The Role of “Extrastriate” Areas

1. 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.)

2. The Role of “Extrastriate” Areas • Identifying colour sensitive regions Subtract Voxel intensities during these scans… …from voxel intensities during these scans …etc. Time ->

3. The Role of “Extrastriate” Areas • result • voxels are identified that are preferentially selective for colour • these tend to cluster in anterior/inferior occipital lobe

4. The Role of “Extrastriate” Areas • similar logic was used to find motion-selective areas Subtract Voxel intensities during these scans… …from voxel intensities during these scans …etc. STATIONARY STATIONARY MOVING MOVING Time ->

5. The Role of “Extrastriate” Areas • 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

6. The Role of “Extrastriate” Areas • Thus PET studies doubly-dissociate colour and motion sensitive regions

7. The Role of “Extrastriate” Areas • 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

8. The Role of “Extrastriate” Areas • 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

9. The Role of “Extrastriate” Areas • Consider two plausible models: • 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 • System is analytic and parallel: • different areas elaborate on different features of the input

10. 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!

11. 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?

12. Visual Neuron Responses • LGN cells converge on “simple” cells in V1 imparting orientation (and location) specificity

13. Visual Neuron Responses • 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?

14. Visual Neuron Responses • Edges are important because they are the boundaries between objects and the background or objects and other objects

15. Visual Neuron Responses • 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!

16. Visual Neuron Responses • Boundaries between objects can be defined by color rather than brightness

17. Visual Neuron Responses • Boundaries between objects can be defined by texture

18. Visual Neuron Responses • Boundaries between objects can be defined by motion and depth cues

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

20. The Feed-Forward Sweep • What is the feed-forward sweep?

21. 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

22. 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”

23. The Feed-Forward Sweep • Hierarchy of visual cortical areas defined anatomically Dorsal “where”/”how” Ventral “what”

24. 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

25. 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 Geniculo-striate pathway (LGN to V1) • Will be important in understanding blindsight

26. 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 feed-forward sweep

27. 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!

28. 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?

29. 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”

30. 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

31. 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.

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

33. 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

34. 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

35. 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?

36. 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

37. 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

38. 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

39. Feedback Signals and the binding problem • What is the binding problem?

40. 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

41. Feedback Signals and the binding problem • So what’s the connection between Attention and Recurrent Signals?

42. 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

43. 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?

44. 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

45. 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

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