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 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?

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