Coherence

and

recurrency

maintenance

control

and

integration

in

working

memory

Working

memory

-LRB-

WM

-RRB-

including

a

central

executive

is

used

to

guide

behavior

by

internal

goals

or

intentions

We

suggest

that

WM

is

best

described

as

a

set

of

three

interdependent

functions

which

are

implemented

in

the

prefrontal

cortex

-LRB-

PFC

-RRB-

These

functions

are

maintenance

control

of

attention

and

integration

A

model

for

the

maintenance

function

is

presented

and

we

will

argue

that

this

model

can

be

extended

to

incorporate

the

other

functions

as

well

Maintenance

is

the

capacity

to

briefly

maintain

information

in

the

absence

of

corresponding

input

and

even

in

the

face

of

distracting

information

We

will

argue

that

maintenance

is

based

on

recurrent

loops

between

PFC

and

posterior

parts

of

the

brain

and

probably

within

PFC

as

well

In

these

loops

information

can

be

held

temporarily

in

an

active

form

We

show

that

a

model

based

on

these

structural

ideas

is

capable

of

maintaining

a

limited

number

of

neural

patterns

Not

the

size

but

the

coherence

of

patterns

-LRB-

i.e.

a

chunking

principle

based

on

synchronous

firing

of

interconnected

cell

assemblies

-RRB-

determines

the

maintenance

capacity

A

mechanism

that

optimizes

coherent

pattern

segregation

also

poses

a

limit

to

the

number

of

assemblies

-LRB-

about

four

-RRB-

that

can

concurrently

reverberate

Top-down

attentional

control

-LRB-

in

perception

action

and

memory

retrieval

-RRB-

can

be

modelled

by

the

modulation

and

re-entry

of

top-down

information

to

posterior

parts

of

the

brain

Hierarchically

organized

modules

in

PFC

create

the

possibility

for

information

integration

We

argue

that

large-scale

multimodal

integration

of

information

creates

an

episodic

buffer

and

may

even

suffice

for

implementing

a

central

executive

Introduction

When

we

return

home

from

work

we

sometimes

find

ourselves

deeply

engaged

in

thinking

about

an

unsolved

problem

Yet

at

the

same

time

we

manage

to

find

our

way

home

and

avoid

accidents

This

indicates

that

apparently

we

are

able

to

automatically

control

our

behavior

without

needing

conscious

attention

If

suddenly

an

obstacle

appears

however

we

are

also

able

to

switch

attention

in

a

split

second

Given

the

right

cues

we

may

also

remember

that

in

the

morning

we

planned

to

do

some

shopping

on

the

way

home

That

too

would

put

solving

the

problem

to

a

halt

and

bring

us

to

figure

out

the

best

route

to

the

shop

This

shows

that

control

may

be

taken

over

by

retrieving

a

plan

and

that

in

order

to

carry

it

out

we

need

access

to

a

large

database

including

a

route

planner

If

later

on

we

resume

thinking

about

the

problem

we

may

figure

out

a

solution

by

combining

known

elements

in

a

novel

configuration

and

then

start

thinking

how

to

use

it

in

the

future

The

description

given

above

is

an

example

of

the

coordinated

and

goal-directed

thoughts

and

actions

that

are

generated

in

our

brains

all

the

time

Clearly

such

coordinated

behavior

requires

a

versatile

system

of

cognitive

control

for

which

as

yet

there

does

not

seem

to

exist

an

adequate

concept

Some

of

the

contours

of

such

a

control

system

however

are

discernable

in

the

example

given

above

like

the

ability

to

maintain

representations

to

direct

attention

and

to

combine

previously

unrelated

information

An

early

and

influential

framework

for

the

study

of

control

and

coordination

of

cognitive

behavior

is

the

multi-component

model

of

working

memory

-LRB-

Baddeley

and

Hitch

-RRB-

Working

memory

-LRB-

WM

-RRB-

refers

to

a

limited

capacity

system

for

temporary

holding

and

manipulating

information

that

is

required

for

performing

a

wide

range

of

cognitive

tasks

such

as

comprehension

learning

and

reasoning

Originally

the

model

distinguished

three-components

A

general

control

system

or

central

executive

and

two

subsidiary

slave

systems

a

phonological

loop

and

a

visuo-spatial

sketch

pad

each

capable

of

maintaining

a

limited

amount

of

information

More

recently

-LRB-

Baddeley

-RRB-

an

episodic

buffer

was

added

to

connect

working

memory

with

long-term

memory

and

to

allow

binding

together

information

from

different

sources

into

integrated

episodes

The

central

executive

is

the

most

important

but

least

understood

component

of

WM

and

it

looks

conspicuously

like

a

homunculus

Baddeley

is

well

aware

of

this

criticism

but

he

defends

the

central

executive

concept

by

pointing

out

that

it

defines

a

problem

area

for

which

the

processes

have

to

be

specified

An

important

conceptual

model

was

suggested

by

Norman

and

Shallice

-LRB-

-RRB-

They

proposed

a

data

base

system

for

automatic

habitual

action

routines

containing

well-learned

stimulus-response

associations

and

cognitive

and

behavioral

skills

and

a

supervisory

attentional

system

capable

of

controlling

behavior

by

selectively

biasing

and

reconfiguring

the

available

skills

and

schemas

Automatic

control

develops

gradually

through

practice

as

learning

processes

create

associative

pathways

between

perception

and

action

The

conscious

form

of

control

is

applied

when

we

are

confronted

by

novel

and

unexpected

stimuli

and

when

we

have

the

intention

to

attain

specific

goals

In

this

case

automatic

actions

to

stimulus

patterns

in

the

environment

have

to

be

suppressed

and

replaced

by

novel

task

and

goal-directed

actions

The

model

of

Norman

and

Shallice

aimed

to

explain

deficits

of

executive

control

that

are

often

observed

in

patients

with

damage

in

prefrontal

cortical

areas

It

is

now

generally

accepted

that

the

prefrontal

cortex

-LRB-

PFC

-RRB-

is

of

crucial

importance

when

behavior

must

be

guided

and

controlled

by

internal

states

and

intentions

when

automatic

responses

have

to

be

suppressed

and

when

tasks

require

the

establishment

of

new

or

rapidly

changing

mappings

between

perception

and

action

-LRB-

e.g.

Goldman-Rakic

Miller

and

Cohen

Smith

and

Jonides

Wood

and

Grafman

-RRB-

Anatomically

the

PFC

is

well

positioned

to

coordinate

processing

in

the

rest

of

the

brain

It

consists

of

a

number

of

strongly

interconnected

areas

that

collectively

have

reciprocal

connections

with

virtually

all

other

neocortical

and

subcortical

structures

It

is

also

an

area

that

shows

late

development

both

phylogenetically

and

ontogenetically

reaching

maturity

only

in

adolescence

-LRB-

Fuster

-RRB-

Based

on

these

anatomical

considerations

we

have

suggested

-LRB-

Phaf

and

Wolters

-RRB-

that

working

memory

or

more

general

executive

functions

may

have

emerged

as

a

consequence

of

an

evolutionary

development

of

the

PFC.

This

development

may

have

created

the

possibility

for

neural

processes

that

could

run

-LRB-

partly

-RRB-

independent

of

present

input

and

output

but

that

also

could

modulate

and

thus

control

perception-action

relations

in

the

rest

of

the

cortex

In

this

view

the

role

of

PFC

in

controlling

behavior

is

modulatory

rather

than

transmissive

which

is

similar

to

the

role

assigned

to

PFC

in

several

other

models

-LRB-

e.g.

Norman

and

Shallice

Miller

and

Cohen

O'Reilly

et

al.

-RRB-

Whereas

simple

adaptive

behavior

rests

on

a

cycle

of

perception

action

and

perception-of-action

results

the

added

PFC

would

allow

an

internalization

of

this

loop

freeing

the

organism

of

the

restriction

of

being

aware

of

or

acting

upon

physically

present

objects

or

situations

only

In

fact

it

would

implement

a

medium

for

creating

a

virtual

world

i.e.

manipulating

internal

representations

that

are

independent

of

the

present

environment

-LRB-

for

a

similar

idea

describing

thinking

as

simulated

behavior

and

perception

see

Hesslow

-RRB-

More

specifically

we

suggested

that

the

development

of

PFC

created

the

possibility

to

maintain

physically

absent

information

in

an

active

state

by

recurrent

connections

-LRB-

loops

-RRB-

between

PFC

and

the

rest

of

the

cortex

The

recurrent

activity

in

these

loops

may

affect

subsequent

perceptual

and

motor

processing

i.e.

it

can

redirect

attention

and

control

actions

by

activating

or

inhibiting

particular

motor

programs

By

assuming

interactions

and

integration

between

loops

more

complex

forms

of

representation

and

control

may

develop

For

instance

recurrent

connections

with

memory

systems

would

allow

access

to

all

stored

information

and

mechanisms

for

combining

information

in

the

loops

would

allow

the

formation

and

updating

of

future

goal

states

and

ways

to

achieve

them

A

taxonomy

of

working

memory

functions

Although

there

is

no

generally

accepted

taxonomy

of

executive

functions

we

may

speculate

which

functions

would

be

required

It

can

be

argued

for

instance

that

to

be

able

to

exert

top-down

control

over

cognitive

processes

patterns

of

activity

representing

task

relevant

information

must

be

actively

maintained

in

the

PFC.

Moreover

PFC

must

be

able

to

generate

biasing

signals

to

guide

the

flow

of

information

by

selectively

inhibiting

or

activating

particular

representations

and

pathways

in

other

parts

of

the

brain

This

interaction

between

PFC

and

posterior

parts

of

the

brain

would

also

be

needed

to

retrieve

any

relevant

information

stored

in

memory

Finally

PFC

must

be

able

to

integrate

information

from

different

sources

to

implement

goal

directed

behavior

over

time

We

suggest

therefore

that

three

main

executive

functions

have

to

be

distinguished

in

PFC.

The

first

function

is

maintenance

i.e.

holding

a

limited

amount

of

currently

needed

information

i.e.

all

task-relevant

information

supplied

by

preceding

events

in

an

active

form

The

second

function

is

attentional

control

i.e.

top-down

controlled

selective

activation

of

task-relevant

stimulus

representations

and

responses

Only

selective

activation

is

required

because

in

a

competitive

system

this

automatically

induces

selective

inhibition

of

task-irrelevant

stimuli

and

responses

The

third

function

is

integration

This

function

consists

of

the

ability

to

flexibly

combine

and

reorganize

information

from

different

sources

in

the

service

of

controlling

task

execution

This

also

includes

control

over

search

in

LTM

monitoring

and

evaluating

results

of

actual

or

imagined

actions

and

sequencing

operations

that

are

needed

for

planning

decision

making

and

problem

solving

In

our

view

the

central

executive

operations

in

PFC

are

strongly

interdependent

and

continuously

interacting

with

processing

in

other

cortical

areas

-LRB-

see

also

Duncan

for

emphasis

on

interdependence

of

control

working

memory

and

attention

in

PFC

-RRB-

A

somewhat

similar

view

of

PFC

functions

was

suggested

by

Smith

and

Jonides

-LRB-

-RRB-

They

proposed

a

distinction

between

short-term

storage

and

two

executive

processes

selective

attention

and

task

management

In

this

paper

we

will

first

discuss

the

neuroanatomical

correlates

of

these

hypothesized

interdependent

working

memory

functions

Then

we

will

present

a

neural

model

for

binding

and

segregation

in

working

memory

to

simulate

the

maintenance

function

This

model

is

based

on

recent

ideas

about

synchronization

of

activation

patterns

in

networks

and

it

is

able

to

explain

capacity

limitations

of

maintenance

in

WM.

Next

extensions

of

this

model

will

be

discussed

to

realize

the

attentional

control

mechanism

It

is

suggested

that

modulatory

effects

within

PFC

may

selectively

enhance

or

suppress

representations

in

posterior

cortex

by

recurrent

connections

Finally

we

will

present

some

ideas

on

how

to

realize

integration

and

manipulation

functions

given

that

large-scale

multi-modal

integration

is

necessary

to

explain

coherence

and

coordination

of

behavior

Neuroanatomical

correlates

of

working

memory

in

PFC

Generally

the

brain

shows

a

remarkable

specialization

of

anatomically

distinctive

areas

performing

specific

cognitive

functions

Also

the

PFC

comprises

a

number

of

cyto-architectural

distinctive

areas

or

modules

that

differ

in

patterns

of

connectivity

with

other

brain

regions

-LRB-

e.g.

Miller

and

Cohen

Wood

and

Grafman

-RRB-

Much

effort

has

been

invested

in

finding

functional

specializations

within

the

PFC

i.e.

the

extent

that

different

areas

or

modules

of

PFC

are

selectively

involved

in

encoding

particular

types

of

information

-LRB-

e.g.

verbal

versus

visual

-RRB-

or

in

implementing

various

processing

functions

-LRB-

e.g.

maintenance

versus

attentional

control

-RRB-

Although

this

debate

is

still

ongoing

there

seems

to

be

a

growing

consensus

that

broadly

speaking

three

major

areas

in

PFC

seem

to

be

distinguishable

in

terms

of

processing

specific

types

of

information

-LRB-

Fuster

Wood

and

Grafman

-RRB-

probably

with

additional

sub-specializations

First

ventromedial

and

orbitofrontal

regions

-LRB-

BA

-RRB-

seem

specifically

involved

in

representing

and

processing

reward

and

affective

information

and

thus

participate

in

emotionally

and

motivationally

driven

behavior

-LRB-

e.g.

Fuster

Rolls

-RRB-

These

regions

have

major

reciprocal

connections

with

the

temporal

lobe

and

the

amygdale

complex

Second

medial

regions

of

the

PFC

-LRB-

in

particular

the

anterior

cingulate

cortex

BA

-RRB-

are

suggested

to

be

involved

in

error

monitoring

and

in

detecting

conflicts

between

competing

stimuli

and

responses

-LRB-

e.g.

Ridderinkhof

et

al.

-RRB-

Third

lateral

and

anterior

PFC

areas

-LRB-

BA

see

Petrides

for

a

review

of

the

architectonic

organization

of

this

area

-RRB-

are

supposedly

involved

in

all

executive

functions

that

are

needed

for

organized

goal-directed

behavior

such

as

the

selection

of

goal-relevant

information

the

manipulation

and

maintenance

of

information

and

monitoring

multiple-events

-LRB-

e.g.

Miller

and

Cohen

Petrides

-RRB-

This

area

has

strong

reciprocal

connections

with

parietal

and

temporal

association

areas

the

hippocampal

formation

and

with

all

other

PFC

regions

Damage

to

lateral

PFC

typically

impairs

the

ability

to

formulate

and

carry

out

plans

and

sequences

of

actions

-LRB-

Fuster

-RRB-

and

to

control

long-term

memory

encoding

and

retrieval

-LRB-

Blumenfeldt

and

Ranganath

Tomita

et

al.

-RRB-

There

is

also

increasing

evidence

however

that

localization

of

informational

content

is

not

precise

but

rather

a

matter

of

degree

A

review

of

imaging

results

showed

that

quite

different

cognitive

demands

-LRB-

i.e.

response

conflict

task

novelty

WM

delay

and

load

and

perceptual

difficulty

-RRB-

induced

very

similar

patterns

of

prefrontal

recruitment

mainly

involving

lateral

and

medial

PFC

areas

-LRB-

Duncan

and

Owen

-RRB-

This

finding

is

corroborated

by

single

cell

studies

that

have

shown

a

substantial

adaptability

of

function

at

the

level

of

individual

neurons

Many

lateral

PFC

neurons

show

highly

specific

activity

patterns

depending

upon

the

current

task

and

task

conditions

-LRB-

e.g.

Asaad

et

al.

White

and

Wise

Quintana

and

Fuster

Wallis

et

al.

-RRB-

These

results

led

Duncan

-LRB-

-RRB-

to

postulate

the

adaptive

coding

model

According

to

this

model

neurons

in

PFC

adjust

their

function

to

match

the

requirements

of

particular

tasks

that

are

carried

out

Apparently

any

given

cell

can

be

driven

by

many

different

kinds

of

input

both

from

posterior

parts

of

the

brain

and

from

other

PFC

areas

The

collective

responsiveness

pattern

of

all

PFC

cells

thus

reflects

the

particular

task

relevant

conditions

Duncan

also

noted

however

that

the

potential

of

cells

to

process

different

types

of

information

does

not

exclude

regional

specializations

This

would

reconcile

results

indicating

regional

specialization

with

results

from

single

cell

studies

showing

large

scale

integration

and

adaptation

For

example

a

statistical

rather

than

an

absolute

specialization

seems

to

apply

for

left-hemisphere

PFC

recruitment

for

verbal

material

and

right-hemisphere

PFC

recruitment

for

non-verbal

stimuli

-LRB-

see

e.g.

Passingham

et

al.

-RRB-

A

slightly

different

perspective

on

functional

specialization

of

PFC

areas

is

to

assume

that

some

areas

are

relatively

specialized

whereas

others

serve

more

general

integrative

purposes

and

come

into

play

in

almost

any

task

Gruber

and

von

Cramon

-LRB-

-RRB-

for

instance

found

both

modality

specific

and

amodal

areas

in

PFC

in

verbal

and

visuo-spatial

working

memory

tasks

Other

findings

suggest

that

many

cells

in

ventrolateral

areas

of

PFC

are

specifically

sensitive

to

maintaining

representations

of

single

stimuli

-LRB-

e.g.

objects

or

locations

-RRB-

whereas

cells

sensitive

to

maintaining

complex

integrated

stimuli

and

elaborative

rehearsal

are

found

more

often

in

dorsolateral

areas

-LRB-

Prabhakaran

et

al.

Owen

Wagner

et

al.

-RRB-

Wallis

and

Miller

-LRB-

-RRB-

presented

evidence

suggesting

that

orbitofrontal

cortex

which

is

specifically

involved

in

reward

and

affect

processing

passes

on

this

information

to

dorsolateral

areas

of

PFC

where

it

is

combined

with

other

information

to

control

behavior

Similarly

Ridderinkhof

et

al.

-LRB-

-RRB-

concluded

from

a

review

that

detection

of

response

conflicts

and

response

errors

elicits

overlapping

foci

of

activation

in

medial

PFC

areas

and

that

this

activation

serves

as

a

signal

that

engages

regulatory

processes

in

lateral

PFC

areas

Learning

and

working

memory

Our

view

of

working

memory

is

that

its

informational

content

consists

of

the

active

part

of

long-term

memory

representations

that

is

available

at

any

point

in

time

for

controlled

processing

-LRB-

e.g.

Cowan

O'Reilly

et

al.

-RRB-

These

representations

presumably

are

stored

in

the

temporal

parietal

and

occipital

cortex

so

the

functions

of

working

memory

-LRB-

maintenance

selection

and

integration

-RRB-

that

apply

to

these

representations

are

realized

by

an

interaction

between

PFC

and

posterior

cortical

areas

In

this

view

PFC

is

not

itself

involved

in

storing

long-term

memory

information

Its

representational

role

would

be

limited

to

temporarily

maintaining

and

integrating

information

stored

elsewhere

This

view

seems

to

be

endorsed

by

the

fact

that

the

neural

circuits

and

pathways

assumed

to

be

involved

in

the

two

main

learning

processes

-LRB-

Eichenbaum

and

Cohen

-RRB-

and

the

corresponding

procedural

and

declarative

memory

systems

-LRB-

Squire

and

Zola-Morgan

-RRB-

do

not

include

the

PFC

as

a

representational

medium

The

suggestion

that

PFC

implements

WM

functions

but

is

not

itself

involved

in

associative

learning

would

fit

the

requirement

that

it

is

maximally

flexible

in

the

service

of

generating

novel

combinations

of

representations

that

are

stored

somewhere

else

There

are

no

indications

however

that

the

neural

architecture

and

processes

of

the

PFC

differ

fundamentally

from

those

in

posterior

cortex

which

intrinsically

generate

a

learning

capacity

Therefore

some

learning

-LRB-

and

representational

-RRB-

capacity

in

PFC

can

not

be

ruled

out

a

priori

Some

evidence

suggests

that

the

information

used

in

working

memory

tasks

does

not

have

to

be

stored

in

PFC.

First

prefrontal

patients

generally

perform

quite

well

in

tasks

requiring

the

retrieval

of

previously

learned

procedural

and

declarative

knowledge

However

they

are

typically

impaired

in

episodic

memory

tasks

requiring

for

instance

elaboration

of

stimuli

ordered

recall

and

source

monitoring

So

the

problem

with

prefrontal

damage

does

not

seem

an

inability

to

store

information

in

memory

but

an

inability

to

control

encoding

and

retrieval

processes

-LRB-

e.g.

Shimamura

-RRB-

Second

several

recent

studies

have

linked

activity

in

PFC

areas

with

promoting

effective

LTM

formation

-LRB-

e.g.

Blumenfeld

and

Ranganath

Buckner

-RRB-

They

suggest

however

that

the

information

itself

is

not

stored

in

PFC.

Instead

the

interaction

between

PFC

and

posterior

cortex

controls

what

is

selectively

activated

maintained

and

elaborated

and

what

is

subsequently

stored

as

a

novel

representation

via

the

medial

temporal

lobe

and

the

hippocampus

-LRB-

see

e.g.

Ranganath

et

al.

-RRB-

Third

an

increasing

number

of

studies

showed

that

the

activation

patterns

of

many

single

neurons

or

neuron

ensembles

in

PFC

quickly

and

flexibly

adapt

to

represent

completely

arbitrary

rules

-LRB-

Asaad

et

al.

Wallis

et

al.

White

and

Wise

-RRB-

or

to

arbitrary

action

sequences

-LRB-

Averbeck

et

al.

-RRB-

Although

in

all

of

these

studies

a

number

of

learning

trials

precedes

the

development

of

task

specific

activation

patterns

the

speed

and

flexibility

of

the

adaptation

process

seems

more

suggestive

of

an

adaptive

coding

than

of

an

associative

learning

process

Other

authors

have

suggested

that

learning

in

PFC

does

occur

O'Reilly

et

al.

-LRB-

-RRB-

for

example

suggested

that

the

information

that

is

maintained

in

WM

consists

of

activation

patterns

in

PFC

that

are

sustained

by

strong

mutual

excitation

of

the

neurons

involved

To

explain

the

flexibility

in

combining

these

representations

without

losing

their

specificity

they

assumed

that

PFC

representations

had

to

be

relatively

isolated

from

each

other

Therefore

learning

in

PFC

was

supposed

to

be

slow

taking

place

over

many

years

and

eventually

producing

a

rich

palette

of

independent

PFC

representations

that

enable

flexible

problem-solving

skills

Such

representations

might

have

a

hierarchical

structure

with

an

increasing

level

of

abstraction

along

a

posterior-anterior

PFC

axis

probably

with

the

most

posterior

PFC

representations

being

closely

connected

to

detailed

memory

representations

in

posterior

cortex

A

worked

out

version

of

this

model

by

Rougier

et

al.

-LRB-

-RRB-

showed

that

extensive

training

of

their

model

with

various

tasks

resulted

in

the

development

of

abstract

rule-like

PFC

representations

that

supported

flexible

generalization

in

novel

tasks

The

PFC

representations

developed

slowly

but

once

learned

adaptive

behavior

was

mediated

by

a

search

for

a

task

appropriate

pattern

of

activity

-LRB-

cf.

the

adaptive

coding

mechanism

of

Duncan

-RRB-

rather

than

the

need

to

update

connection

weights

Although

the

simple

tasks

simulated

in

this

model

may

also

apply

to

more

realistic

and

complex

rules

the

authors

point

out

that

it

still

leaves

unexplained

how

PFC

representations

can

be

dynamically

recombined

and

can

interact

with

other

systems

-LRB-

such

as

episodic

memory

language

and

affect

-RRB-

It

remains

to

be

seen

whether

the

supposed

PFC

learning

mechanism

is

viable

Over

brief

periods

of

time

learning

probably

does

not

play

a

major

role

but

a

role

of

learning

over

extensive

periods

can

not

be

ruled

out

Evidence

for

three

interdependent

working

memory

functions

in

PFC

Maintenance

Neurophysiological

studies

have

established

persistent

activity

during

delays

-LRB-

i.e.

after

stimulus

offset

-RRB-

as

the

main

candidate

for

a

neural

substrate

of

the

maintenance

function

of

WM.

Single

cells

showing

persistent

firing

during

a

delayed-matching

task

were

first

discovered

in

PFC

and

later

in

other

neocortical

areas

like

inferotemporal

-LRB-

IT

-RRB-

cortex

-LRB-

Fuster

and

Alexander

Goldman-Rakic

Nakamura

and

Kubota

-RRB-

Different

areas

especially

in

posterior

parts

of

PFC

may

be

involved

in

maintaining

modality

specific

information

Smith

and

Jonides

-LRB-

-RRB-

for

instance

suggested

a

relatively

specialized

role

of

left

and

right

PFC

hemispheres

in

maintaining

verbal

and

visual

information

respectively

Moreover

they

proposed

that

maintenance

of

spatial

information

may

involve

more

dorsally

located

regions

than

maintenance

of

object

information

-LRB-

see

also

Baddeley

-RRB-

In

principle

there

are

two

ways

to

maintain

activity

in

neurons

during

delays

One

is

to

assume

that

input

activation

creates

an

activation

pattern

in

a

network

of

cells

that

is

maintained

within

the

network

by

auto-association

-LRB-

Amit

Deco

and

Rolls

-RRB-

and

possibly

augmented

by

a

dopamine

gating

mechanism

-LRB-

O'Reilly

et

al.

-RRB-

The

other

possibility

is

that

the

activation

pattern

oscillates

in

a

recurrent

loop

between

different

networks

of

cells

There

is

much

evidence

that

supports

an

important

role

of

recurrent

loops

in

maintaining

information

during

working

memory

tasks

Strongest

evidence

is

that

during

delays

continued

activity

is

not

only

found

in

PFC

but

also

in

modality

specific

areas

in

IT

cortex

when

coding

for

objects

-LRB-

Fuster

et

al.

Miller

et

al.

Tomita

et

al.

Ungerleider

et

al.

-RRB-

and

in

parietal

cortex

when

coding

for

locations

-LRB-

Curtis

and

d'Esposito

Rowe

et

al.

Sakai

et

al.

-RRB-

Moreover

localized

cooling

of

either

PFC

or

IT

cortex

interferes

with

activity

in

the

other

area

and

causes

behavioral

deficits

in

a

working

memory

task

-LRB-

see

Fuster

-RRB-

From

a

review

of

the

literature

Ranganath

-LRB-

-RRB-

concluded

that

information

in

visual

WM

tasks

is

maintained

through

persistent

activity

in

visual

cortical

areas

-LRB-

e.g.

inferotemporal

and

parietal

areas

-RRB-

that

is

promoted

by

top-down

input

from

PFC.

So

according

to

Ranganath

maintenance

would

be

an

interaction

between

PFC

and

modality

or

object

specific

posterior

areas

An

additional

loop

involving

the

medial

temporal

lobe

-LRB-

i.e.

the

hippocampus

and

related

areas

-RRB-

may

be

required

to

maintain

complex

novel

stimuli

and

to

quickly

create

new

long-term

representations

-LRB-

e.g.

Ranganath

et

al.

-RRB-

Ranganath

-LRB-

-RRB-

is

somewhat

unclear

about

the

nature

of

the

feedback

from

PFC

in

maintenance

As

a

further

specification

we

would

suggest

a

fixed

connection

scheme

-LRB-

probably

shaped

by

long-term

learning

-RRB-

between

posterior

brain

areas

containing

long-term

memory

or

newly

created

representations

of

stimuli

and

adjacent

areas

in

PFC.

These

pathways

convey

to

PFC

what

information

is

being

maintained

This

may

also

explain

the

finding

that

during

maintenance

activation

in

posterior

areas

is

more

vulnerable

to

distraction

than

activation

in

PFC

-LRB-

Miller

et

al.

O'Connor

et

al.

-RRB-

Maintenance

may

resist

such

distraction

because

PFC

activation

can

reinstate

activation

of

corresponding

lower

level

representations

Such

reinstatement

probably

also

underlies

memory

retrieval

and

mental

imagery

It

has

been

shown

for

example

that

in

tasks

requiring

participants

to

imagine

faces

or

buildings

category

specific

regions

in

the

IT

cortex

became

activated

-LRB-

e.g.

Ishai

et

al.

O'Craven

and

Kanwisher

-RRB-

As

was

argued

by

O'Reilly

et

al.

-LRB-

-RRB-

there

are

two

seemingly

incompatible

core

requirements

of

working

memory

The

first

is

to

robustly

maintain

goals

and

task

instructions

over

time

even

when

faced

with

strong

interference

The

second

is

to

rapidly

and

flexibly

update

goals

and

tasks

if

circumstances

change

Their

solution

to

combine

robust

maintenance

with

flexible

updating

has

been

to

assume

a

dopamine

gating

mechanism

Under

tonic

dopamine

levels

a

strong

mutual

excitation

within

a

cell

assembly

would

lead

to

sustained

activity

and

resistance

to

interference

by

irrelevant

input

Phasic

shifts

of

dopamine

levels

induced

by

significant

events

-LRB-

e.g.

success

or

failure

in

a

task

or

novel

stimuli

predicting

reward

-RRB-

would

enhance

the

strength

of

afferent

input

and

cause

updating

of

the

activation

state

which

subsequently

may

lead

to

representing

novel

strategies

rules

goals

or

task

states

Although

a

dopamine-gating

mechanism

is

an

interesting

possibility

we

believe

that

re-entry

of

activation

through

recurrent

neuronal

circuits

is

the

main

mechanism

for

the

maintenance

function

of

WM.

It

is

likely

that

there

are

many

of

such

recurrent

loops

for

different

types

of

information

These

loops

link

the

perceptual

memory

and

motor

representational

areas

in

posterior

cortex

to

PFC.

They

feed

information

into

PFC

and

in

turn

are

activated

by

the

recurrent

activation

from

PFC.

Modulating

activity

in

these

loops

by

other

PFC

sources

would

modulate

the

top-down

recurrency

We

also

suggest

and

that

these

loops

form

a

hierarchy

at

the

lowest

level

maintaining

simple

stimuli

or

features

and

at

higher

levels

maintaining

increasingly

complex

stimulus

relations

and

rules

We

will

show

how

this

idea

can

be

implemented

and

explore

the

feasibility

of

such

an

implementation

We

agree

however

that

especially

for

the

highest

and

most

global

or

integrative

levels

of

maintaining

information

a

dopamine

gating

mechanism

for

auto-associative

maintenance

can

not

be

ruled

out

Attentional

control

Attentional

control

by

executive

functions

in

PFC

requires

top-down

effects

on

local

information

processing

in

posterior

brain

areas

-LRB-

e.g.

Desimone

and

Duncan

Miller

and

Cohen

Shimamura

-RRB-

Evidence

for

such

a

role

comes

for

instance

from

findings

showing

that

an

important

characteristic

of

behavioral

deficits

following

damage

to

the

PFC

is

extreme

distractability

-LRB-

i.e.

an

inability

to

suppress

interfering

information

-RRB-

and

disinhibition

-LRB-

i.e.

an

inability

to

suppress

inappropriate

responses

-RRB-

A

mechanism

for

top-down

attentional

control

is

suggested

by

the

biased

competition

model

-LRB-

Chelazzi

et

al.

Desimone

and

Duncan

Downing

Reynolds

et

al.

Gazzaley

et

al.

-RRB-

According

to

such

a

model

top-down

control

activates

corresponding

representations

that

are

then

in

a

better

position

to

compete

with

irrelevant

information

for

perceptual

awareness

and

control

of

motor

behavior

Such

top-down

attentional

modulation

of

neural

responses

i.e.

relative

enhancement

of

neuronal

responses

to

task-relevant

stimuli

and

relative

suppression

of

neuronal

responses

to

task-irrelevant

stimuli

has

been

shown

throughout

the

visual

system

as

early

as

the

primary

visual

cortex

-LRB-

e.g.

Desimone

and

Duncan

Reynolds

et

al.

-RRB-

and

the

exact

areas

that

are

biased

depend

on

the

task

that

is

performed

-LRB-

e.g.

Kastner

et

al.

-RRB-

Top-down

attentional

bias

not

only

pre-activates

specific

target

representations

but

also

spreads

activation

to

related

representations

Distractors

with

visual

similarities

to

targets

also

attract

attention

-LRB-

are

positively

biased

-RRB-

as

well

as

distractors

with

semantic

or

associative

links

to

the

target

-LRB-

Moores

et

al.

-RRB-

Task

specific

activity

in

PFC

also

generates

top-down

signals

involved

in

the

selection

of

actions

and

in

long-tem

memory

retrieval

-LRB-

Hasegawa

et

al.

-RRB-

and

storage

-LRB-

Brewer

et

al.

Kyd

and

Bilkey

Blumenfeld

and

Ranganath

Wagner

et

al.

-RRB-

This

perspective

thus

suggests

a

single

underlying

mechanism

of

cognitive

control

by

the

PFC

namely

a

top-down

biasing

effect

on

processing

in

specialized

sensory

motor

and

memory

systems

responsible

for

task

performance

-LRB-

see

e.g.

Miller

and

Cohen

-RRB-

We

will

explore

the

feasibility

of

a

mechanism

for

attentional

control

based

on

modulating

feedback

signals

from

PFC

in

the

recurrent

circuits

that

are

used

for

maintenance

We

suggest

that

attentional

control

is

mediated

by

biasing

the

recurrent

loops

between

PFC

and

posterior

cortex

In

this

view

the

biasing

signals

stemming

from

higher

integrative

areas

in

PFC

would

modulate

the

recurrent

feedback

to

posterior

cortical

areas

-LRB-

see

also

Deco

and

Rolls

-RRB-

Integration

The

importance

of

a

large-scale

integrative

system

for

the

coordination

of

behavior

has

been

widely

acknowledged

Such

a

system

would

provide

the

combination

and

integration

of

all

information

necessary

for

controlling

task-relevant

behavior

i.e.

it

would

comprise

all

task-relevant

information

supplied

by

the

present

context

and

goals

and

by

knowledge

retrieved

from

memory

Baddeley

-LRB-

-RRB-

for

instance

suggested

an

episodic

buffer

as

an

additional

subsystem

in

his

WM

model

which

is

capable

of

integrating

and

maintaining

information

from

different

sources

In

a

similar

vein

Miller

and

Cohen

-LRB-

-RRB-

argued

for

a

cognitive

control

system

in

the

PFC

that

represents

goals

and

the

means

to

achieve

them

They

suggest

that

this

system

integrates

converging

inputs

from

many

sources

It

exerts

control

by

divergent

feedback

signals

to

sensory

memory

and

motor

areas

in

posterior

cortex

which

mediate

directed

attention

response

selection

and

guide

retrieval

from

LTM

A

high

level

integrative

control

system

was

also

proposed

by

Koechlin

et

al.

-LRB-

-RRB-

They

suggested

a

cascade

model

consisting

of

three

nested

levels

of

cognitive

control

At

the

top

of

this

cascade

located

in

rostral

-LRB-

anterior

-RRB-

parts

of

the

PFC

they

assume

an

episodic

control

system

involved

in

selecting

task-set

representations

according

to

events

that

previously

occurred

or

to

ongoing

internal

goals

-LRB-

p.

-RRB-

The

view

that

progressively

higher

neural

areas

support

functions

that

are

increasingly

more

integrative

also

has

been

endorsed

also

by

Fuster

-LRB-

-RRB-

He

argued

that

a

cascaded

control

model

assuming

several

nested

levels

of

control

with

a

highly

integrative

system

at

the

top

may

explain

neuropsychological

results

showing

that

damage

to

the

top

system

only

affects

tasks

that

require

a

high

degree

of

information

integration

-LRB-

e.g.

planning

and

problem

solving

-RRB-

but

does

not

interfere

with

tasks

that

can

be

executed

at

a

lower

level

of

control

Potentially

the

highest

level

of

integration

is

attained

in

the

anterior

PFC

-LRB-

aPFC

-RRB-

in

humans

corresponding

to

In

a

recent

discussion

Ramani

and

Owen

-LRB-

-RRB-

pointed

out

this

area

as

a

likely

candidate

for

being

the

apex

of

a

hierarchical

system

of

PFC

modules

because

it

is

predominantly

-LRB-

or

even

exclusively

-RRB-

reciprocally

connected

to

other

supramodal

PFC

areas

Therefore

it

is

in

the

position

to

integrate

everything

the

brain

is

capable

of

representing

Moreover

the

aPFC

has

anatomical

characteristics

-LRB-

a

high

dendritic

spine

density

combined

with

a

low

density

of

cell

bodies

-RRB-

that

make

it

likely

to

be

involved

in

integration

The

aPFC

has

been

suggested

to

be

specialized

for

processing

internal

mental

states

and

introspective

evaluation

-LRB-

Christoff

and

Gabrieli

-RRB-

monitoring

successful

retrieval

-LRB-

Ranganath

and

Paller

-RRB-

management

and

monitoring

of

goals

and

sub-goals

-LRB-

Koechlin

et

al.

Braver

and

Bongliatti

-RRB-

integration

of

information

over

time

-LRB-

Braver

et

al.

Koechlin

et

al.

Sakai

et

al.

-RRB-

and

manipulation

of

relational

knowledge

-LRB-

Kroger

et

al.

-RRB-

A

hierarchical

model

of

integration

based

on

the

convergence

of

input

from

lower

level

specialized

PFC

modules

on

higher

integrative

levels

is

consistent

with

claims

that

a

distinction

can

be

made

between

PFC

areas

involved

in

simple

maintenance

and

others

involved

in

maintenance

of

complex

information

and

executive

processes

-LRB-

D'Esposito

et

al.

Sakai

et

al.

-RRB-

For

example

several

findings

suggest

a

distinction

between

ventrolateral

-LRB-

vlPFC

-RRB-

and

dorsolateral

-LRB-

dlPFC

-RRB-

areas

in

terms

of

the

level

of

abstractness

of

information

processed

-LRB-

e.g.

Koechlin

et

al.

O'Reilly

et

al.

-RRB-

Tasks

requiring

processing

of

single

words

or

concepts

mainly

seem

to

involve

vlPFC

-LRB-

e.g.

Wagner

et

al.

-RRB-

whereas

in

more

complex

tasks

like

sentence

processing

enhanced

involvement

of

dlPFC

has

been

found

-LRB-

e.g.

Hashimoto

and

Sakai

Kerns

et

al.

-RRB-

A

relatively

high

integrative

role

for

the

dlPFC

-LRB-

as

compared

to

vlPFC

-RRB-

is

also

suggested

by

findings

showing

this

area

to

be

involved

in

tasks

that

go

beyond

simple

maintenance

such

as

elaborative

rehearsal

-LRB-

Blumenfeldt

and

Ranganath

-RRB-

processing

relations

between

stimuli

-LRB-

Kroger

et

al.

-RRB-

and

solving

conflicts

that

are

signalled

by

medial

areas

of

PFC

-LRB-

Egner

and

Hirsch

Kerns

et

al.

-RRB-

We

endorse

the

idea

that

the

PFC

is

hierarchically

organized

with

subordinate

modules

being

-LRB-

relatively

-RRB-

specialized

in

processing

simple

aspects

of

tasks

and

super-ordinate

modules

located

in

dlPFC

and

frontopolar

cortex

being

specialized

in

large

scale

integration

-LRB-

i.e.

binding

-RRB-

of

inputs

from

different

sources

At

the

apex

of

the

supposed

hierarchy

the

aPFC

might

integrate

all

goal

related

information

over

space

and

time

-LRB-

see

Fig.

-RRB-

Here

emotional

and

motivational

evaluations

-LRB-

from

orbitofrontal

areas

-RRB-

results

of

memory

retrieval

language

and

rule-like

representations

-LRB-

from

dlPFC

-RRB-

and

anticipated

action

effects

response

conflicts

and

bodily

states

-LRB-

from

medial

PFC

areas

-RRB-

converge

So

the

aPFC

would

be

crucial

for

integrating

multiple

forms

of

information

in

the

pursuit

of

general

goals

-LRB-

e.g.

Ramnani

and

Owen

-RRB-

or

when

information

from

temporally

dispersed

events

has

to

be

integrated

-LRB-

e.g.

Koechlin

et

al.

-RRB-

Conversely

the

ensuing

activation

patterns

in

aPFC

set

the

stage

for

task

and

goal

directed

behavior

with

control

being

exerted

by

top-down

modulation

of

subordinate

modules

In

this

way

a

hierarchy

of

top-down

coordinated

control

is

implemented

by

entraining

successively

lower

order

areas

eventually

biasing

perceptual

processing

memory

search

and

action

selection

Fig.

schematic

view

of

the

hierarchical

structure

of

PFC.

Many

details

regarding

sensory

input

and

motor

output

processing

in

subcortical

centres

and

cerebellum

are

not

shown

Double

arrowheads

indicate

recurrent

connections

Connections

and

modules

shown

are

not

suggested

to

be

anatomically

fully

complete

and

accurate

aPFC

anterior

PFC

dlPFC

dorsolateral

PFC

vlPFC

ventrolateral

PFC

oPFC

orbital

and

ventromedial

PFC

mPFC

medial

PFC

-LRB-

anterior

cingulate

cortex

-RRB-

pm

cortex

premotor

cortex

A

neurocomputational

model

of

maintenance

control

and

integration

Elsewhere

-LRB-

Raffone

and

Wolters

-RRB-

we

have

presented

a

model

for

the

temporary

holding

in

-LRB-

visual

-RRB-

working

memory

of

a

limited

number

of

neural

patterns

simulating

either

single

features

or

integrated

objects

The

model

implemented

a

cortical

mechanism

of

maintenance

in

a

network

of

model

neurons

with

biologically

plausible

parameters

Although

the

model

implemented

a

visual

working

memory

system

the

principles

may

be

applicable

to

any

form

of

information

or

type

of

working

memory

In

the

model

WM

was

assumed

to

be

based

on

recurrent

connections

between

IT

cortex

containing

representations

of

objects

or

features

and

corresponding

neurons

in

PFC.

The

IT

representations

were

modelled

as

strongly

associated

neural

assemblies

that

generate

synchronized

firing

patterns

when

activated

by

external

input

The

simultaneous

activation

of

independent

assemblies

in

IT

causes

competition

via

inhibitory

interneurons

Due

to

the

neuron

characteristics

this

leads

to

desynchronization

among

the

activation

patterns

of

competing

assemblies

resulting

in

a

sustained

phase-locked

activation

of

multiple

assemblies

over

time

Maintenance

in

cortical

circuits

of

visual

working

memory

was

shown

to

be

possible

in

terms

of

oscillatory

reverberations

between

PFC

and

IT

modules

Firing

rate

oscillations

induced

during

stimulus

presentation

were

maintained

after

stimulus

offset

by

active

feedback

from

prefrontal

areas

Neurophysiological

plausible

model

parameters

enforced

a

limitation

of

about

three

to

four

independent

assemblies

that

could

be

maintained

in

this

way

This

number

closely

coincides

with

recent

estimates

of

the

maintenance

capacity

of

WM

-LRB-

e.g.

Cowan

-RRB-

The

same

mechanism

that

optimizes

coherent

pattern

segregation

also

poses

a

limit

to

the

number

of

assemblies

-LRB-

about

four

-RRB-

that

can

concurrently

reverberate

The

model

thus

indicated

that

selective

synchronization

and

desynchronization

of

feedback-based

oscillatory

reverberations

creates

a

suitable

medium

for

a

visual

working

memory

Simulations

showed

that

the

model

was

able

to

explain

both

the

existence

of

severe

limits

in

the

number

of

assemblies

-LRB-

stimuli

-RRB-

that

can

be

held

-LRB-

e.g.

Luck

and

Vogel

Luck

and

Beach

-RRB-

and

the

absence

of

a

limit

on

the

size

of

assemblies

i.e.

representing

either

simple

stimuli

or

complex

chunks

-LRB-

e.g.

Ericsson

and

Delaney

-RRB-

We

introduced

the

concept

of

chunking

fields

to

account

for

the

creation

of

more

complex

neural

assemblies

-LRB-

e.g.

higher

order

information

units

or

chunks

-RRB-

through

previous

Hebbian

learning

-LRB-

e.g.

Hummel

and

Biederman

Singer

-RRB-

The

model

can

potentially

account

for

different

degrees

of

within-object

feature

integration

-LRB-

Olson

and

Jiang

-RRB-

in

terms

of

graded

synchrony

between

neurons

coding

for

features

of

the

same

object

Here

we

will

explore

an

extension

of

the

model

of

Raffone

and

Wolters

-LRB-

-RRB-

simulating

not

only

maintenance

but

also

a

selective

attention

mechanism

and

a

particular

characteristic

of

an

integration

mechanism

The

network

architecture

presented

here

to

model

these

functions

is

composed

of

three

modules

which

we

assume

to

correspond

to

an

IT

module

a

ventrolateral

prefrontal

module

-LRB-

vlPFC

-RRB-

and

a

dorsolateral

prefrontal

module

-LRB-

dlPFC

-RRB-

respectively

-LRB-

see

Fig.

-RRB-

We

assume

that

visual

features

are

coded

by

individual

assemblies

of

neurons

in

IT

which

are

matched

to

one

assembly

in

vlPFC

in

a

recurrent

circuit

Moreover

we

assume

that

different

subsets

of

four

vlPFC

assemblies

coding

for

given

visual

chunks

are

bi-directionally

connected

to

dlPFC

assemblies

-LRB-

one

for

each

set

of

four

vlPFC

assemblies

-RRB-

Fig.

of

the

cortical

network

architecture

In

the

IT

module

neural

assemblies

code

for

hypothetical

visual

features

or

separate

representational

elements

The

figure

shows

the

case

with

five

four-feature

chunks

with

synchronizing

connections

between

the

assemblies

coding

the

features

of

the

same

object

-LRB-

depicted

as

diamond-like

configurations

-RRB-

The

IT

module

also

comprises

an

assembly

of

globally

inhibitory

neurons

which

are

implicitly

modeled

through

inhibitory

postsynaptic

potentials

-LRB-

IPSPs

-RRB-

The

vlPFC

module

is

a

set

of

assemblies

of

neurons

with

a

coding

structure

matching

the

IT

module

structure

For

simplicity

each

IT

assembly

is

recurrently

connected

with

one

vlPFC

assembly

with

a

signaling

delay

of

ms

in

both

directions

The

vlPFC

assemblies

coding

for

a

given

chunk

are

reciprocally

connected

with

a

dlPFC

assembly

which

propagates

synchronous

firing

before

Hebbian

learning

takes

place

in

the

IT

module

In

the

IT

module

strong

connections

are

implemented

within

and

weak

connections

between

assemblies

coding

different

stimuli

We

also

implemented

a

global

inhibition

-LRB-

competition

-RRB-

mechanism

between

IT

assemblies

This

circuitry

in

which

a

given

assembly

is

inhibited

by

the

firing

of

the

other

assemblies

in

the

IT

module

mediates

an

active

desynchronization

mechanism

Stimulus

input

to

the

IT

module

is

given

by

stochastic

spike

trains

from

-LRB-

not

explicitly

modeled

-RRB-

lower

visual

areas

during

a

limited

onset-offset

period

During

stimulus

presentation

all

stimulus

specific

IT

neurons

received

a

high

frequent

train

of

spikes

as

input

which

was

added

to

a

continuous

stochastic

low

frequency

spike

input

to

all

IT

neurons

The

vlPFC

module

has

a

coding

structure

matching

the

structure

of

the

IT

module

-LRB-

see

Fig.

-RRB-

Each

IT

assembly

is

recurrently

connected

with

one

vlPFC

assembly

with

a

signaling

delay

of

ms

in

both

directions

Although

we

implemented

a

monosynaptic

feedback

circuit

in

real

cortical

networks

this

delayed

feedback

is

likely

to

be

mediated

by

multisynaptic

circuitries

of

the

synfire

type

with

a

stable

transmission

along

multiple

diverging/converging

synaptic

links

-LRB-

Abeles

et

al.

b

see

also

Villa

and

Fuster

-RRB-

In

vlPFC

and

dlPFC

there

are

no

inhibitory

neurons

and

no

inter-assembly

connections

Although

stimuli

are

coded

by

large

assemblies

of

neurons

in

real

cortical

networks

in

the

present

model

individual

stimuli

are

coded

by

single

neurons

Our

simulations

will

show

that

the

functional

processes

we

have

investigated

previously

at

the

level

of

assemblies

of

neurons

-LRB-

Raffone

and

Wolters

-RRB-

also

hold

at

the

level

of

single

neurons

thus

pointing

out

the

robustness

of

the

observed

effects

The

use

of

single

neurons

instead

of

assemblies

of

tens

of

neurons

is

also

motivated

by

the

sake

of

running

relatively

fast

simulations

with

a

smaller

time-step

in

numerical

integration

of

neuronal

equations

-LRB-

higher

computational

accuracy

-RRB-

than

in

our

previous

investigations

The

model

of

course

is

very

simplified

with

respect

to

the

complexity

of

the

real

cortical

networks

Feedback

from

the

vlPFC

module

does

no

more

than

maintaining

the

oscillatory

state

of

IT

assemblies

after

the

stimulus

offset

More

realistic

network

versions

might

include

closed

reverberatory

circuits

within

prefrontal

areas

making

prefrontal

assemblies

independent

from

the

IT

assemblies

in

maintaining

the

delay

activity

This

would

allow

modality

shifts

by

activating

other

prefrontal

assemblies

which

in

turn

would

trigger

reverberatory

circuitries

in

lower

cortical

areas

in

the

absence

of

actual

physical

stimulation

Moreover

we

used

a

simplified

one-to-one

matching

between

IT

and

vlPFC

assemblies

whereas

it

seems

more

realistic

to

assume

that

prefrontal

assemblies

are

relatively

non-specific

thus

being

connected

to

multiple

sets

of

neurons

in

posterior

areas

Instead

of

the

one-to-one

matching

of

assemblies

dlPFC

neurons

might

simply

send

back

activation

to

all

vlPFC

neurons

and

these

in

turn

to

all

IT

neurons

from

which

they

receive

activation

However

the

present

simple

architecture

is

sufficient

for

demonstrating

the

functional

principles

we

have

specified

earlier

Simulations

Simulating

maintenance

in

working

memory

We

first

replicated

our

earlier

results

-LRB-

Raffone

and

Wolters

-RRB-

with

stimulus

features

coded

by

individual

neurons

rather

than

by

sets

of

neurons

as

well

as

with

a

higher

temporal

resolution

of

the

simulations

-LRB-

computational

accuracy

-RRB-

and

a

more

realistic

global

inhibition

mechanism

In

this

simulation

the

weights

of

the

synchronizing

inter-neuron

connections

in

IT

were

set

to

zero

so

all

neurons

code

for

different

independent

features

As

shown

in

Fig.

about

four

elements

are

retained

in

the

network

after

offset

of

the

stimuli

which

is

in

accordance

with

actual

capacity

limitations

-LRB-

e.g.

Cowan

Luck

and

Vogel

-RRB-

The

frequency

of

the

reverberatory

oscillations

is

approximately

Hz

Figure

and

b

show

the

retention

of

three

out

of

four

retained

items

Fig.

and

d

the

retention

of

four

out

of

four

and

Fig.

and

f

the

retention

of

four

out

of

eight

items

Note

the

automatic

phase-segregation

due

to

the

mutual

inhibition

desynchronizing

effects

-LRB-

see

Raffone

and

Wolters

for

a

systematic

investigation

of

retention

capacity

with

different

network

parameters

-RRB-

The

missed

retention

of

some

items

-LRB-

Figs.

b

e

f

-RRB-

is

due

to

the

transient

inhibition

propagated

by

the

firing

of

other

competing

neurons

in

IT

This

inhibition

counteracts

the

feedback

input

from

the

matched

vlPFC

neuron

thus

interrupting

the

reverberatory

cycle

after

stimulus

offset

Such

an

interruption

is

more

likely

to

occur

with

multiple

reverberatory

activities

and

stronger

inhibition

as

in

that

case

the

probability

of

the

simultaneous

arrival

of

strong

inhibition

and

feedback

signals

increases

Fig.

retention

capacity

related

to

between-item

segregation

Due

to

mutual

inhibitory

activity

the

neuronal

action

potentials

-LRB-

spikes

-RRB-

become

spaced

in

the

oscillatory

phase

thus

allowing

a

markedly

discriminative

oscillatory

reverberation

and

retention

of

the

coded

items

Often

not

all

reverberations

survived

the

stimulus

offset

The

panels

show

the

fluctuating

membrane

potentials

of

neurons

as

well

as

their

spikes

by

short

vertical

bars

above

the

membrane

potentials

The

panels

a

c

and

e

shows

the

dynamic

behavior

of

IT

neurons

and

the

panels

b

d

and

f

the

responses

of

respectively

matched

vlPFC

neurons

In

-LRB-

a

b

-RRB-

three

out

of

the

four

neurons

remained

active

In

-LRB-

c

d

-RRB-

all

the

reverberations

remained

active

with

all

the

four

items

being

retained

In

-LRB-

e

f

-RRB-

four

out

of

eight

neurons

maintained

their

sustained

oscillation

Note

that

the

oscillatory

reverberations

tend

to

be

optimally

spaced

in

the

phase-lag

depending

on

the

number

of

reverberating

neurons

In

this

simulation

we

did

not

replicate

our

previous

demonstration

-LRB-

Raffone

and

Wolters

-RRB-

that

the

capacity

of

the

reverberatory

circuit

is

independent

of

the

size

of

the

IT

representations

As

we

showed

there

larger

cell

assemblies

of

connected

neurons

with

fast-signaling

positive

weights

quickly

synchronize

and

behave

as

single

units

We

demonstrated

that

the

maintenance

capacity

of

our

model

is

not

a

function

of

the

size

of

a

cell

assembly

but

of

the

presence

and

strength

of

associations

between

the

units

of

an

assembly

This

probably

mimics

the

fact

that

working

memory

capacity

is

not

a

function

of

the

amount

of

information

per

se

but

of

the

level

of

organization

or

chunking

of

the

material

to

be

maintained

-LRB-

Miller

-RRB-

Simulating

attentional

control

in

working

memory

Given

the

severe

limitation

of

the

capacity

of

working

memory

neural

mechanisms

are

necessary

to

restrict

access

to

it

depending

on

the

behavioral

relevance

of

the

information

As

shown

by

Asaad

et

al.

-LRB-

-RRB-

prefrontal

neurons

of

monkeys

performing

a

delayed-matching-to-sample

task

exhibited

a

higher

firing

rate

when

they

coded

for

a

rule-defined

target-object

Such

neurons

were

suggested

to

be

involved

in

both

the

selection

and

maintenance

of

behaviorally

relevant

information

To

model

a

selective

attention

mechanism

by

which

selection

and

retention

is

controlled

by

working

memory

cortical

circuits

a

top-down

input

to

IT

neurons

has

to

be

supplied

by

a

prefrontal

source

involved

in

supervisory

control

rather

than

in

mere

information

maintenance

A

similar

assumption

was

made

in

a

model

for

attentional

control

by

Deco

and

Rolls

-LRB-

-RRB-

Such

a

mechanism

implements

the

top-down

biasing

of

the

competition

between

neural

assemblies

in

posterior

cortex

as

suggested

by

Desimone

and

Duncan

-LRB-

-RRB-

In

our

model

this

supervisory

control

signal

could

be

supplied

through

the

dlPFC

and

vlPFC

modules

We

first

considered

an

additive

-LRB-

voltage-independent

-RRB-

input

to

a

subset

of

four

-LRB-

the

bottom

four

in

Fig.

-RRB-

out

of

eight

independent

IT

neurons

all

receiving

external

input

This

additional

input

was

modeled

in

terms

of

additional

spikes

with

an

excitatory

postsynaptic

potential

-LRB-

EPSP

-RRB-

amplitude

equal

to

the

external

input

signals

and

a

spiking

probability

equal

to

As

can

be

seen

in

Fig.

the

four

items

receiving

top-down

input

exhibited

a

higher

-LRB-

subthreshold

-RRB-

membrane

potential

before

the

stimulus

onset

This

top-down

bias

was

crucial

in

selecting

the

items

to

be

retained

in

terms

of

reverberatory

oscillations

All

four

biased

-LRB-

and

one

unbiased

-RRB-

item

were

maintained

after

stimulus

offset

Fig.

modulation

of

reverberatory

maintenance

Four

out

of

the

five

maintained

activities

are

of

neurons

-LRB-

the

first

four

neurons

from

below

in

the

panels

-RRB-

the

activities

of

which

are

selectively

enhanced

either

by

additive

-LRB-

voltage-independent

-RRB-

modulation

-LRB-

a

-RRB-

or

by

voltage-dependent

-LRB-

multiplicative

-RRB-

signals

-LRB-

b

-RRB-

Note

that

with

additive

modulation

the

membrane

potentials

of

the

four

focused

neurons

are

higher

before

the

stimulus

onset

-LRB-

a

-RRB-

whereas

the

amplification

mostly

takes

place

after

stimulus

onset

in

the

voltage-dependent

modulation

case

-LRB-

b

-RRB-

We

also

implemented

a

voltage-dependent

modulatory

input

to

IT

neurons

A

voltage-dependent

gain

effect

simulates

the

possible

role

of

NMDA

receptors

-LRB-

i.e.

receptors

that

activate

a

neuron

only

if

a

signal

arrives

at

an

already

depolarized

synapse

-RRB-

NMDA-based

gain

effects

are

suggested

to

play

a

crucial

role

in

cognitive

coordination

and

control

-LRB-

Phillips

and

Silverstein

see

also

Raffone

et

al.

-RRB-

and

such

gain

effects

are

often

modelled

as

a

multiplication

of

input

signals

This

multiplicative

effect

was

modeled

in

terms

of

the

product

of

the

voltage-independent

external

input

-LRB-

VI

-RRB-

and

voltage-dependent

top-down

signals

-LRB-

VD

-RRB-

with

amplitude

equal

to

and

spiking

probability

equal

to

according

to

the

following

equations

Following

suggestions

of

Tononi

et

al.

-LRB-

-RRB-

the

product

of

equation

-LRB-

-RRB-

was

set

equal

to

when

a

membrane

potential

-LRB-

ui

-RRB-

was

less

than

a

voltage

threshold

VT

-LRB-

set

equal

to

-RRB-

In

that

case

only

a

voltage-independent

stimulus-related

input

determines

the

net

input

to

the

neuron

without

any

amplification

As

can

be

inferred

from

Fig.

the

attentional

bias

-LRB-

attentional

input

for

four

out

of

eight

items

-RRB-

in

the

voltage

dependent

condition

is

expressed

after

the

stimulus-input

onset

in

terms

of

an

amplification

effect

Also

in

this

simulation

all

four

biased

-LRB-

and

one

unbiased

-RRB-

items

were

maintained

after

stimulus

offset

We

believe

that

voltage-dependent

input

to

high-level

areas

of

the

visual

system

may

enable

an

effective

top-down

control

mechanism

since

it

produces

strong

amplification

effects

but

only

in

the

presence

of

relevant

stimulus-input

-LRB-

see

also

Hirsch

and

Gilbert

Tononi

et

al.

about

voltage-dependent

signaling

in

the

visual

system

-RRB-

This

multiplicative

signaling

effect

would

prevent

spurious

activation

of

representations

in

the

visual

system

in

the

absence

of

any

bottom-up

sensory

evidence

A

series

of

follow-up

simulations

showed

that

the

probability

of

item

retention

depends

on

the

relative

biasing

top-down

input

for

a

given

item

A

strong

attentional

input

to

one

or

two

items

may

narrow

visual

working

memory

capacity

to

one

or

two

highly

focused

elements

due

to

the

higher

firing

rate

-LRB-

e.g.

bursting

-RRB-

of

the

neurons

coding

for

the

focused

items

Mechanisms

for

integration

in

working

memory

Any

domain-specific

account

of

functions

or

representations

in

the

brain

ultimately

implies

a

binding

problem

since

the

specialized

neural

representations

need

to

be

dynamically

bound

to

enable

the

creation

-LRB-

and

a

conscious

readout

-RRB-

of

coherent

and

integrated

representations

of

complex

events

These

binding

processes

may

occur

in

terms

of

the

selective

synchronization

of

reverberating

cell

assemblies

as

shown

in

our

simulations

In

this

framework

segregated

neural

representations

may

be

bound

via

reciprocal

synchronizing

connections

that

originate

within

the

prefrontal

cortex

-LRB-

for

a

discussion

of

this

and

other

forms

of

binding

see

Murre

et

al.

see

also

Engel

et

al.

Varela

et

al.

-RRB-

In

our

model

dlPFC

can

play

a

crucial

role

in

what

we

call

associative

control

i.e.

in

selecting

meaningful

or

currently

salient

conjunctions

of

otherwise

separated

representational

elements

held

in

a

temporarily

active

state

within

vlPFC-IT

circuits

We

have

shown

that

reciprocal

fast

signaling

between

a

dlPF

neuron

and

its

corresponding

vlPFC

neurons

may

entrain

all

these

vlPFC

neurons

coding

for

different

features

or

units

to

fire

in

a

nearly

synchronous

manner

This

synchronous

firing

is

propagated

to

IT

neurons

which

in

turn

fire

almost

coincidently

before

the

action

of

the

mutual

inhibitory

signals

within

the

IT

module

-LRB-

Fig.

b

-RRB-

We

suggest

that

such

a

top-down

controlled

selective

synchronization

of

independent

representations

is

a

likely

candidate

of

the

process

underlying

integration

in

PFC.

This

mechanism

ensures

the

simultaneous

and

coherent

activation

of

independent

representations

that

may

be

widely

dispersed

over

various

brain

areas

It

may

cause

for

instance

the

large-scale

task-dependent

synchronization

in

the

gamma

band

as

observed

by

Rodriguez

et

al.

-LRB-

-RRB-

Fig.

control

on

vlPFC

neuronal

activities

by

dlPFC

neurons

The

panels

a

c

and

e

shows

the

dynamic

behavior

of

IT

neurons

and

the

panels

b

d

and

f

the

responses

of

respectively

matched

vlPFC

neurons

As

shown

in

panels

a

b

inter-neuron

synchronization

of

the

four

vlPFC

neurons

is

induced

by

a

dlPFC

neuron

-LRB-

activity

not

shown

-RRB-

and

is

then

propagated

to

the

matched

IT

neurons

As

shown

in

panels

c

d

the

joint

effect

of

dlPFC

feedback

and

Hebbian

associative

signals

in

IT

may

induce

a

higher

firing

rate

of

neurons

in

IT

and

vlPFC

Panels

e

f

show

that

a

stable

synchronization

of

IT

and

vlPFC

neurons

is

observed

after

Hebbian

strengthening

of

IT

chunking

synapses

with

feedback

from

the

dlPFC

neuron

being

switched-off

The

same

mechanism

may

allow

Hebbian

learning

to

occur

between

meaningful

or

behaviorally-salient

conjunctions

of

independent

elements

-LRB-

see

Miltner

et

al.

-RRB-

In

order

to

account

for

the

retention

of

integrated

units

in

visual

working

memory

in

previous

simulations

we

defined

units

by

assuming

already

established

chunking

fields

i.e.

pre-existing

associative

connections

between

IT

neurons

coding

different

features

of

the

same

unit

A

major

problem

however

is

how

chunking

fields

or

integrated

representations

in

long-term

memory

are

formed

i.e.

how

associative

Hebbian

learning

may

take

place

in

the

simulated

cortical

circuits

of

-LRB-

visual

-RRB-

working

memory

This

learning

process

should

operate

against

the

phase-segregation

tendency

due

to

mutual

desynchronizing

inhibition

In

the

present

model

the

dlPFC

module

may

perform

this

temporary

synchronization

control

processes

In

a

series

of

simulations

we

used

the

entrainment

of

vlPFC

and

IT

neurons

by

a

top-down

dlPFC

signal

and

combined

it

with

a

timing-dependent

Hebbian

learning

rule

in

IT

-LRB-

simulating

timing-dependent

plasticity

of

synapses

see

e.g.

Körding

and

König

Song

et

al.

see

Fig.

d

-RRB-

These

simulations

showed

that

the

synchronous

firing

in

IT

induced

by

a

top-down

recurrent

signal

causes

the

weights

between

the

IT

neurons

to

increase

resulting

in

a

chunked

representation

of

initially

independent

features

As

synaptic

weights

increased

according

to

the

timing-dependent

learning

rule

the

synchronization

among

related

IT

neurons

becomes

gradually

less

dependent

on

the

feedback

action

of

dlPFC

neurons

on

related

vlPFC

neurons

ultimately

leading

to

an

automatic

within-chunk

integration

within

the

IT

module

in

the

absence

of

any

control

feedback

from

the

dlPFC

module

-LRB-

Fig.

f

-RRB-

Control

simulations

with

the

same

model

and

learning

parameters

showed

that

a

relatively

high

number

of

simultaneous

presentations

of

initially

independent

elements

may

slowly

give

rise

to

novel

chunks

with

a

variable

degree

of

stability

even

in

the

absence

of

feedback

from

dlPFC

to

vlPFC

This

slow

chunk-learning

process

may

be

related

to

an

implicit

learning

process

rather

than

the

more

rapid

process

of

controlled

chunk

formation

by

feedback

from

dlPFC

Discussion

Miller

and

Cohen

-LRB-

-RRB-

concluded

that

PFC

is

critical

when

top-down

control

of

processing

is

needed

for

the

guidance

of

behavior

by

internal

states

or

intentions

In

this

paper

we

conjecture

that

such

control

is

possible

by

assuming

that

the

PFC

is

a

brain

system

that

is

reciprocally

connected

to

posterior

parts

of

the

brain

and

therefore

capable

of

modulating

direct

perception-action

relations

We

argued

that

three

interdependent

functions

are

required

for

such

control

maintenance

of

activation

patterns

even

in

the

face

of

distraction

large

scale

integration

of

information

from

different

sources

and

top-down

selective

attention

We

also

showed

that

these

functions

can

be

simulated

at

least

in

principle

with

a

biologically

plausible

model

assuming

recurrent

connections

and

a

hierarchical

structure

of

PFC.

Importantly

the

functions

were

simulated

using

a

single

framework

consisting

of

a

hierarchy

of

recurrently

connected

modules

This

framework

proved

capable

of

maintaining

a

limited

number

of

mutually

desynchronized

patterns

of

controlling

selective

attention

by

top-down

modulatory

signals

and

of

high

level

integration

by

top-down

synchronization

of

the

activity

of

independent

patterns

Of

course

the

model

suggested

here

is

extremely

simplified

and

it

does

not

capture

many

of

the

complexities

and

intricate

details

of

the

real

system

For

instance

we

did

not

try

to

incorporate

modulatory

effects

of

neurotransmitters

and

we

have

not

discussed

the

many

possible

interactions

of

the

PFC

with

subcortical

systems

like

the

basal

ganglia

and

amygdala

and

the

cerebellum

It

may

also

be

noted

that

we

discussed

but

did

not

try

to

incorporate

the

possibly

important

role

of

a

dopamine

gating

system

for

maintaining

high

level

integrative

activation

states

-LRB-

O'Reilly

et

al.

-RRB-

One

of

our

aims

to

develop

the

model

was

to

try

to

find

out

how

the

structural

and

functional

characteristics

of

the

PFC

may

explain

the

central

executive

component

of

working

memory

assumed

by

Baddeley

-LRB-

-RRB-

In

the

latest

version

of

this

model

an

additional

component

an

episodic

buffer

was

proposed

This

component

was

thought

necessary

to

explain

interactions

between

working

memory

and

LTM

for

example

to

explain

how

chunking

may

supplement

the

capacity

for

immediate

serial

recall

A

continuous

interaction

between

working

memory

and

LTM

is

a

basic

assumption

in

our

model

and

we

have

shown

that

chunking

is

a

natural

consequence

of

this

interaction

According

to

Baddeley

-LRB-

-RRB-

the

episodic

buffer

allows

information

from

different

systems

to

be

integrated

and

it

may

be

regarded

as

the

storage

component

of

the

executive

Moreover

Baddeley

suggested

that

the

buffer

is

a

separate

temporary

store

in

which

long-term

memory

information

is

downloaded

in

order

to

be

manipulated

and

used

for

creating

new

representations

In

our

view

the

structure

of

such

an

episodic

buffer

would

actually

be

a

hierarchy

of

PFC

modules

maintaining

and

integrating

information

at

successively

higher

levels

Information

is

not

copied

and

downloaded

in

PFC

but

selected

task-relevant

perceptual

and

memory

information

in

posterior

cortical

areas

is

kept

in

an

active

state

by

recurrent

loops

We

assume

that

at

the

highest

integration

levels

presumably

located

in

anterior

and

dorsolateral

areas

of

PFC

information

can

be

integrated

over

space

and

time

At

this

level

general

goals

and

plans

to

achieve

them

would

be

generated

We

suggest

that

this

may

be

the

implementation

of

an

episodic

buffer

and

even

of

a

central

executive

The

creation

of

short-term

and

long-term

goals

at

high

levels

of

integration

allows

to

control

behavior

by

top-down

regulation

of

activation

in

subordinate

modules

and

eventually

by

modulating

perception-action

systems

Current

goals

and

tasks

would

have

to

be

maintained

here

in

order

to

control

ongoing

behavior

More

distant

goals

however

may

be

stored

in

LTM

to

be

retrieved

later

on

We

do

not

imply

that

control

is

always

hierarchical

Because

the

PFC

is

assumed

to

be

a

modulatory

system

simple

tasks

may

be

executed

automatically

without

PFC

involvement

Only

controlled

processing

requires

modulation

of

automatic

processes

by

PFC

feedback

Also

here

however

control

does

not

need

to

be

completely

hierarchical

Depending

on

the

type

and

complexity

of

a

task

control

may

be

executed

by

specialized

modules

at

subordinate

levels

Automatic

processing

and

controlled

processing

under

the

guidance

of

subordinate

levels

in

PFC

is

a

requirement

to

free

higher

integrative

areas

to

engage

in

the

kind

of

simulated

actions

and

perceptions

and

using

anticipated

outcomes

that

are

the

contents

of

conscious

thought

and

planning

So

there

are

things

we

can

do

automatically

using

previously

established

associations

between

perceptual

events

and

effective

responses

and

there

are

things

we

can

do

in

a

controlled

way

using

the

possibility

of

PFC

to

modulate

and

thus

control

perception

and

action

The

possibility

of

PFC

to

work

off-line

to

use

and

integrate

all

present

and

past

knowledge

for

creating

virtual

worlds

and

for

making

plans

and

carry

them

out

if

conditions

are

appropriate

has

generated

a

tremendously

flexible

potential

for

control

The

model

we

propose

is

vastly

insufficient

to

simulate

such

cognitive

feats

that

we

engage

in

daily

We

believe

however

that

the

principles

for

a

controlling

system

we

have

suggested

may

be

a

first

step

towards

a

better

understanding

of

what

is

until

now

a

cognitive

Terra

Incognita

working memory
maintenance
control of attention
integration
prefrontal cortex
synchronization