Download 3680Lecture13 - U of L Class Index

Read this article for Friday next week
[1]Chelazzi L, Miller EK, Duncan J, Desimone
R. A neural basis for visual search in inferior
temporal cortex. Nature 1993; 363: 345-347.
Test Oct. 21
Review Session Oct 19 2pm in TH201 (that’s
here)
The distinct modes of vision
offered by feedforward and
recurrent processing
Victor A.F. Lamme and Pieter R. Roelfsema
The Role of “Extrastriate” Areas
• Different visual cortex
regions contain cells with
different tuning properties
Dichotomies in the Visual System?
• What are three dichotomies that Lamme identifies in
the visual system?
Dichotomies in the Visual System?
• What are three dichotomies that Lamme identifies in
the visual system?
• Dorsal vs. Ventral stream
Dichotomies in the Visual System?
• What are three dichotomies that Lamme identifies in
the visual system?
• Dorsal vs. Ventral stream
• Pre-attentive vs. Attentive
Dichotomies in the Visual System?
• What are three dichotomies that Lamme identifies in
the visual system?
• Dorsal vs. Ventral stream
• Pre-attentive vs. Attentive
• Conscious vs. Unconscious
The Feed-Forward Sweep
• What is the feed-forward sweep?
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
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
• What does it mean for an area to be “lower” or
“higher”
The Feed-Forward Sweep
• Hierarchy of visual cortical areas defined
anatomically
Dorsal “where”/”how”
Ventral “what”
The Feed-Forward Sweep
• Hierarchy can be defined more functionaly
• The feed-forward sweep is the initial response of each visual
area “in turn” as information is passed to it from a “lower” area
• Consider the latencies of the first responses in various areas
The Feed-Forward Sweep
• 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 Geniculostriate pathway (LGN to V1)
• Will be important in understanding blindsight
The Feed-Forward Sweep
• 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 feedforward 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!
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?
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?
• 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
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?
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?
Yes, but not during the feed-forward sweep
Recurrent Signals in Object
Perception
• 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
Recurrent Signals in Object
Perception
• 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?
Feedback Signals and 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
Feedback Signals and the binding
problem
• 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
Feedback Signals and Attention
• Instruction cues (for example in the Posner CueTarget 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?
Feedback Signals and Attention
• 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
Feedback Signals and Attention
• 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
Feedback Signals and Attention
• We’ll consider the role of feedback signals in
attention in more detail as we discuss the
neuroscience of attention