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