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Visual Pathways

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Visual Pathways. Primary cortex maintains distinct pathways – functional segregation M and P pathways synapse in different layers Ascending (i.e. feed-forward) projections synapse in middle layers Descending (i.e. feed-back) projections synapse in superfical and deep layers. W. W. Norton. - PowerPoint PPT Presentation
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Visual Pathways W. W. Norton Primary cortex maintains distinct pathways – functional segregation M and P pathways synapse in different layers Ascending (i.e. feed- forward) projections synapse in middle layers Descending (i.e. feed-back) projections synapse in superfical and
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Page 1: Visual Pathways

Visual Pathways

W. W. Norton

• Primary cortex maintains distinct pathways – functional segregation

• M and P pathways synapse in different layers

• Ascending (i.e. feed-forward) projections synapse in middle layers

• Descending (i.e. feed-back) projections synapse in superfical and deep layers

Page 2: Visual Pathways

Visual Pathways

• Visual scene is represented:– Retinotopically thus…

– spatiotopically

= Fovea

Tootell R B H et al. PNAS 1998;95:811-817

Page 3: Visual Pathways

How does the visual system represent visual information?

How does the visual system represent features of scenes?

• Vision is analytical - the system breaks down the scene into distinct kinds of features and represents them in functionally segregated pathways

Page 4: Visual Pathways

Visual Neuron Responses

• The notion of a receptive field is fundamental in vision science– A neuron’s receptive field is the

region in space in which a stimulus will evoke a response from that neuron

– Receptive field properties vary widely across visual neurons and are never just “ON” or “OFF”

– Unit recordings in LGN reveal a centre/surround receptive field

Page 5: Visual Pathways

Visual Neuron Responses

• Unit recordings in LGN reveal a centre/surround receptive field

• many arrangements exist, but the “classical” RF has an excitatory centre and an inhibitory surround

• these receptive fields tend to be circular - they are not orientation specific

How could the outputs of such cells be transformed into a cell with orientation specificity?

Page 6: Visual Pathways

Visual Neuron Responses

• LGN cells converge on “simple” cells in V1 imparting orientation (and location) specificity

Page 7: Visual Pathways

Visual Neuron Responses

• LGN cells converge on “simple” cells in V1 imparting orientation (and location) specificity

• Again, information is physically seperated into a “map”

Page 8: Visual Pathways

Visual Neuron Responses

• LGN cells converge on simple cells in V1 imparting orientation specificity

• Thus we begin to see how a simple representation – orientations of lines - can be maintained in the visual system– increase in spike rate of specific neurons indicates presence of a line

with a specific orientation at a specific location on the retina

– Reality is that spike rate probably is only one part of the story: information is coded in many ways e.g.

• Relative timing

• Graded potentials

Page 9: Visual Pathways

The Role of “Extrastriate” Areas

• Different visual cortex regions contain cells with different tuning properties

Page 10: Visual Pathways

The Role of “Extrastriate” Areas

• Consider two plausible models:

1. System is hierarchical:– each area performs some elaboration on the input it is given

and then passes on that elaboration as input to the next “higher” area

2. System is analytic and parallel:– different areas elaborate on different features of the input

Page 11: Visual Pathways

The Role of “Extrastriate” Areas

• Functional imaging (PET) investigations of motion and colour selective visual cortical areas

• Zeki et al.

• Subtractive Logic– stimulus alternates between two scenes that differ only in

the feature of interest (i.e. colour, motion, etc.)

Page 12: Visual Pathways

The Role of “Extrastriate” Areas

• Identifying colour sensitive regions

Subtract Voxel intensities during these scans… …from voxel

intensities during these scans

…etc.Time ->

Page 13: Visual Pathways

The Role of “Extrastriate” Areas

• result– voxels are identified that are preferentially selective for

colour– these tend to cluster in anterior/inferior occipital lobe

Page 14: Visual Pathways

The Role of “Extrastriate” Areas

• similar logic was used to find motion-selective areas

Subtract Voxel intensities during these scans… …from voxel

intensities during these scans

…etc.Time ->

MOVING STATIONARY MOVING STATIONARY

Page 15: Visual Pathways

The Role of “Extrastriate” Areas

• result– voxels are identified that are preferentially selective for

motion

– these tend to cluster in superior/dorsal occipital lobe near TemporoParietal Junction

– Akin to Human V5

Page 16: Visual Pathways

The Role of “Extrastriate” Areas

• Thus PET studies doubly-dissociate colour and motion sensitive regions

Page 17: Visual Pathways

The Role of “Extrastriate” Areas

• V4 and V5 are doubly-dissociated in lesion literature:

Page 18: Visual Pathways

The Role of “Extrastriate” Areas

• V4 and V5 are doubly-dissociated in lesion literature:

– achromatopsia (color blindness): • there are many forms of color blindness• cortical achromatopsia arises from lesions in the area of V4• singly dissociable from motion perception deficit - patients with

V4 lesions have other visual problems, but motion perception is substantially spared

Page 19: Visual Pathways

The Role of “Extrastriate” Areas

• V4 and V5 are doubly-dissociated in lesion literature:

– akinetopsia (motion blindness): • bilateral lesions to area V5 (extremely rare)• severe impairment in judging direction and velocity of

motion - especially with fast-moving stimuli• visual world appeared to progress in still frames• similar effects occur when M-cell layers in LGN are

lesioned in monkeys

Page 20: Visual Pathways

Visual Neuron Responses

• Edges are important because they are the boundaries between objects and the background or objects and other objects

Page 21: Visual Pathways

Visual Neuron Responses

• This conceptualization of the visual system was “static” - it did not take into account the possibility that visual cells might change their response selectivity over time

– Logic went like this: if the cell is firing, its preferred line/edge must be present and…

– if the preferred line/edge is present, the cell must be firing

• We will encounter examples in which these don’t apply!

• Representing boundaries must be more complicated than simple edge detection!

Page 22: Visual Pathways

Visual Neuron Responses

• Boundaries between objects can be defined by color rather than brightness

Page 23: Visual Pathways

Visual Neuron Responses

• Boundaries between objects can be defined by texture

Page 24: Visual Pathways

Visual Neuron Responses

• Boundaries between objects can be defined by motion and depth cues


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