebral cortex including entorhinal cortex and hippocampus course of clinical procedures to treat epilepsy.
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Interestingly this cell did not respond to pictures of Jennifer Aniston with Brad Pitt, maybe this cell had ‘moved on’ just like Miss Aniston. But other cells in this work did respond to selectively to Aniston with her friend’s costar Lisa Kudrow.
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One object per neuron?
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however these results may be best understood in a non-visual context. Some of the example cells responded not only to pictures but also to the printed name of a particular person or object. So this type of invariance must be based off learned associations.
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## Grandmother neurons: a sparse neural code
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C. Connor, Nature 2005
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<div><img src="figs/ScreenShot2015-11-03at9.53.42AM_c595899.png" height="100px"><figcaption></figcaption></div>
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Note:
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invariant visual representation by single neurons in the human brain. Quiroga et al., Nature 2005
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Connor Nature 2005, N&V on Quiroga et al:
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>a more technical term for the grandmother issue is ‘sparseness’.
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>At earlier stages in the object recognition pathway the neural code for an object is a broad activity pattern distributed across a population of neurons, each responsive to a discrete visual feature. At later, higher order processing stages, neurons become increasingly responsive for combinations of features and the code becomes increasingly sparse.
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sparse and non-variant
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---
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