We are on track for an exam on
NOVEMBER 2nd
To cover everything since last exam up to
Friday the 28th
What can a visual neuron “know”
about the image?
•
If a neuron is only an edge detector and/or only has a small receptive field, it can’t
represent information about the relationship between the contents of its receptive
field and other receptive fields elsewhere in the display.
•
Also the 2004 Harvard – Yale Game:
Visual Neuron Responses
• Boundaries between objects can be defined by texture
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
Extra-RF Influences
• One thing they seem to be doing is helping each
other figure out what aspects of the entire scene are
contained within a given receptive field
– 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
Note that these are
responses to the
same stimulus!
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
•
Consider this analogy:
– Imagine when each fan puts up a card he or she is told to shake it – so that the
entire scene is full of shaking cards
– After some delay, the fans holding up the cards that make up the word are told
to keep shaking but the fans holding background cards are told to stop…the
words will be enhanced
– But the fans can’t each figure that out on their own because they don’t actually
know how their one card relates to the rest of the scene
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