The Role of “Extrastriate” Areas

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.)


• Identifying colour sensitive regions

Subtract Voxel intensities during these scans… …from voxel

intensities during these scans

…etc.Time ->


• result– voxels are identified that are preferentially selective for

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


• 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


• 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


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


• 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


• 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


• 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

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?


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


• 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?


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


• 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!


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


• Boundaries between objects can be defined by texture


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

Feed-Forward and Feed-Back Processing in the Visual System

The Feed-Forward Sweep

• What is the feed-forward sweep?


• The feed-forward sweep is the initial response of each visual area “in turn” as information is passed to it from a “lower” area

• Characteristics:– a single spike per synapse– no time for lateral connections – no time for feedback connections



• What does it mean for an area to be “lower” or “higher”


• Hierarchy of visual cortical areas defined anatomically

Dorsal “where”/”how”

Ventral “what”


• Hierarchy can be defined more functionaly


• Consider the latencies of the first responses in various areas


• Thus the “hierarchy” of visual areas differs depending on temporal or anatomical features

• aspects of the visual system account for this fact:

– multiple feed-forward sweeps progressing at different rates (I.e. magno and parvo pathways) in parallel

• M pathway is myelinated

• P pathway is not

– signals arrive at cortex via routes other than the Geniculo-striate pathway (LGN to V1)

• Will be important in understanding blindsight


• The feed-forward sweep gives rise to the “classical” receptive field properties– tuning properties exhibited in very first spikes

• Orientation tuning in V1• Optic flow tuning in MST

– think of cortical neurons as “detectors” only during feed-forward sweep

After the Forward Sweep

• By 150 ms, virtually every visual brain area has responded to the onset of a visual stimulus

• But visual cortex neurons continue to fire for hundreds of milliseconds!




• What are they doing?




• What are they doing?

• with sufficient time (a few tens of ms) neurons begin to reflect aspects of cognition other than “detection”

Extra-RF Influences

• One thing they seem to be doing is helping each other figure out what aspects of the entire scene each RF contains

– That is, the responses of visual neurons begin to change to reflect global rather than local features of the scene

– recurrent signals sent via feedback projections are thought to mediate these later properties

Extra-RF Influences

• consider texture-defined boundaries– classical RF tuning

properties do not allow neuron to know if RF contains figure or background

– At progressively later latencies, the neuron responds differently depending on whether it is encoding boundaries, surfaces, the background, etc.

Extra-RF Influences

• How do these data contradict the notion of a “classical” receptive field?

Extra-RF Influences

• How do these data contradict the notion of a “classical” receptive field?

• Remember that for a classical receptive field (i.e. feature detector):

– If the neuron’s preferred stimulus is present in the receptive field, the neuron should fire a stereotypical burst of APs

– If the neuron is firing a burst of APs, its preferred stimulus must be present in the receptive field

Recurrent Signals in Object Perception

• Can a neuron represent whether or not its receptive field is on part of an attended object?

• What if attention is initially directed to a different part of the object?


• Can a neuron represent whether or not its receptive field is on part of an attended object?

• What if attention is initially directed to a different part of the object?

Yes, but not during the feed-forward sweep


• curve tracing– monkey indicates whether a

particular segment is on a particular curve

– requires attention to scan the curve and “select” all segments that belong together

– that is: make a representation of the entire curve

– takes time


• curve tracing– neuron begins to respond

differently at about 200 ms

– enhanced firing rate if neuron is on the attended curve

Feedback Signals and the binding problem

• What is the binding problem?


• What is the binding problem?• curve tracing and the binding problem:

– if all neurons with RFs over the attended curve spike faster/at a specific frequency/in synchrony, this might be the binding signal


• So what’s the connection between Attention and Recurrent Signals?

Feedback Signals and Attention

• One theory is that attention (attentive processing) entails the establishing of recurrent “loops”

• This explains why attentive processing takes time - feed-forward sweep is insufficient


• Instruction cues (for example in the Posner Cue-Target paradigm) may cause feedback signal prior to stimulus onset (thus prior to feed-forward sweep)

• think of this as pre-setting the system for the upcoming stimulus

• What does this accomplish?


• What does this accomplish?

• Preface to attention: Two ways to think about attention– Attention improves perception, acts as a gateway to memory

and consciousness

– Attention is a mechanism that routes information through the brain

• It is the brain actively reconfiguring itself by changing the way signals propagate through networks

• It is a form of very fast, very transient plasticity


• Put another way:

– It may strike you as remarkable that a single visual stimulus should “activate” so many brain areas so rapidly

– In fact it should be puzzling that a visual input doesn’t create a runaway “chain reaction”

• The brain is massively interconnected• Why shouldn’t every neuron respond to a visual stimulus


• We’ll consider the role of feedback signals in attention in more detail as we discuss the neuroscience of attention

The Role of “Extrastriate” Areas

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