Some concepts from Cognitive Psychology to review:ShadowingVisual SearchCue-target Paradigm
Hint: you’ll find these in Chapter 12
Read this article for next week:
A Neural Basis for Visual Search in Inferior Temporal Cortex
Leonardo Chelazzi et al. (1993) Nature
Attention Solves an Ambiguity ProblemSensory Input Ambiguity
Cell “tuned” to red. Should it fire?
Area V4 Receptive field = ~4 deg visual angle
Attention Solves an Ambiguity ProblemVisual search is the process of seeking
a target from a complex scene
Should the cell tuned to “red” fire: •whenever red is present in RF?
•In proportion to how much red is present?
•In a more sophisticated way?
Area V4 Receptive field = ~4 deg visual angle
Attention Solves an Ambiguity ProblemResponse Mapping Ambiguity
(e.g. Stroop Task)
Cell “tuned” to line orientation. Should it affect your response?
Area V4 Receptive field = ~4 deg visual angle
If you do computational neuroscience,This is why you should think about attention.
B L U E
Attention Solves a Network Complexity ProblemThe brain is a massively
interconnected network - each neuron makes ~ 1000 connections
Gordon Kindlmann & Andrew AlexanderUniversity of Wisconsin Van Essen, Andersen & Felleman (1992)
Attention Solves a Network Complexity ProblemOn the time scale of behaviour,
the network is anatomically hard-wired
Fast functional reconfiguration
Attention Solves a Network Complexity ProblemPoint to the red horizontal line
Attention Solves a Network Complexity ProblemPoint to the red horizontal line
Visual stimulus drives visual neurons
Black Brain Box Motor plan is executed
Attention Solves a Network Complexity ProblemPoint to the red horizontal line
Visual stimulus drives visual neurons
Black Brain Box Motor plan is executed
Attention Solves a Network Complexity Problem
Point to the red horizontal lineNotice the mapping is selective:
Attention Solves a Network Complexity Problem
Point to the red horizontal lineNotice the mapping is selective:
Attention Solves a Network Complexity Problem
Now point to the green vertical line
Notice the mapping is easily reconfigured
Attention Solves a Network Complexity Problem
Attention Solves a Network Complexity Problem Thus sensory neurons
are in some sense omnipotent
each one’s contribution to cognitive and motor networks is not determined by anatomical connectivity
it is determined dynamically by some control system
Attention Solves a Network Complexity Problem Notice this is an extension
of the “binding problem”
Cells representing features of the same objects must contribute to a “reconstituted” whole object representation
These cells must be “bound” to all the other cells mediating the current cognitive or motor behaviour
If you study the “connectome”, this is why you should think about attention.
Attention Solves a Network Complexity ProblemThe brain is a massively
interconnected network - each neuron makes ~ 1000 connections
Attention Solves a Network Complexity ProblemThe brain is a massively
interconnected network - each neuron makes ~ 1000 connections
X 1000
Attention Solves a Network Complexity ProblemThe brain is a massively
interconnected network - each neuron makes ~ 1000 connections
X 1000
X 1000
Attention Solves a Network Complexity ProblemThe brain is a massively
interconnected network - each neuron makes ~ 1000 connections
X 1000
X 1000
X 1000
X 1000
X 1000
Attention Solves a Network Complexity ProblemCrude AnalogyBy 4 synapses the tree comprises
more than 10 Billion cells!
Attention prevents runaway connectivity:◦Clearly the brain must have a system
by which information is routed appropriately through the network
Attention Solves a Network Complexity ProblemWhat does runaway connectivity look
like?Here’s a hint: the “feed forward”
sweep of signal following a visual event is relatively unconstrained by attention
Red = earliest response at this latencyYellow = has already responded
Lamme (2000)
By ~115 ms post-stimulus, much of the cortex has responded to the visual event
Attention Solves a Network Complexity ProblemWhat would be the consequence
if attention did not select cell assemblies?
Neural Gridlock? Maybe not the right concept.
Attention Solves a Network Complexity ProblemThe brain is a system of coupled
oscillatorsDriving such systems can trigger
unexpected synchronization
Attention Solves a Network Complexity ProblemClassic Example of spontaneous
synchronization
Attention Solves a Network Complexity Problem
See a fabulous TED talk about synchronization by Steven Strogatz at:
www.ted.com/talks/steven_strogatz_on_sync.html
Attention Solves a Network Complexity Problem Do brains exhibit
runaway global synchronization?
Yes, this is characteristic of certain kinds of epileptic seizures.
3 Hz “Spike and Wave” EEG pattern during absence seizure
Attention Solves a Network Complexity Problem OK so how might a brain solve this problem? How
might the attention system facilitate a dominant cell assembly and suppress others?
“Neuronal communication through neuronal coherence”
- Pascal Fries, TINS (2005)
Attention Solves a Network Complexity ProblemIndividual oscillators coupled to a
central oscillator
Attention Solves a Network Complexity ProblemRole of the “central oscillator” has been
called the “dominant network”
Communication-through-coherence suggests that oscillations within cell assemblies become phase locked
One set of such assemblies achieves global dominance by having their individual phases nudged into coherence