Earl Miller

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

Basic sensory and motor functions

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

Sensory Motor

Learning and memory (Hippocampus, basal

ganglia, etc.)

Memories, habits and skills

Consolidation(long-term storage)

Sensory Motor

Executive Functionsgoal-related information


ganglia, etc.)


Sensory Motor

Bot

tom

-up


ganglia, etc.)


Top-down

Selection(flexibility)


Sensory Motor

Bot

tom

-up


ganglia, etc.)


Top-down



Sensory Motor


ganglia, etc.)


Top-down



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

Record from groups of single neurons whilemonkeys perform those tasks.

Our Methods:

Sensory Motor

Bot

tom

-up


ganglia, etc.)


Top-down



Perceptual Categories

David Freedman Maximillian Riesenhuber

Tomaso PoggioEarl Miller

www.millerlab.org

Category boundary

Prototypes

100% Cat

80% Cat Morphs

60% Cat Morphs

60% Dog Morphs 80% Dog

Morphs

Prototypes 100% Dog

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 .

“Cats”

“Dogs”

Category boundary

.. .

.

.

FixationSample

Delay

Test(Nonmatch)

(Match)

600 ms.

1000 ms.

500 ms.

Delayed match to category task

Test object is a “match” if it thesame category (cat or dog) as thesample

RELEASE(Category Match)

HOLD(Category Non-match)

A “Dog Neuron” in the Prefrontal Cortex

-500 0 500 1000 1500 20001

4

7

10

13

Time from sample stimulus onset (ms)

Fir

ing

Ra

te (

Hz)

100% Dog 80:20 Dog:Cat 60:40 Dog:Cat

TestSample Delay

100% Cat

Fixation

60:40 Cat:Dog80:20 Cat:Dog

P > 0.1

P > 0.1

Cats vs. DogsP < 0.01

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

Category boundary

Prototypes

100% Cat

80% Cat Morphs

60% Cat Morphs

60% Dog Morphs 80% Dog

Morphs

Prototypes 100% Dog

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

Old, now-irrelevant, boundary

New, now-relevant, boundary

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

Old, now-irrelevant, boundary

New, now-relevant, boundary

PosteriorParietalCortex (PPC )

Ps

LateralPrefrontalCortex (LPFC)

As

Cs

Ls

IPL

SPL

P arietal P ath w ay“w here”

IPS

Sts

AITCIT

PIT

Inferior TemporalCortex (IT)

T em p oralP ath w ay“w ha t”

???

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 .

Category Effects in the Prefrontal versus Inferior Temporal Cortex

“cats”

“dogs”

category boundary

C1

C2

C3

D2

D3D1

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

C1

C1

C1

C2

C2

C2

C3

C3

C3

D1

D2

D3

D1

D2

D3

D2

D3

D1

C1

C1

C1

C2

C2

C2

C3

C3

C3

D1

D2

D3

D1

D2

D3

D2

D3

D1

ITC

0 0.5 1.0

Normalized firing rate

Cats Dogs Cats DogsC1

C1

C1

C2

C2

C2

C3

C3

C3

D1

D2

D3

D1

D2

D3

D2

D3

D1

C1

C1

C1

C2

C2

C2

C3

C3

C3

D1

D2

D3

D1

D2

D3

D2

D3

D1

Category Effects were Stronger in the PFC than ITC: Population

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

-0.4 -0.2 0 0.2 0.4 0.60

10

20

30

40

50

60

70

Category Index Value

Num

ber o

f Neu

rons

-0.4 -0.2 0 0.2 0.4 0.60

10

20

30

40

50

Category Index Value

Num

ber o

f Neu

rons

ITC PFC

Stronger category effects

Category index values

Quantity (numerosity)

Andreas NiederDavid FreedmanEarl Miller

www.millerlab.org

Behavioral protocol: delayed-match-to-number task

Preventing the monkey from memorizing visual patterns:1. Position and size of dots shuffled pseudo-randomly. 2. Each numerosity tested with 100 different images per

session.3. All images newly generated after a session.4. Sample and test images never identical.

Fixation500 m s

Time

Sample800 m s

Fixation500 m s

Time

Sample800 m s

Delay1000 m s

Fixation500 m s

Time

Sample800 m s

Delay1000 m s

Test 1200 m s

M atch

Fixation500 m s

Time

Sample800 m s

Delay1000 m s

Test 1200 m s

M atch

N onm a tch

Fixation500 m s

Time

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

Numbers 1 – 5were used

Release

Hold

Standard stimulus

Equal area

Equal circumference

Variable features

‘Shape’

Linear

Low density

High density

Trained

Monkeys instantly generalized acrossthe control stimulus sets.

1 2 3 4 510

20

30

standard

Spi

ke r

ate

(Hz)

Numerosity

1 2 3 4 510

20

30 equal area

Spi

ke r

ate

(Hz)

Numerosity

Standard stimulus

Equal area 1 2 3 4 5

Spi

ke ra

te (H

z)

Time (ms)

Sample Delay

Average sample interval activity

Standard stimulus

Variable features

1 2 3 4 55

10

15

20 standard

Spi

ke r

ate

(Hz)

Numerosity

1 2 3 4 55

10

15

20

variable features

Spi

ke r

ate

(Hz)

Numerosity

1 2 3 4 5

Spi

ke ra

te (H

z)

Time (ms)

Sample Delay

Average delay interval activity

1 2 3 4 5

Spi

ke ra

te (H

z)

Time (ms)

Low density

High density

1 2 3 4 50

2

4

6

8

10 high density

Spi

ke r

ate

(Hz)

Numerosity

1 2 3 4 50

2

4

6

8

10

low density

Spi

ke r

ate

(Hz)

Numerosity

Sample Delay

Average sample interval activity

Characteristics of Numerosity

1. Preservation of numerical order – numbers are not isolatedcategories.

2. 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.

0 2 4 6 8 10 120

25

50

75

100

Preferred numerosity

Nor

mal

ized

resp

onse

(%)

0 2 4 6 8 10 120

25

50

75

100

Preferred numerosity

Nor

mal

ized

resp

onse

(%)

Characteristics of Numerosity

1. Preservation of numerical order – numbers are not isolatedcategories.

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

3. 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).

Numerical Magnitude Effect

1 2 3 4 50 5

1.0

1.5

2.0

2.5

3.0

Ban

dwid

th o

f tu

ning

cur

ves

Average population tuning curve for each number

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

Average width of populationtuning curves

Numerosity1 2 3 4 5

0

25

50

75

100

Nor

mal

ized

res

pons

e (%

)

Numerosity

Scaling of numerical representations

Linear-coding hypothesis Non-linear compression hypothesis

•symmetric distributions 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

0 2 4 6 8 1 0 1 2 1 4 1 6 1 80 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0

Am

plitu

de

Number of items (linear scale)

2 1 00 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0

Am

plitu

de

Number of items (log scale)

2 2 00 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0


0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 00 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0


asymmetric on log scale asymmetric on linear scale

1 2 3 4 5 6 7 8 9 10 11

0

20

40

60

80

100

monkey Tmonkey PaverageP

erfo

rman

ce (

% c

orre

ct)


1 5 10

0

20

40

60

80

100

monkey Tmonkey PaverageP

erfo

rman

ce (

% c

orre

ct)


Non-linear scaling of behavioral data

Logarithmic scaling

linear pow(1/2)pow(1/3) log0.90

0.95

1.00 *

Goo

dnes

s-of

-fit

(r2 )

Scale

linear pow(1/2)pow(1/3) log0.75

0.80

0.85

0.90

0.95

1.00 *

Goo

dnes

s-of

-fit

(r2 )

Scale

1 2 3 4 5

0

20

40

60

80

100

Nor

mal

ized

act

ivity

(%

)


1 5

0

20

40

60

80

100

Nor

mal

ized

act

ivity

(%

)


Logarithmic scaling

Non-linear scaling of neural data

Scaling of numerical representations

Linear-coding hypothesis Non-linear compression hypothesis

•symmetric distributions 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

0 2 4 6 8 1 0 1 2 1 4 1 6 1 80 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0

Am

plitu

de


2 1 00 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0

Am

plitu

de


2 2 00 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0


0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 00 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0


asymmetric on log scale asymmetric on linear scale

PosteriorParietalCortex (PPC )

Ps

LateralPrefrontalCortex (LPFC)

As

Cs

Ls

IPL

SPL

P arietal P ath w ay“w here”

IPS

Sts

AITCIT

PIT

Inferior TemporalCortex (IT)

T em p oralP ath w ay“w ha t”

Number-encoding neurons

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

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

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

Sts

Ps

As

Cs

Ls

55ip

VIP

7A

AIT

30 %

3 %0 % 12 % 4 %

7 %

4 %

55ip

VIP

7A

AIT

Parietal CortexN = 404

Abstract number-encoding neurons

Lateral PrefrontalCortex

N = 352

Inferior Temporal CortexN = 77

16

1 2 3 4 520

25

30

35 low density

Sp

ike

ra

te (

Hz)

Numerosity

1 2 3 4 520

25

30

35

high density

Sp

ike

ra

te (

Hz)

Numerosity

Low density

Inferior Temporal Cortex

1 2 3 4 510

15

20

25

standard

Sp

ike

ra

te (

Hz)

Numerosity

1 2 3 4 510

15

20

25 equal circumference

Sp

ike

ra

te (

Hz)

Numerosity

High densityEqual circumference

Standard stimulus

Behavior-guiding Rules

Jonathan WallisWael AsaadKathleen AndersonGregor RainerEarl Miller

www.millerlab.org

CONCRETE ABSTRACT

What is a rule?Rules are conditional associations that describe the

logic of a goal-directed task.

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

Wise et al)

Asaad, Rainer, & Miller (2000)task context

Wallis et al (2001)

Release

Hold

Match Rule(same)

Sample Test

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

Sample

Nonmatch Rule(different)

Test


Release

Hold

Hold

Release

Sample Test

Sample

Sample

Test

Test

Release

Hold

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

Match Rule(same)

Nonmatch Rule(different)

Hold

Release

+ juice

+ no juice

Match+ low tone

+ high tone

OR

OR

Sample + Cue

Nonmatch

Match Neuron

Cue

Rule Representation in Other Cortical Areas

PFC

ITC

PMC

SAMPLETEST

RO

C V

alue

Number of neurons

(All recorded neurons) Time from sample onset (ms)

PFC

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

Note: ROC Values are sorted by each time bin independentlyWallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

Rule Representation in Other Cortical Areas

PFC

ITC

PMC

PFC

Abstract Rule-Encoding in Three Cortical Areas

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

PFC

ITC


Wallis and Miller, in preparation



PFC

ITC

PMC




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

SAMPLE TEST

PMCSAMPLE TEST

RO

C V

alue

Number of neurons

(All recorded neurons)Time from sample onset (ms)

PFC

Latency for rule-selectivity (msec)

Num

ber

of n

euro

ns

Median = 410 Median = 310

PFC PMC

Wallis and Miller, in press, J. Neurophysiol.


PFC

ITC

PMC




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

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 action that 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, 1:59-65Miller, 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

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.

CONCLUSIONS:

Active Inactive

The PF cortex and cognitive control

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PF cortex

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PF cortex

Reward signals(VTA neurons?)

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PF cortex

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

PF cortex

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.

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

GOAL-DIRECTIONFLEXIBILITY

Categories:David FreedmanMax Riesenhuber (Poggio lab)Tomaso Poggio

Numbers:Andreas NiederDavid Freedman

Rules:Jonathan WallisWael AsaadKathy AndersonGregor Rainer

Other Miller Lab members:Tim BuschmanMark HistedChristopher IrvingCindy KiddooKristin Maccully Michelle Machon Anitha PasupathyJefferson RoyMelissa Warden

Miller Lab @ MIT (www.millerlab.org)

Earl Miller

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