Transcript 3680Lecture15.pptx

Midterm 1
Oct. 6 in class
Review Session after class on Monday
Read this article for Friday Oct 8th!
Mental Representations
• Mental representations can start with sensory input
and progress to more abstract forms
– Local features such as colors, line orientation, brightness,
motion are represented at low levels
How might a neuron
“represent” the
presence of this
line?
Mental Representations
• Mental representations can start with sensory input
and progress to more abstract forms
– Local features such as colors, line orientation, brightness,
motion are represented at low levels
A “labeled line”
-Activity on this unit “means” that
a line is present
-Does the line actually have to be
present?
Mental Representations
• Mental representations can be “embellished”
- Kaniza Triangle is represented
in a way that is quite different
from the actual stimulus
-the representation is
embellished and extended
First Principles
• What are some ways that information might be
represented by neurons?
First Principles
•
What are some ways that information might be represented by
neurons?
– Magnitude might be represented by firing rate (e.g. brightness)
– Presence or absence of a feature or piece of information might be
represented by whether certain neurons are active or not – the “labeled line”
(e.g. color, orientation, pitch)
– Conjunctions of features might be represented by coordinated activity
between two such labeled lines
– Binding of component features might be represented by synchronization of
units in a network
VISION SCIENCE
Visual Pathways
• Themes to notice:
– Contralateral nature of visual system
– Information is organized:
• According to spatial location
• According to features and kinds of information
Visual Pathways
• Image is focused on the
retina
• Fovea is the centre of visual
field
– highest acuity
• Peripheral retina receives
periphery of visual field
– lower acuity
– sensitive under low light
Visual Pathways
• Retina has distinct layers
Visual Pathways
• Retina has distinct layers
• Photoreceptors
– Rods and cones respond to
different wavelengths
Visual Pathways
• Retina has distinct layers
• Amacrine and bipolar cells
perform “early” processing
– converging / diverging input
from receptors
– lateral inhibition leads to
centre/surround receptive
fields - first step in shaping
“tuning properties” of higherlevel neurons
Visual Pathways
•
Retina has distinct layers
– signals converge onto ganglion
cells which send action
potentials to the Lateral
Geniculate Nucleus (LGN)
– two kinds of ganglion cells:
Magnocellular and
Parvocellular
• visual information is already
being shunted through
functionally distinct pathways
as it is sent by ganglion cells
Visual Pathways
•
visual hemifields project
contralaterally
– exception: bilateral
representation of fovea!
•
Optic nerve splits at optic
chiasm
•
about 90 % of fibers project to
cortex via LGN
•
about 10 % project through
superior colliculus and pulvinar
– but that’s still a lot of fibers!
Note: this will be important
when we talk about
visuospatial attention
Visual Pathways
• Lateral Geniculate Nucleus
maintains segregation:
– of M and P cells (mango
and parvo)
– of left and right eyes
P cells project to layers 3 - 6
M cells project to layers 1 and 2
Visual Pathways
• Primary visual cortex
receives input from LGN
– also known as “striate”
because it appears striped
when labeled with some
dyes
– also known as V1
– also known as Brodmann
Area 17
Visual Pathways
• Primary cortex maintains
distinct pathways –
functional segregation
• M and P pathways synapse
in different layers
W. W. Norton
The Role of “Extrastriate” Areas
• Different visual cortex
regions contain cells with
different tuning properties
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
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.)
The Role of “Extrastriate” Areas
• Identifying colour sensitive regions
Subtract Voxel
intensities during
these scans…
…from voxel
intensities during
these scans
…etc.
Time ->
The Role of “Extrastriate” Areas
• result
– voxels are identified that are preferentially selective for
colour
– these tend to cluster in anterior/inferior occipital lobe
The Role of “Extrastriate” Areas
• similar logic was used to find motion-selective areas
Subtract Voxel
intensities during
these scans…
MOVING
…from voxel
intensities during
these scans
STATIONARY
MOVING
Time ->
STATIONARY
…etc.
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
The Role of “Extrastriate” Areas
• Thus PET studies doubly-dissociate colour and
motion sensitive regions
The Role of “Extrastriate” Areas
• Electrical response (EEG) to
direction reversals of moving
dots generated in (or near)
V5
• This activity is absent when
dots are isoluminant with
background
The Role of “Extrastriate” Areas
• V4 and V5 are doubly-dissociated in lesion literature:
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
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
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
• but…
• the spike timing matters too!
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?
Visual Neuron Responses
• LGN cells converge on “simple” cells in V1 imparting orientation
(and location) specificity
Visual Neuron Responses
• LGN cells converge on simple cells in V1 imparting orientation
specificity
• Thus we begin to see how a simple representation - the
orientation of a line in the visual scene - 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
– Why should this matter?
Visual Neuron Responses
• Edges are important because they are the boundaries
between objects and the background or objects and other
objects
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!
Visual Neuron Responses
• Boundaries between objects can be defined by color rather
than brightness
Visual Neuron Responses
• Boundaries between objects can be defined by texture
Visual Neuron Responses
• Boundaries between objects can be defined by motion and
depth cues