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Prefrontal cortex: categories, concepts and cognitive control Earl K. Miller Picower Center for Learning and Memory, PowerPoint PPT Presentation


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Prefrontal cortex: categories, concepts and cognitive control Earl K. Miller Picower Center for Learning and Memory, RIKEN-MIT Neuroscience Research Center, and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology www.millerlab.org. Sensory. Motor.

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Prefrontal cortex: categories, concepts and cognitive control Earl K. Miller Picower Center for Learning and Memory,

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Prefrontal cortex: categories, concepts and cognitive control

Earl K. Miller

Picower Center for Learning and Memory,

RIKEN-MIT Neuroscience Research Center, and

Department of Brain and Cognitive Sciences,

Massachusetts Institute of Technology

www.millerlab.org


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Sensory

Motor

Executive (cognitive) control – The ability of the brain to wrest control of its processing from reflexive reactions to the environment in order to direct it toward unseen goals. Volition, goal-direction

Basic sensory and motor functions


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Sensory

Motor

Consolidation(long-term storage)

Learning and memory

Memories, habits and skills

(Hippocampus, basal ganglia, etc.)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Sensory

Motor

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Consolidation(long-term storage)


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Our Methods:

Train monkeys on tasks designed to isolate cognitive operations related to executive control.

Record from groups of single neurons whilemonkeys perform those tasks.


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Learning and memory

(Hippocampus, basal ganglia, etc.)

Executive Functionsgoal-related information

Top-down

Selection(flexibility)

Sensory

Motor

Bottom-up

Consolidation(long-term storage)


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Perceptual Categories

David Freedman Maximillian RiesenhuberTomaso Poggio

Earl Miller

www.millerlab.org


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Perceptual Categorization: “Cats” Versus “Dogs”

Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2001) Science, 291:312-316

Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2002) J. Neurophysiology, 88:914-928.

Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K, (2003) J. Neuroscience, 23:5235-5246 .

60% Dog

Morphs

60% Cat

Morphs

80% Cat

Morphs

80% Dog

Morphs

Prototypes

100% Dog

Prototypes

100% Cat

Category boundary


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“Cats”

Category boundary

“Dogs”


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Delayed match to category task

RELEASE(Category Match)

.

.

.

.

(Match)

Fixation

Sample

500 ms.

.

HOLD

(Category Non-match)

Delay

600 ms.

1000 ms.

Test

Test object is a “match” if it the

same category (cat or dog) as the

sample

(Nonmatch)


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Fixation

Sample

Delay

Test

13

100% Dog

P > 0.1

80:20 Dog:Cat

60:40 Dog:Cat

10

Firing Rate (Hz)

Cats vs. DogsP < 0.01

7

4

100% Cat

80:20 Cat:Dog

P > 0.1

60:40 Cat:Dog

1

-500

0

500

1000

1500

2000

Time from sample stimulus onset (ms)

A “Dog Neuron” in the Prefrontal Cortex


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60% Dog

Morphs

60% Cat

Morphs

80% Cat

Morphs

80% Dog

Morphs

Prototypes

100% Dog

Prototypes

100% Cat

Category boundary

To test the contribution of experience, we moved the category boundaries and retrained a monkey


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To test the contribution of experience, we moved the category boundaries and retrained a monkey

Old, now-irrelevant, boundary

New, now-relevant, boundary


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PFC neural activity shifted to reflect the new boundariesand no longer reflected the old boundaries

Old, now-irrelevant, boundary

New, now-relevant, boundary


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Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2001) Science, 291:312-316

Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2002) J. Neurophysiology, 88:914-928

???

Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K, (2003)J. Neuroscience, 23:5235-5246.


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D1

C1

C1

D1

C1

C1

D2

D2

D3

C1

D3

C1

C2

D1

D1

D1

C2

C1

D2

C2

D2

D2

C2

C1

D3

D3

D3

C2

C2

C1

D1

D1

C3

C3

D1

C2

D2

C3

D2

C3

D2

C2

D3

D3

C3

D3

C3

C2

D1

C3

D2

C3

C2

D3

C3

ITC

C3

C1

D1

C1

D2

C1

“cats”

D3

C1

D1

C2

D2

C2

D3

C2

category boundary

D1

C3

D2

C3

D3

C3

“dogs”

D1

D3

D2

1.0

0.5

0

Normalized firing rate

Category Effects in the Prefrontal versus Inferior Temporal Cortex

Activity to individual stimuli along the 9 morph lines that crossed the category boundary

PFC

Cats Dogs

Cats Dogs


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PFC

ITC

Category index values

Stronger category effects

Category Effects were Stronger in the PFC than ITC: Population

Index of the difference in activity to stimuli from different, relative to same, category


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Quantity (numerosity)

Andreas NiederDavid FreedmanEarl Miller

www.millerlab.org


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Behavioral protocol: delayed-match-to-number task

Release

Numbers 1 – 5were used

Hold

  • Preventing the monkey from memorizing visual patterns:

  • Position and size of dots shuffled pseudo-randomly.

  • Each numerosity tested with 100 different images per session.

  • All images newly generated after a session.

  • Sample and test images never identical.

A. Nieder, D.J. Freedman, and E.K. Miller (2002) Science, 297:1708-1711.


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Trained

Equal area

Equal circumference

Low density

High density

Variable features

‘Shape’

Linear

Standard stimulus

Monkeys instantly generalized acrossthe control stimulus sets.


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Standard stimulus

Sample Delay

Equal area

Average sample interval activity


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Standard stimulus

Sample Delay

Variable features

Average delay interval activity


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Low density

Sample Delay

High density

Average sample interval activity


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1

0

0

)

1

0

0

)

%

%

(

(

e

e

s

s

n

7

5

n

7

5

o

o

p

p

s

s

e

e

r

r

5

0

5

0

d

d

e

e

z

z

i

i

l

l

a

a

m

2

5

m

2

5

r

r

o

o

N

N

0

0

0

2

4

6

8

1

0

1

2

0

2

4

6

8

1

0

1

2

P

r

e

f

e

r

r

e

d

n

u

m

e

r

o

s

i

t

y

P

r

e

f

e

r

r

e

d

n

u

m

e

r

o

s

i

t

y

Characteristics of Numerosity

  • Preservation of numerical order – numbers are not isolatedcategories.

  • Numerical Distance Effect – discrimination between numbersimprove with increasing distance between them(e.g., 3 and 4 are harder to discriminate than 3 and 7)

PFC neurons show tuning curves for number.


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Characteristics of Numerosity

  • Preservation of numerical order – numbers are not isolatedcategories.

  • Numerical Distance Effect – discrimination between numbersimprove with increasing distance between them.

  • Numerical Magnitude Effect – discrimination between numbers of equal numerical distance is increasingly difficult as their size increases (e.g., 1 and 2 are easier to tell apart than 5 and 6).


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Numerical Magnitude Effect

Average width of populationtuning curves

Average population tuning curve for each number

1

0

0

3

.

0

)

%

(

2

.

5

7

5

e

s

n

o

2

.

0

p

Bandwidth of tuning curves

s

5

0

e

r

1

.

5

d

e

z

i

l

2

5

a

1

.

0

m

r

o

N

0

0

5

1

2

3

4

5

1

2

3

4

5

Numerosity

Numerosity

Neural tuning becomes increasing imprecise with increasingnumber. Therefore, smaller size numbers are easier todiscriminate.


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Scaling of numerical representations

Linear-coding hypothesis

Non-linear compression hypothesis

  • symmetricdistributions on linear scale (centered on numbers)

  • wider distributions in proportion to increasing quantities

  • symmetric distributions on a logarithmically compressed scale

  • standard deviations of distributions constant across quantities

asymmetric on log scale

asymmetric on linear scale


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Non-linear scaling of behavioral data

Logarithmic scaling


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Non-linear scaling of neural data

Logarithmic scaling


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Scaling of numerical representations

Linear-coding hypothesis

Non-linear compression hypothesis

  • symmetricdistributions on linear scale (centered on numbers)

  • wider distributions in proportion to increasing quantities

  • symmetric distributions on a logarithmically compressed scale

  • standard deviations of distributions constant across quantities

asymmetric on log scale

asymmetric on linear scale


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Scaling of numerical representations

Linear-coding hypothesis

Non-linear compression hypothesis

  • symmetricdistributions on linear scale (centered on numbers)

  • wider distributions in proportion to increasing quantities

  • symmetric distributions on a logarithmically compressed scale

  • standard deviations of distributions constant across quantities

asymmetric on log scale

asymmetric on linear scale


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Number-encoding neurons

A. Nieder and E.K. Miller (in preparation)

A. Nieder, D.J. Freedman, and E.K. Miller (2002)Science, 297:1708-1711.

A. Nieder and E.K. Miller (in preparation)


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Abstract number-encoding neurons

Parietal CortexN = 404

Lateral PrefrontalCortexN = 352

Inferior Temporal CortexN = 77

16


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Standard stimulus

Low density

Equal circumference

High density

Inferior Temporal Cortex


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Behavior-guiding Rules

Jonathan WallisWael Asaad

Kathleen AndersonGregor RainerEarl Miller

www.millerlab.org


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CONCRETE

ABSTRACT

Asaad, Rainer, & Miller (1998)(also see Fuster, Watanabe,Wise et al)

Asaad, Rainer, & Miller (2000)task context

What is a rule?

Rules are conditional associations that describe the logic of a goal-directed task.

Wallis et al (2001)


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Sample

Test

Release

Hold

Match Rule(same)

Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956


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Sample

Release

Hold

Sample

Test

Hold

Release

Nonmatch Rule(different)

Test

Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956


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Sample

Release

Hold

Match Rule(same)

Sample

Test

Hold

Release

Nonmatch Rule(different)

Test

The rules were made abstract by training monkeys until they couldperform the task with novel stimuli


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+ juice

OR

Match

+ low tone

+ no juice

OR

Nonmatch

+ high tone

Sample + Cue


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Match Neuron

Cue


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Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956


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Rule Representation in Other Cortical Areas

PMC

PFC

ITC


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Timecourse of Rule-Selectivity Across the PFC Population:Sliding ROC Analysis

TEST

SAMPLE

PFC

ROC Value

Number of neurons(All recorded neurons)

Time from sample onset (ms)

Note: ROC Values are sorted by each time bin independently

Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology


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Rule Representation in Other Cortical Areas

PMC

PFC

ITC


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Abstract Rule-Encoding in Three Cortical Areas

PFC

Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology


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Abstract Rule-Encoding in Three Cortical Areas

PFC

Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

ITC

Wallis and Miller, in preparation


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Abstract Rule-Encoding in Three Cortical Areas

PMC

Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

PFC

Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

ITC

Wallis and Miller, in preparation


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TEST

SAMPLE

TEST

SAMPLE

PFC

PMC

ROC Value

Time from sample onset (ms)

Number of neurons(All recorded neurons)

Abstract Rule-Encoding was Stronger and Appeared Earlier in the PMC than PFC

Wallis and Miller, in press, J. Neurophysiol.

Median = 410

Median = 310

PMC

PFC

Number of neurons

Latency for rule-selectivity (msec)


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Abstract Rule-Encoding in Three Cortical Areas

PMC

Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

PFC

Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

ITC

Wallis and Miller, in preparation


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CONCLUSIONS:

1. Goal-related information, including the categories and concepts needed for executive control, is represented in the PFC while irrelevant details are largely discarded.

2. Neural representations of categories and concepts are stronger and more explicit in the PFC than in cortical areas that provide the PFC with visual input (“cats and dogs”, numbers). Highly familiar rules may be more strongly encoded in the PMC than PFC.

3. This ability of the PFC and related areas to convey categories, concepts and rules may reflect their role in acquiring and representing the formal demands of tasks, the internal models of situations and courses of actionthat provide a foundation for complex, intelligent behavior.

A Model of PFC function:Miller, E.K. (2000) The prefrontal cortex and cognitive control. Nature Reviews Neuroscience, 1:59-65

Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202

For reprints etc: www.millerlab.org


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The PF cortex and cognitive control

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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Reward signals(VTA neurons?)

The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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Reward signals(VTA neurons?)

The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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The PF cortex and cognitive control

PF cortex

At home

Guest

Phone rings

Answer

Don’t

answer

Inactive

Active


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PF cortex

The prefrontal cortex may be like a switch operator in a system of railroad tracks:

Its integrative anatomy allows it to rapidly acquire a “map” that specifies which pattern of “tracks” (neural pathways) are needed to solve a given task.


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PF cortex

The prefrontal cortex may be like a switch operator in a system of railroad tracks:

Its integrative anatomy allows it to rapidly acquire a “map” that specifies which pattern of “tracks” (neural pathways) are needed to solve a given task.

The PF cortex actively maintains this pattern during task performance, allowing feedback signals to bias the flow of activity in other brain areas along task-appropriate pathways.

GOAL-DIRECTIONFLEXIBILITY


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Miller Lab @ MIT (www.millerlab.org)

Other Miller Lab members:

Tim Buschman

Mark Histed

Christopher Irving

Cindy Kiddoo

Kristin Maccully

Michelle MachonAnitha PasupathyJefferson Roy

Melissa Warden

Categories:

David Freedman

Max Riesenhuber (Poggio lab)

Tomaso Poggio

Numbers:

Andreas Nieder

David Freedman

Rules:

Jonathan WallisWael AsaadKathy AndersonGregor Rainer


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