16.899A: Physiology (contd) Lavanya Sharan January 24 th,
2011
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Before we start, a few caveats A lot is not known about how the
human visual system works. We (Alyosha + Lavanya) dont know a lot
about physiology. But, before you worry, a few lines from Marr
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Slide source: Nancy Kanwisher & Jim DiCarlo
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We care about big picture In this class, we are interested in
the underlying software/algorithm/computations Not in specifics of
the `particular hardware Want back pocket models for various
components of the human visual system Very few of these exist. Our
closest cousins: computational neuroscientists/cognitive
scientists/psychophysicists
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Overview of `particular hardware Retina, LGN What are visual
areas? Tools for studying human visual system Area V1 Beyond area
V1 What and where pathways Summary of what (and how little) we
know
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Primary Visual Pathway 1.Retina 2.Thalamus Lateral Geniculate
Nucleus (LGN) divided into magno and parvo layers 3.Primary visual
cortex (V1) 4.Extrastriate visual areas Each visual hemifield
projects to the opposite hemisphere Slide source: Jody Culham
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7 Slide source: Nancy Kanwisher & Jim DiCarlo
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Primary Visual Pathway 1.Retina 2.Thalamus Lateral Geniculate
Nucleus (LGN) divided into magno and parvo layers 3.Primary visual
cortex (V1) 4.Extrastriate visual areas Each visual hemifield
projects to the opposite hemisphere Slide source: Jody Culham
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What is a Visual Area? 1.Function an area has a unique pattern
of responses to different stimuli 2.Architecture different brain
areas show differences between cortical properties (e.g., thickness
of different layers, sensitivity to various dyes) 3.Connectivity
Different areas have different patterns of connections with other
areas 4.Topography many sensory areas show topography (retinotopy,
somatotopy, tonotopy) boundaries between topographic maps can
indicate boundaries between areas (e.g., separate maps of visual
space in visual areas V1 and V2 Slide source: Jody Culham
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Why are there so many visual areas? Source: Felleman & Van
Essen, 1991Source: Mapping the MInd cover image MAGNO quick and
dirty PARVO slow and detailed Slide source: Jody Culham
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More brain, more visual areas Slide source: Jody Culham
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Why not one really big visual area? V1 Slide source: Jody
Culham
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Why not a really big visual area? As areas become larger,
longer interconnections are required Limits on cortical thickness
and connections may constrain max area size Slide source: Jody
Culham
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Parallel processing is more efficient Teach neural network to
identify what and where One neural network with 18 nodes (~neurons)
devoted to both tasks versus One neural networks with two streams
of 9 nodes each (total = 18) After 300 training trials, the two
stream model outperformed the single-system model Rueckl, Cave
& Kosslyn, 1989 Slide source: Jody Culham
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Different Tasks Require Different Information different regions
may need to use different coding systems ventral stream:
object-centred dorsal stream: viewer-centred Slide source: Jody
Culham
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Wiring Constraints Source: Van Essen, 1997 David Van Essen
proposes that as the brain develops, areas that are richly
interconnected will be pulled together to form a gyrus (and those
that are weakly interconnected form sulci). Slide source: Jody
Culham
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Sulcal Formation: V1-V2 Source: Van Essen, 1997 The V1/V2
border provides one example of two richly interconnected areas that
form a gyrus. This arrangement also explains why maps in V1 and V2
are mirror images of each other! calcarine sulcus Slide source:
Jody Culham
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Optimized Connections Multidimensional Scaling strength of
connections can be used to infer spatial layout expected layout of
visual areas matches anatomy amazingly well Occipital Parietal
Temporal Malcolm Young Slide source: Jody Culham
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Tools for mapping human areas Neuropsychological Lesions
Temporary Disruption transcranial magnetic stimulation (TMS)
Electrical and magnetic signals electroencephalography (EEG)
magnetoencephalography (MEG) Brain Imaging positron emission
tomography (PET) functional magnetic resonance imaging (fMRI) Slide
source: Jody Culham
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Slide source: Nancy Kanwisher & Jim DiCarlo
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Overview of `particular hardware Retina, LGN What are visual
areas? Tools for studying human visual system Area V1 Beyond area
V1 What and where pathways Summary of what (and how little) we
know
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Cortical Receptive Fields Single-cell recording from visual
cortex David Hubel & Thorston Wiesel Stephen E. Palmer,
2002
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Cortical Receptive Fields Single-cell recording from visual
cortex Stephen E. Palmer, 2002
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Cortical Receptive Fields Three classes of cells in V1 Simple
cells Complex cells Hypercomplex cells Stephen E. Palmer, 2002
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Cortical Receptive Fields Simple Cells: Line Detectors Stephen
E. Palmer, 2002
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Cortical Receptive Fields Simple Cells: Edge Detectors Stephen
E. Palmer, 2002
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Cortical Receptive Fields Constructing a line detector Stephen
E. Palmer, 2002
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Cortical Receptive Fields Complex Cells 0o0o Stephen E. Palmer,
2002
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Cortical Receptive Fields Complex Cells 60 o Stephen E. Palmer,
2002
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Cortical Receptive Fields Complex Cells 90 o Stephen E. Palmer,
2002
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Cortical Receptive Fields Complex Cells 120 o Stephen E.
Palmer, 2002
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Cortical Receptive Fields Constructing a Complex Cell Stephen
E. Palmer, 2002
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Cortical Receptive Fields Hypercomplex Cells Stephen E. Palmer,
2002
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Cortical Receptive Fields Hypercomplex Cells Stephen E. Palmer,
2002
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Cortical Receptive Fields Hypercomplex Cells Stephen E. Palmer,
2002
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Cortical Receptive Fields Hypercomplex Cells End-stopped Cells
Stephen E. Palmer, 2002
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Cortical Receptive Fields End-stopped Simple Cells Stephen E.
Palmer, 2002
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Cortical Receptive Fields Constructing a Hypercomplex Cell
Stephen E. Palmer, 2002
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Overview of `particular hardware Retina, LGN What are visual
areas? Tools for studying human visual system Area V1 Beyond area
V1 What and where pathways Summary of what (and how little) we
know
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Logothetis 1999; from
http://psychology.uwo.ca/fMRI4Newbies/RetinotopicandEarlyVisualAreas.html
Overview of visual areas
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Macaque & human visual areas are similar Tootell et al.
2003; from
http://psychology.uwo.ca/fMRI4Newbies/RetinotopicandEarlyVisualAreas.html
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Slide source: Nancy Kanwisher & Jim DiCarlo
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Retinotopy (Tootell et al. 1982) Adjacent parts of visual field
are mapped to adjacent parts of cortex. Not all visual areas have
retinotopy, may be graded. Slide source: Nancy Kanwisher & Jim
DiCarlo
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Slide source: Nancy Kanwisher, Jim DiCarlo, David Heeger
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Why edges? So, why edge-like structures in the Plenoptic
Function?
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Two visual pathways The two visual processing streams for
different visual percepts: What (ventral stream)- object
recognition main input from slow and detailed parvo system Where or
How (dorsal stream) - spatial perception, motor planning main input
from quick and dirty magno system Source: Mishkin &
Ungerleider, 1982 Slide source: Jody Culham
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Two visual pathways The two visual processing streams for
different visual percepts: What (ventral stream)- object
recognition main input from slow and detailed parvo system Where or
How (dorsal stream) - spatial perception, motor planning main input
from quick and dirty magno system Source: Mishkin &
Ungerleider, 1982 Slide source: Jody Culham
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The What Pathway body motionfacesplacesbodiesobjects Other
Visual Areas contain more complex receptive fields Temporal Lobe
contains many specialized areas for recognizing various things
Slide source: Jody Culham
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The Where or How Pathway eye movements grasping and reaching
motion perception Parietal Lobe contains many specialized areas for
using vision to guide actions in space head movements attention
Slide source: Jody Culham
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Slide source: Nancy Kanwisher & Jim DiCarlo
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Summary Low-level areas Filter banks, SIFT, HOG for color,
orientation, spatial frequencies, motion High-level areas Desired
output from computer vision systems e.g., segmentation, robust
object/scene/texture recognition, motion understanding and planning
Middle-level area Where the magic happens No one (neuroscientists,
psychologists, computer scientists, etc.) really understands this
stage of processing. For more, come find us for pointers to
papers/books/readings and people to talk to.