Presentation is loading. Please wait.

Presentation is loading. Please wait.

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.

Similar presentations


Presentation on theme: "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."— Presentation transcript:

1 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:

2 Test Oct. 21 Review Session Oct 19 2pm in TH201 (that’s here)

3 The distinct modes of vision offered by feedforward and recurrent processing Victor A.F. Lamme and Pieter R. Roelfsema

4 The Role of “Extrastriate” Areas Different visual cortex regions contain cells with different tuning properties

5 The Feed-Forward Sweep What is the feed-forward sweep? What evidence is there that the feed-forward sweep is not sufficient to encode all aspects of vision

6 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

7 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.

8 Extra-RF Influences How do these data contradict the notion of a “classical” receptive field?

9 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

10 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

11 Extra-RF Influences How do these data contradict the notion of a “classical” receptive field? The classical receptive field provides no mechanism by which a neuron can be influenced by contextual information –Distant parts of the scene are not incorporated into the neurons representation

12 Extra-RF Influences How do these data contradict the notion of a “classical” receptive field? The classical receptive field provides no mechanism by which a neuron can be influenced by contextual information –Distant parts of the scene are not incorporated into the neurons representation

13 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?

14 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

15 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

16 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

17 Feedback Signals and the binding problem What is the binding problem?

18 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

19 Feedback Signals and the binding problem So what’s the connection between Attention and Recurrent Signals?

20 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

21 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?

22 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

23 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

24 Feedback Signals and Attention We’ll consider the role of feedback signals in attention in more detail as we discuss the neuroscience of attention


Download ppt "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."

Similar presentations


Ads by Google