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Cortex and Memory: Emergence of a New Paradigm
Joaquín M. Fuster
Abstract
■Converging evidence from humans and nonhuman pri-
mates is obliging us to abandon conventional models in favor
of a radically different, distributed-network paradigm of corti-
cal memory. Central to the new paradigm is the concept of
memory network or cognit—that is, a memory or an item of
knowledge defined by a pattern of connections between neu-
ron populations associated by experience. Cognits are hi-
erarchically organized in terms of semantic abstraction and
complexity. Complex cognits link neurons in noncontiguous
cortical areas of prefrontal and posterior association cortex.
Cognits overlap and interconnect profusely, even across hier-
archical levels (heterarchically), whereby a neuron can be part
of many memory networks and thus many memories or items
of knowledge. ■
INTRODUCTION
The history of cognitive neuroscience began in the 19th
century with the controversy between phrenologists and
experimentalists about the cerebral localization of func-
tions and with Brocaʼs (1861) publication of the disorder
of language from frontal injury that bears his name
(Young, 1990). Since that time, the field has been divided
into two camps or schools of thought. In one are those
who advocate that each complex cognitive function is lo-
calized in a separate part of the cerebral cortex, as Broca
advocated with respect to articulated speech. In the
other are those who maintain that complex cognitive
functions are widely distributed in the cortex, as is the
information they use. Until now, however, this second
position has remained in the shadows for lack of empiri-
cal support, whereas modular views of cognition have
thrived, largely inspired by the successes of reductionism
in most all other fields of neuroscience. The cognitive
neuroscience of memory has evolved on both sides of
that conceptual divide.
Two lines of evidence traditionally support the localiza-
tion of memory in the cortex, that is, the allocation of a
given cortical area or an anatomical module to a given
memory content: (1) discrete cortical lesions cause dis-
crete memory deficits, and (2) electrical stimulation at
certain locations, especially in cortex of association, can
elicit vivid memories. Further, modular views of memory
have been inferred from cortical sensory physiology. Sen-
sory qualities are represented in discrete module-like
areas of sensory cortex. From this evidence derives the
unproven assumption that, beyond those sensory areas,
perceptual memory is represented in modules of associa-
tion cortex. At most, however, those lines of evidence or
extrapolation indicate that some cortical areas are more
related to one kind of memory than to others.
Nonetheless, modular concepts are ubiquitous in cog-
nitive neuroscience. A functional module, as generally
understood, is a continuous and circumscribed portion
of cortex dedicated to one particular function and not
others. In cortical physiology, certain anatomical config-
urations of neural elements (e.g., microscopic columns)
have been identified as functional modules inasmuch as
they contain geometrical arrangements of neurons spe-
cialized in a particular sensory or motor function. Argu-
ably, even beyond primary sensory and motor cortices,
certain circumscribed areas are functionally modular, in
that they specialize in discrete physiological functions
such as the detection of visual movement (area MT) or
ocular motility (FEFs). A serious problem arises, how-
ever, when a cognitive function such as perception, mem-
ory, attention, language, or intelligence is ascribed to a
discrete module of cortex as defined above. Modular mod-
els are all based on that definition of a module, which at
least with regard to memory is theoretically and empiri-
cally inconsistent with the recent literature.
Network memory models, on the other hand, are not
incompatible with the presence of physiological modules
at the interface of the associative—cognitive—cortex with
the environment. In fact, the present model assumes sen-
sory and motor modules at the foundation of memory
networks. However, with those modules at the base,
the architecture of the present network model takes
the form of a massive scaffolding of hierarchically orga-
nized memory networks in a continuum of increasing
network size, from the primary cortex to the highest lev-
els of association cortex.
That the cortex in its totality is a network is a truism.
What is far from a truism is the parceling of that gigantic
network into the multiplicity of overlapping, interactive,
University of California, Los Angeles
© 2009 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 21:11, pp. 2047–2072
and specialized memory networks that emerges from the
recent studies compiled in this review. The emerging
model postulates that the neocortex harbors an immense
array of distinct neuronal networks dedicated to the
representation and retrieval of individual memory and
knowledge. Still largely unknown, however, are the struc-
ture and the dynamic properties of those networks, in-
cluding their mechanisms, their resilience in the face of
neural damage, their deterioration in disease and old age,
and their potential for rehabilitation.
As noted, the present network model is built upon a
modular model. Sensory modules, conceivably with sim-
ple netlike structure, represent simple sensory stimuli as-
sociated in evolution with others with similar features
(see later, phyletic memory). Complex sensory stimuli
of the same or different modality associate those simple
sensory networks into larger networks of association cor-
tex. Those, in turn, form even larger networks to rep-
resent yet more complex perceptual information. Thus,
memory networks of increasing amplitude come to rep-
resent progressively more complex perceptual memories
in progressively higher levels of posterior cortex. In sum,
a hierarchy of increasingly wider networks develops there
to represent a hierarchy of progressively higher and more
complex memories and knowledge, from sensory qualia
at the bottom to semantic and conceptual memories at
the top. Arguably, a comparable hierarchy develops in
frontal cortex to represent motor, “procedural,”and ex-
ecutive memories and knowledge. However, at some
stage in the hierarchies from sensory and motor cortices
into association cortex, the present network model de-
parts radically from other modular or network models
of memory in four fundamental ways:
1. In the present model, a memory or an item of knowl-
edge consists of a widespread cortical network of con-
nections, formed by experience, that joins dispersed cell
populations. These cell populations represent the asso-
ciated percepts and actions that, together, constitute that
memory or cognitive item. Thus, the memory code is fun-
damentally a relational code, sparse and distributed,
etched in cortical space by connections between distrib-
uted neurons—unlike the information in a theoretical “mod-
ule of memory.”
2. A complex memory network, such as an autobio-
graphical memory, is largely interregional, linking neuron
assemblies and smaller networks in separate and non-
contiguous areas of the cortex; in turn, those assemblies
or networks represent other, more concrete aspects of
memory or knowledge.
3. As a result of the practically infinite combinational
power of billions of cortical neurons, memory networks
differ widely in content, complexity, source, temporal ori-
gin, and level of abstraction—from concrete sensation or
action to semantic or conceptual knowledge and plans of
action. Accordingly, the individuality of memory derives
from that combinational power.
4. Memory networks overlap and interlink profusely
with one another by common nodes (i.e., smaller net-
works), whereby a cortical neuron or neuronal assembly,
practically anywhere in the cortex, can be part of multi-
ple networks, thus of multiple memories or items of
knowledge.
These architectural features distinguish this network
model from the more conventional modular and network
models of cortical memory, making the transition from
those models to the present one a shift of scientific para-
digm à la Kuhn (1996). The principal purpose of this re-
view is to critically examine and substantiate those four
tenets.
Lashley (1950), after unsuccessfully attempting to in-
duce memory deficits by discrete cortical lesions, inferred
widely distributed memory, almost by default. At about
the same time, others (Hayek, 1952; Hebb, 1949) began
to postulate cortical network models of perception, learn-
ing, and memory. Several neuroscientists subsequently
incorporated variants of the network idea in their theo-
retical constructs of cortical cognition (McIntosh, 2000;
Mesulam, 1998; Bressler, 1995; Goldman-Rakic, 1988;
Edelman & Mountcastle, 1978; Luria, 1966). Further theo-
retical support for that idea came from the fields of neu-
rocomputation and artificial intelligence (McClelland &
Rumelhart, 1986; Hinton & Anderson, 1981), especially
connectionism (Marcus, 1998; Fodor & Pylyshyn, 1988;
Myers, 1967).
Not until recently, however, has a flood of empirical
evidence given to the network memory paradigm here
presented its innovative and distinctive character. The
evidence comes from three confluent methodologies:
microelectrode recording in the behaving primate, com-
putational analysis of electrocortical potentials, and func-
tional imaging in the human. The three methodologies
provide insight into the structure and dynamics of mem-
ory networks. Elsewhere (e.g., Fuster, 2003), I have used
the term cognit to characterize a memory network be-
cause such a network can represent semantic knowledge
as well as autobiographical memory, with comparable
network structure and the same essential features noted
above. In this review, the two terms, cognit and memory
network, are used interchangeably.
STRUCTURE OF A MEMORY
NETWORK (COGNIT)
Any reasonable model of cortical memory must accom-
modate two interrelated phenomenological facts: the
heterogeneity and the integrative character of memory.
Theoretically, any memory network is heterogeneous be-
cause it includes or can include semantic facts as well as
events, categories as well as sensory qualia, percepts as
well as actions, and biological incentives as well as value
principles. Thus, taxonomies of memory by content are
2048 Journal of Cognitive Neuroscience Volume 21, Number 11
not very helpful to the cognitive neuroscience of cortical
memory. More helpful is the evidence that the categories
of perceptual memory predominate in posterior cortices,
whereas those of executive memory predominate in fron-
tal cortices. This review, however, points to the distrib-
uted and intermixed character of most all cortical memory.
In the light of that evidence, the specific localization of
complex knowledge, such as autobiographical memory,
to single neurons or neuron assemblies is theoretically
and empirically implausible. That does not deny the pres-
ence of certain cortical foci of heavy association (network
nodes) that by lesion or stimulation are implicated more
in one type of memory than in others.
The other obligatory attribute of memory, which the
new paradigm accommodates, is its integrative character.
In static as well as dynamic terms, all memory is essen-
tially associative, in its formation, in its structure, and in
its activation. Associative integration is essential to acqui-
sition, storage, and recall, especially if the memory or
item of knowledge is complex. Take away integration,
and the memory degrades or literally disintegrates. The
same is true for knowledge and semantic memory; in
their case, however, the disintegration is less likely be-
cause they are anchored in multiple and to some degree
redundant cortical associations (i.e., connections).
Integration is missing in most modular models of mem-
ory or perception, even in those that are based on a hier-
archical architecture similar to that of the network model
discussed here. Two constraints make their architecture
orthogonal to that of this model: absence of associative
connections at low levels (e.g., cross-modal, sensorimotor)
and absence of heterarchy (see below) in the associated
networks and assemblies. Further, many modular models
are based on the assumed hierarchical organization of the
visual system, which they expand to visual perception.
That assumption almost inevitably leads to the theoretical
absurdity of the “grandmother cell.”The network para-
digm proposes almost the opposite. Although allowing
for some convergence of connections into certain rela-
tively “specialized”regions (e.g., for faces, words), this
paradigm emphasizes the divergence of connection to-
ward the top, toward ever wider networks that represent
ever more categorical and abstract information; thus, inte-
gration takes place everywhere, but especially at the top,
among widely dispersed elements.
In the present network paradigm, a cognit is defined
by associations and connectivity and is thus essentially
an integrative entity. Two currents of cognitive science
are closely related to it. One is connectionism, the other
Gestalt psychology. Although the first was originally a
doctrine that based all behavior on the bonds between
stimuli and responses, it later developed into a cogni-
tive theory of the relationships between inputs and out-
puts in the processing of neurocognition (Marcus, 1998;
Fodor & Pylyshyn, 1988; Myers, 1967). It has been ap-
plied to modular formulations of language and other com-
plex cognition.
Our network paradigm is even more germane to Gestalt
psychology. The latter began as a theory of visual percep-
tion (Koffka, 1935), according to which a form or gestalt
is defined by the mutual relationships between its parts
and irreducible to them. That theory postulates a set of
rules about the nature of those relationships (cohesion,
continuity, similarity, etc.). Eventually, Gestalt psychology
transcended visual perception and the trite dictum that
the whole is more than the sum of the parts. Nonetheless,
it has practically disappeared from current discourse in
cognitive science. One significant legacy of Gestalt, how-
ever, is the tenet that perception is based on a relational
code. Perception, Hayek (1952) adduced, is the classifica-
tion of the world by an isomorphic set of connective rela-
tionships previously established in the cortex by temporal
coincidence of external stimuli, hence the inextricable re-
lationship between perception and memory (as first stated
by Helmholtz, 1925), which led Hayek to postulate over-
lapping cortical “maps”of perceptual experience, much
like the memory networks or cognits postulated here.
Figure 1 illustrates in highly schematic and three-
dimensional manner the chief principles of network archi-
tecture in one cortical hemisphere. Cognitive networks
or cognits are represented in the figure by circles of
different size depending on network size and hierarchi-
cal level. At lower levels (sensory and motor cortices,
“deep”in the figure), cognits are small, their networks
within the limits of traditional sensory and motor “mod-
ules.”At higher levels, in cortex of association, cognitive
networks are larger and represent broader categories of
memory (polymodal, semantic, episodic, etc., perceptual
or executive), which are distributed in the cortex within
broad areas or across regions. Cognits of almost any size
and level can constitute the nested nodes of more widely
distributed networks. Some such networks are large cog-
nits that represent complex memories or items of knowl-
edge of different hierarchical level; in other words, they
are heterarchical cognits. Two such networks are shown
activated in Figure 1B and C.
The present network model differs distinctly from other
models of associative nets (e.g., Hinton & Anderson,
1981). Its two principal distinguishing characteristics are
as follows:
1. Hierarchy of memory networks and contents. Al-
though the present module is layered like most other
network models, it accommodates a hierarchy of net-
works and contents whereas other models do not.
2. Nets with common nodes. The present model postulates
that memory networks share common associative nodes—
smaller cognits, with network structure themselves—that
represent contents common to several nets.
This feature endows the neurons within those nodes with
flexible “allegiance”to multiple networks and cognits. The
present model, however, shares one important feature
with other network models: feedback connectivity. This
Fuster 2049
feature is critical to the dynamic interplay of cognits in the
perception–action cycle.
MEMORY CELLS
Some of the most valid inferences about the structure of
a cognit can be drawn from its dynamics in behavior. It
is by studying a memory network in the active state that
we can glean its structure. Memory networks are acti-
vated in a variety of conditions. First and foremost, some
are activated, consciously or unconsciously, voluntarily or
involuntarily, in every act of perception. They are also ac-
tivated, more or less voluntarily, in free recall, recogni-
tion, new memory acquisition, rehearsal, and working
memory. The last, I argue, is the most suitable condition
for the study of active memory networks.
Working memory is the ability to temporarily retain in-
formation for a prospective action. The reasons for its
suitability to the study of memory structure and dynamics
are because (1) it is relatively easy to instruct a human or
an animal to retain, for a defined period, a specific item
of information that calls for a specific prospective action;
(2) it is reasonable to assume that, during that time, the
neural substrate for mnemonic retention is in a different
state than at rest; (3) the physiological measures of dif-
fering state probably reflect the physical nature of the
information in temporary storage, in such a manner that
changes in the parameters of that information will be
reflected by commeasurable changes in network dynam-
ics; and (4) the extent of those changes in the cortex and
their correlations with memory performance will help us
determine the boundaries and the dynamics of a cognit.
For nearly 40 years, it has been known that in the mon-
key performing working-memory tasks, such as delayed re-
sponse and delayed matching, neurons in certain cortical
regions undergo persistent elevations of firing frequency
during working memory. Because they were assumed to
intervene in a memory process, such neurons were named
“memory cells.”Ordinarily, these cells show a transient
frequency change in response to the sensory cue that
the animal must retain in memory. After the cue has dis-
appeared, that change is followed by above-baseline dis-
charge for much or all of the ensuing delay or memory
period—seconds or minutes—before the motor response
or choice.
Memory cells were first encountered in the pFC of mon-
keys performing delayed response (Fuster & Alexander,
1971). Subsequent investigations of that cortex revealed
memory cells whose level of memory activity was depen-
dent on the physical characteristics of the stimulus cue to
be remembered (memorandum). Some prefrontal cells re-
spond preferentially to spatial memoranda (Genovesio,
Brasted, & Wise, 2006; Constantinidis, Franowicz, & Goldman-
Rakic,2001; Funahashi,Bruce, & Goldman-Rakic, 1989; Niki,
1974), others to visual memoranda (Wilson, Scalaidhe, &
Goldman-Rakic, 1993; Fuster, Bauer, & J ervey, 1982), includ-
ing faces (Scalaidhe, Wilson, & Goldman-Rakic, 1999), and
still others to auditory (Romanski, 2007; Fuster, Bodner,
& Kroger, 2000) or tactile (Romo, Brody, Hernández, &
Lemus, 1999) memoranda. Furthermore, in some studies,
the relation between prefrontal memory firing and phys-
ical stimulus dimension has been found parametric for
such properties as the location of gaze (Funahashi et al.,
1989) or the frequency of mechanical vibration sensed
by touch (Romo et al., 1999). In any given prefrontal area,
however, the memory cells that show sharp tuning for
any given sensory memorandum constitute a minority.
Cells preferring memoranda of one sensory modality
or another tend to concentrate in certain domains of
pFC, but these domains are poorly demarcated; cells with
any given preference can be found practically anywhere
in pFC. For example, cells preferring spatial memoranda
predominate in dorsolateral areas but are also present
in ventrolateral areas, although in lower numbers, and
vice versa for nonspatial memoranda. Likewise, cells pre-
ferring and anticipating a reward concentrate in an or-
bital domain but are also present in lateral cortex. Some
cells are attuned to stimuli of more than one sensory
modality—for example, auditory and visual—that have be-
come associated with one another by the learning of a
working-memory task (Artchakov et al., 2007; Romanski,
2007; Fuster et al., 2000; Watanabe, 1992; Vaadia, Benson,
Hienz, & Goldstein, 1986). Others are attuned to both the
spatial and the nonspatial attributes of the memorandum
(Fukushima, Hasegawa, & Miyashita, 2004; Rao, Rainer, &
Miller, 1997; Fuster et al., 1982).
Furthermore, in any prefrontal region, some cells are
also attuned to the motor requirements of the working-
memory task (Isomura, Ito, Akazawa, Nambu, & Takada,
2003; Akkal, Bioulac, Audin, & Burbaud, 2002; Procyk
& Joseph, 2001; Quintana & Fuster, 1999; Carlson, Rämä,
Tanila, Linnankoski, & Mansikka, 1997; Fuster et al., 1982)
and/or the reward expected or resulting from motor ac-
tion (Ichihara-Takeda & Funahashi, 2008; Hikosaka &
Watanabe, 2000; Schultz, Tremblay, & Hollerman, 2000;
Watanabe, 1996; Rosenkilde, Bauer, & Fuster, 1981). The
prefrontal domains with relatively high concentration of
cells attuned to different modalities, or task attributes
are anatomically connected with specialized posterior
cortical areas or subcortical structures (review in Fuster,
2008). Visual and auditory domains of ventral pFC are con-
nected with corresponding visual and auditory areas of
temporal cortex and reward domains of orbital cortex with
tegmental and limbic formations.
The memory cells in posterior association cortex gen-
erally show more sensory specificity than those in pFC
and tend to cluster in areas of association for the sensory
modality of the memorandum. Thus, cells in inferotempo-
ral cortex (Figure 2) are tuned to visual memoranda (Miller,
Li, & Desimone, 1991; Miyashita & Chang, 1988; Fuster
& Jervey, 1982), whereas cells in parietal cortex are tuned
to spatial (Andersen, Bracewell, Barash, Gnadt, & Fogassi,
1990) or tactile (Zhou, Ardestani, & Fuster, 2007; Burton
& Sinclair, 2000; Zhou & Fuster, 1996) memoranda. As
2050 Journal of Cognitive Neuroscience Volume 21, Number 11
in the pFC, although to a lesser degree (in terms of cell
numbers and response magnitude), cells can be found
in posterior areas that respond to the motor and/or re-
ward attributes of memory tasks.
The thus far summarized microelectrode evidence
points to the widely distributed character of cortical mem-
ory networks, with components in posterior (sensory
or perceptual) cortex and in frontal (executive) cortex
(Constantinidis & Procyk, 2004; Fuster, 1995). It further
points to the presence of cells and cell assemblies that
respond to more than one memorandum or different
characteristics of the same memorandum. This property
of “multiple tuning”argues for the belonging of cells to
multiple networks and for their flexible functional al-
legiance to those networks. Next, I discuss how single-
unit evidence substantiates the associative character of
working-memory networks and their structural identity
with long-term memory networks.
WORKING MEMORY FROM
LONG-TERM MEMORY
The neuropsychological literature provides indirect evi-
dence that the cortical substrate for working memory co-
incides with the substrate for long-term memory. That
evidence derives from a large number of animal and hu-
man lesion studies (reviewed in Fuster, 1995), indicating
that several cortical areas are implicated in the working
Figure 1. Schematic diagram of structural and dynamic principles
of memory network architecture and function. (A) Hierarchies of
perceptual (blue circles) and executive (red circles) networks
or cognits of different sizes and hierarchical levels in one cortical
hemisphere (three sizes and hierarchical levels have been
arbitrarily chosen); thin lines represent bidirectional connections
between cognits. (B) Stimulus 1 activates a large distributed cognit
(Memory 1) made of smaller, more local cognits (three sizes,
color-filled circles) connected by bidirectional excitatory pathways
(large maroon lines). These smaller cognits constitute nodes of
the large memory network. (C) Stimulus 2 activates memory
Network 2 and its nested component networks. Note that
Networks 1 and 2 are heterarchical and share common components
(nodes). Figure 2. Rasters and spike frequency histograms of the activity of
a memory cell in inferotemporal cortex during performance of
delayed matching to sample. A trial begins with brief presentation of a
color sample in the top button. After a 16-sec delay (memory period),
two colors appear in the lower buttons. If the monkey chooses the
color matching the sample, juice is delivered to his mouth through a
spigot. The sample and the position of the choice colors change at
random from trial to trial. In the delay of 16 consecutive trials
(intermixed by sample color), the cell shows elevated activity after
red but not green sample. Note that, after the choice, the elevated
discharge in red-memorandum trials descends abruptly to baseline
level, although the choice has required a second foveation of the
sample color—but without further need for working memory. Adapted
from Fuster and Jervey (1982).
Fuster 2051
memory of certain classes of stimuli as well as in their
retention in long-term memory. The lesion of a given as-
sociative area in the monkey induces deficits in delayed-
response tasks with stimuli of a given modality (visual,
spatial, tactile, etc.), as it does also in the recall or recog-
nition of stimuli of the same modality. In the human, am-
nesias or agnosias for that modality result from lesions of
areas roughly homologous to those of the monkey that
are related to the same modality.
By definition, moreover, working memory determines
a prospective action, commonly a decision between alter-
natives (e.g., a selective instrumental response, a verbal
response, a logical inference, etc.) that is based on long-
term memory. Its content and context are inextricably
linked to that memory, whether they include a simple
learned conditional response to a simple sensory stimu-
lus, an element of lexicon, or the conclusion of a syllo-
gism. Thus, whereas most long-term memory never
enters working memory, all working memory is not only
anchored in long-term memory but also part of it. That
part of long-term memory varies with the circumstances
and is evoked by them; that is, by the associations of
the material to be retained and the prospective conse-
quences of that act of retention, all of them previously
stored in permanent memory.
More direct evidence of the associative nature of working-
memory networks, and thus their identity to cognits of
long-term memory, comes from electrophysiology. Espe-
cially demonstrative are memory cells that show correlated
responses to stimuli of the same or different modality
associated with one another by learning. Such cells have
been found in both frontal (Fuster et al., 2000) and
posterior (Schlack & Albright, 2007; Zhou et al., 2007;
Messinger, Squire, Zola, & Albright, 2001; Gibson &
Maunsell, 1997; Miyashita, 1988) association cortices (Fig-
ure 3); tactile–visual cell response correlations can be
found even in primary somatosensory cortex (Zhou &
Fuster, 1995). The evidence becomes compelling as the
correlations develop in the process of acquiring the asso-
ciations in long-term memory. Further evidence of the
identity of working-memory and long-term memory net-
works is the observation that working-memory cells do
not respond only to the memoranda but also to assorted
sensory and motor inputs that are associated with the
performance of the task at hand (Zhou et al., 2007). In vi-
sual processing, the associative inputs from other parts
of the sensorium would contribute to a stimulus of what
has been considered context (Bressler & McIntosh, 2007;
Albright & Stoner, 2002; Fuster, 1990). Context includes
the history and the meaning of the stimulus in long-term
memory.
Whereas sensory–sensory associations in working mem-
ory have been demonstrated in both posterior and fron-
tal cortex, sensory–motor and motor–motor associations
appear mainly, if not exclusively, in pFC. There, cells seem
to belong to assemblies that encode behavioral sequences
or “temporal motor gestalts”of relatively abstract nature
(Averbeck & Lee, 2007; Shima, Isoda, Mushiake, & Tanji,
2007), regardless of their individual motor components
(Figure 4). Furthermore, some frontal memory cells are
attuned to conditional rules, another indication of their
involvement in associative long-term memory (Mansouri,
Matsumoto, & Tanaka, 2006; Wallis, Anderson, & Miller,
2001; Asaad, Rainer, & Miller, 2000; White & Wise, 1999).
The associative character of working-memory cells sub-
stantiates the assumptions (a) that memory cells belong
to networks of long-term memory that associate the var-
ious sensory and motor features of working-memory de-
mands, including but not limited to the memoranda used
by the animal in working-memory tasks, (b) that those
networks can incorporate widely dispersed neurons in
noncontiguous cortical areas, and (c) that the networks
and their associations are formed by experience.
Within a given cognitive network, however, the density
of associations appears far from homogeneous. To judge
from the groupings of cells attuned to the same or similar
stimuli, a working-memory network seems to contain
nodes of heavy association in areas of relative functional
specialization. Such nodes may be characterized as spe-
cial “modules”or “mini-networks”for spatial, visual, audi-
tory, tactile, or other working memory. However, their
function in the retrieval of long-term memory, as in work-
ing memory, is strictly dependent on their previously estab-
lished associations with other modules or cells scattered
over the width and the depth of the cortex of association,
both frontal and posterior.
To sum up, studies of cortical memory cells lead to the
tentative conclusion that working memory consists of
temporary activation of a preexistent network of long-
term memory. That network connects with one another
all the smaller networks and neuronal assemblies that, in
the aggregate, represent the associated features, per-
ceptual as well as executive, of the behavioral interaction
of the organism with its environment. At the onset of a
trial in a working-memory task, the activated network
is updated by the presence of the memorandum. Thus,
the updated network of long-term memory becomes
operational, and the process of temporary retention of
that memorandum begins within the context of the task.
Neither the content nor the dynamics of working mem-
ory can be separated from the substrate of a sector of
long-term memory and its temporary activation to bridge
the recent past with the proximate future.
MECHANISMS OF WORKING MEMORY
The mechanisms of persistent neuronal activation in work-
ing memory are not well understood. One key mechanism
appears to be the reverberation of cortical circuits by re-
entry of activity from one neuron or neuronal assembly to
another by mutual excitation. That mechanism was first
proposed by Hebb (1949) as the basis of short-term mem-
ory. It has never been conclusively proven, although now
it is gathering evidence as the basis of working memory.
2052 Journal of Cognitive Neuroscience Volume 21, Number 11
Cortical reverberation by reentry has a well-recognized
anatomical base. The existence of profuse recurrent axons
in the cortex has been known since Lorente de Nó
(1949) described them; the axons make reciprocal excit-
atory synapses on neighboring or distant neurons. Most
all pathways between frontal and posterior association
cortices are bidirectional, and so are the connections
between those cortices and thalamic nuclei (Petrides &
Pandya, 2002; review in Fuster, 2008). Therefore, there
is ample structural potential in cortical circuitry for sus-
tained reverberation in working-memory networks, how-
ever separate their neurons may be. Accordingly, reentry
is an integral part of the most plausible computational mod-
els of working memory ( Warden & Miller, 2007; Wang,
2006; Brunel & Wang, 2001; Zipser, Kehoe, Littlewort, &
Fuster, 1993). Some of these models assume that reen-
try connections are mediated through dopamine, gluta-
mate, γ-aminobutyric acid, or N-methyl D-aspartate synaptic
transactions.
Most computational models of working memory, how-
ever, assume the bistability of their functional architecture:
discharge at a certain fixed elevated frequency when in
Figure 3. Cross-temporal association of sound and color in prefrontal
cells. Above left: Trial event sequence: (1) brief tone; (2) 10-sec delay;
(3) two colors simultaneously presented; (4) animal rewarded for
correctly (c) choosing the color that matches the tone according to the
learned rules of the task—green if low-pitch tone, red if high-pitch
tone. (Tone and color position change at random between trials.)
Above right: Monkeyʼs cortex showing in blue the region from which
units were recorded; Brodmannʼs numeration in frontal areas. Below:
Firing frequency histograms from two cells, one (top) selective for
high tone and red and the other (bottom) selective for low tone
and green.Histograms are from a 1-sec period beginning with tone
onset (left) and from a 1-sec period preceding choice of color (right).
Note the correlation of cell responses to tones and colors in accord with
the rule of the task. Adapted from Fuster et al. (2000), with permission. Figure 4. Prefrontal cell signaling abstract plan of action. The
monkey is trained to perform sequences of three hand movements
(push, pull, and turn) in three categorical combinations: (A) “Paired”
(one movement repeated, followed by another repeated; e.g.,
turn–turn–pull–pull); (B) “alternate”(repeated alternation of two
movements; e.g., push–turn–push–turn); (C) “four repeat”(e.g.,
pull–pull–pull–pull). Associated auditory and visual signals instruct the
animal to memorize and to perform the three abstract categories of
sequences. Cell records were taken from lateral pFC before and during
performance of each sequence. Some cells showed increased firing in
preparation for—planning—all the sequences of one given sequence
category and not the others. Green dots mark sites from which 0, 1, 2,
or 3 category-selective cells were recorded. ARC = arcuate sulcus;
PS = principal sulcus. The records are from a cell activated (red
histogram) before “paired-action”sequences, regardless of
component movements. Records are time locked with the GO signal
(green triangle) at the start of the first memorized movement in a
sequence. Adapted from Shima et al. (2007), with permission.
Fuster 2053
memory and reversion to baseline frequency when not.
This assumption does not agree with data from the real
brain. Already from early observations (e.g., Fuster, 1973),
it has been known that, if the delay (memory period) is
long enough (>10 sec), the time course of memory ac-
tivity can adopt many forms. The most obvious are the
descending ramps of sensory-coupled cells at the start of
the delay or the accelerating ramps of motor-coupled cells
during it (Figure 5), but in any case with considerable var-
iability from cell to cell and from trial to trial (Shafi et al.,
2007). Many memory cells show mixed temporal patterns.
The analysis of interspike intervals (ISIs) reveals, in both the
baseline (intertrial) and the delay (memory) periods, a mul-
tiplicity of ISI patterns independent of frequency (Bodner,
Shafi, Zhou, & Fuster, 2005). In working memory, the num-
ber of observable patterns generally increases.
The multiplicity of firing patterns in working memory is
fully consistent with the notion that memory cells belong
to multiple networks (Figure 1B and C). One possibility is
that, when the cortex enters working memory, its mem-
ory cells fire at a variety of frequencies, each frequency
the expression of activity in a given reentry loop tran-
siting through those cells. The cells seem recruited into
a variety of reentry loops, each loop with its own rever-
berating frequency. It is reasonable to infer that the cells
belong to the multiple associative networks that define
the various attributes of the memorandum—including its
prospective attributes related to action and reward. Those
networks can be local or far-flung. Depending on the attri-
bute in the focus of working memory at any given time, a
cell would change its allegiance to one network or another
and thus become attuned to one reverberating frequency
or another.
Whether the reentry is local or far-flung, persistence of
working-memory discharge may also be caused at least
in part by long-lasting ionic changes at synaptic level. A
recent model (Mongillo, Barak, & Tsodyks, 2008) pos-
tulates that working memory is sustained in recurrent
neuronal connections by residual calcium released by in-
coming spiking, such as that elicited by the memorandum.
That residual calcium would increase presynaptically,
thereby facilitating synaptic transmission and responses
to subsequent inputs, including further recurrent inputs
or replicates of the memorandum (Figure 6). To maintain
active memory, the model would economize on spikes.
Also, the following two characteristics make the model
fully compatible with our network assumptions: (1) reen-
try within recurrent cortical networks and (2) preexistent
memory code presumably established by Hebbian associa-
tive learning and activated by the memorandum. Because
of their multiple interactions, several networks (long-term
associative memories) can be activated at the same time
(Figure 6B).
Corticocortical circuitry can maintain memory active not
only by reverberation but also by tonic influences of one
area upon another or from one part of a cognit upon
another. That circuitry would involve the well-known ana-
tomical connections between prefrontal and posterior cor-
tices (Barbas, Ghashghaei, Rempel-Clower, & Xiao, 2002;
Petrides & Pandya, 2002; Jones & Powell, 1970). The im-
portance of mutual influences between those cortices for
memory maintenance is revealed by the results of the
functional inactivation—by cooling—of pFC on the activity
of memory cells in posterior cortex or vice versa (Chafee
& Goldman-Rakic, 2000; Quintana, Fuster, & Yajeya, 1989;
Fuster, Bauer, & Jervey, 1985). For example (Fuster et al.,
1985), cooling the lateral pFC to 20°C during a visual de-
layed match-to-sample task diminishes the ability of some
inferotemporal cells to discriminate the color of the mem-
orandum during the memory period (delay); presumably
as a consequence, the animal commits more errors of
color working memory than at normal cortical tempera-
ture. Thus, prefrontal cooling diminishes “cognitive control”
(Miller & Cohen, 2001) over the inferotemporal cortex
in working memory. Alternatively (or concomitantly), the
procedure interrupts the reverberating loop between the
two cortices that presumably supports active memory
maintenance.
In sum, the mechanisms of working memory are not
yet established, but there is increasing evidence that they
include the reverberation in circuits within the neural
network, part of long-term memory, that has been acti-
vated by the memorandum. Additional evidence is to
be found in the study of patterns and periodicities of
memory-cell firing.
RHYTHM AND SYNCHRONY
The presence of electrical oscillations in the cortex has
been well known since Hans Berger (1929) described
the EEG on the headʼs surface. Oscillations are encoun-
tered not only in the extracranial EEG but also in the local
field potentials (LFPs), which are intracortical signals re-
flecting summed dendritic activity in local neuronal as-
semblies; essentially, LFPs have the same electrogenesis
as the EEG but in more discrete cortical domains than
those that generate the EEG signal. Many rhythms have
been related to a wide variety of brain states and psycho-
logical conditions. Although their observable range is al-
most continuous, those rhythms can be categorized by
frequency into a finite number of discrete classes (Fig-
ure 7). The physiological exploration of electrocortical
rhythms is especially productive with regard to two
highly interrelated cognitive functions: perception and at-
tention. Both these functions are, in turn, highly related
to memory. Perceiving is remembering as much as sens-
ing, and working memory is attention focused on an inter-
nal representation.
In the late 1980s, it was discovered that separate groups
of neurons in the visual cortex fired in synchrony (gamma
range) in response to visual stimuli (Jagadeesh, Gray, &
Ferster, 1992; Engel, König, Kreiter, & Singer, 1991; Gray
& Singer, 1989; Eckhorn et al., 1988). One interpretation
of this phenomenon was that separate neurons firing
2054 Journal of Cognitive Neuroscience Volume 21, Number 11
in synchrony engaged in what was called “perceptual
binding,”that is, the binding of the diverse visual features
of the stimulus into a perceptual whole (Engel, Fries, &
Singer, 2001; Gray, Konig, Engel, & Singer, 1989). Neural
binding, thus, became a model of perceptual constancy to-
ward solving one of the central problems of physiological
psychology (Klüver, 1933). Perceptual constancy would
emerge from the binding of sensory features, namely, from
Figure 5. (A) Task with
temporal and spatial separation
between cues and responses
(below, contingencies between
them). The animal faces a panel
with three stimulus–response
buttons above a pedal, where
the operant hand rests at all
times except to respond to
stimuli. After a warning signal
(flash), one of four colors
appears in the central button
(the cue). A period of delay
follows, at the end of which the
two lateral buttons turn red and
green, or both white. If those
two buttons are colored, the
animal must choose the one
matching the cue; if they are
white, the animal must choose
left for red cue, right for green
cue, right for yellow cue, and
left for blue cue. Thus, in
the delay, if the cue has been
yellow or blue, the animal
can predict the rewarded
response direction (right or
left, respectively) with 100%
probability, whereas if the cue
has been red or green, with only
75% probability (left if red, right
if green). Colors and direction of correct choice change at random. c = correct choice. (B) Average discharge of motor-coupled cells during
the delay of trials with 100% predictable response direction (top graph) and 75% predictable response direction ( lower graph). Note that the
acceleration of discharge during that memory period is related to predictability. C = cue; R = response. Adapted from Quintana and Fuster (1999),
with permission.
Figure 6. Dynamics and
anatomy of memory network.
(A) Short-term synaptic
plasticity model. At left, kinetic
framework with equations for
synaptic variables: δ= Dirac
delta function; t
sp
= time of
presynaptic spike; V
m
=
membrane potential. At right,
postsynaptic response, through
facilitating connection, to a
volley of presynaptic spikes.
During the volley, uincreases
(facilitation) and xdecreases
(depression). The product ux
modulates synaptic efficacy.
(B) Network architecture.
Colored triangles are excitatory
neurons in networks that
encode different memories.
Black empty triangles are
nonselective neurons. Black
circles are inhibitory neurons.
Adapted from Mongillo et al.
(2008), with permission.
Fuster 2055
the mutual functional relationships between cell groups
despite physical variations in each of those features individu-
ally. A relational code would thus emerge in similar manner
as a gestalt. Could oscillations play the same role at higher
cognitive levels, especially in working memory?
The causal relationships between electrocortical oscil-
lation and cell spiking are obscure, although there is in-
creasing evidence of the coupling of cell spikes and LFPs
(Lee, Simpson, Logothetis, & Rainer, 2005; Pesaran, Pezaris,
Sahani, Mitra, & Andersen, 2002; Fries, Reynolds, Rorie, &
Desimone, 2001). On the one hand, periodic spike trains
can generate periodic dendritic potentials (Reyes, 2003).
On the other, dendritic oscillations can change the excit-
ability of a cellʼs membrane, thereby biasing its produc-
tion of spikes, which may occur in phase at various times
with respect to the oscillatory cycle (Tsodyks, Skaggs,
Sejnowski, & McNaughton, 1996).Both mechanisms may
operate in the cortex to some degree, in any case lead-
ing to temporal correlations between periodic spikes and
oscillations.
Synchronous cortical oscillations appear to result from
the interaction of both, local factors at the membrane of
the cell and circuit factors at the network in which the
cell is embedded. Probably among the latter are the peri-
odic inputs from the thalamus (Steriade, 2001; Llinás,
1988), the so-called “inhibitory clocking networks”of in-
terneurons (Buzsáki, Geisler, Henze, & Wang, 2004), and
the loops of corticocortical connections. In ensuing dis-
cussion, I emphasize the last of these factors for its rele-
vance to reentry in working memory, without excluding a
coadjutant or even primary role for the others.
If we assume that reentry is an important component
of network architecture, length of circuitry should be a
determinant of oscillatory frequency. However, length
of circuitry does not necessarily mean length of fibers.
Braitenberg and Schüz (1998) argued that some long fi-
bers (e.g., corticocortical) may actually shorten the ef-
fective connectivity within networks. It follows that there
could be an inverse relationship between network size,
in terms of effective circuitry, and frequency of oscilla-
tion (Buzsáki & Draguhn, 2004; Freeman, Rogers, Holmes,
& Silbergeld, 2000). This reasoning accommodates a
good amount of electrophysiological data (Buzsáki et al.,
2004; Csicsvari, Jamieson, Wise, & Buzsaki, 2003; Steriade,
2001).
Complex cortical processing during behavior involves
many networks, large and small, some nested within
others. This, in conditions of heightened attention and
working memory, will entail a proliferation of activated
networks oscillating at multiple frequencies. More gener-
ally, the proliferation and the fragmentation of frequencies
are most likely to be at the root of the “desynchronization”
of the EEG as the subject awakes from sleep or responds
to sensory stimuli (Basar & Bullock, 2000; Pfurtscheller &
Lopes da Silva, 1999). By contrast, at rest or in sleep, when
simple large networks prevail, low frequencies (theta or
lower) will predominate. This does not preclude that, by
virtue of their stereotypical functional architecture, certain
parts of “ancient cortex”involved in memory, such as the
hippocampus, exhibit low oscillatory frequencies even, or
especially, when its networks are highly active (Huxter,
Burgess, & OʼKeefe, 2003; Penttonen & Buzsáki, 2003;
Huerta & Lisman, 1995).
In the sensory association cortex engaged in sharply
focused (“top–down”) attention to the location or char-
acteristics of sensory stimuli, high-frequency oscillations
(beta and gamma) have been observed in LFPs as well
as in unit discharge (Lakatos, Karmos, Mehta, Ulbert, &
Schroeder, 2008; Buschman & Miller, 2007; Saalmann,
Pigarev, & Vidyasagar, 2007; Womelsdorf, Fries, Mitra, &
Desimone, 2006; Brovelli, Lachaux, Kahane, & Boussaoud,
2005; Fries et al., 2001). Presumably, those oscillations re-
flect reentrant activity in the small, high-frequency oscil-
lating networks that process the item of information that
the subject attends to. That phenomenon is more appar-
ent when attention is extended in time. This is the situa-
tion in working memory, which is attention focused on
the internal representation of a recent stimulus for pro-
spective action (Fuster, 2003; Baddeley, 1993). During
working memory, in sensory or association cortex, oscilla-
tory synchrony (Figure 8) commonly appears (Lee et al.,
2005; Rizzuto et al., 2003; Pesaran et al., 2002; Tallon-
Baudry, Bertrand, & Fischer, 2001). In the human, syn-
chrony predominates in the beta and theta ranges. Further,
the desynchronizing transition from alpha to beta or higher
has been noted to be selective in different areas depend-
ing on the modality or memory load of the memoran-
dum (Stipacek, Grabner, Neuper, Fink, & Neubauer, 2003;
Klimesch et al., 1996).
In higher association cortex, cellular activity during
memory activation is multistable and multivariate, as net-
works there profusely intersect. They represent in long-
term memory the multiple associated aspects of the
memorandum. Accordingly, and supporting our reason-
ing for the commonality of anatomical substrates for
working and long-term memory, we have observed cell-
firing frequencies attuned to several associated aspects
of a working-memory task (Zhou et al., 2007; Fuster
et al., 2000). During the memory period, the analysis of
ISIs shows extensive variability (Shafi et al., 2007) and
a proliferation of patterns (Bodner et al., 2005), both
supposedly reflecting the affiliation of cells to multiple
memory networks. Thus, the electrocortical records
from human subjects in situations of high cognitive de-
mand exhibit a multiplicity of rhythms in areas of asso-
ciation. Especially prevalent are oscillations in the theta,
alpha, beta, and gamma frequencies (Sehatpour et al.,
2008; Gevins & Smith, 2000), in some instances modu-
lating one another (Lakatos et al., 2008; Canolty et al.,
2006).
The most direct electrocortical evidence of the acti-
vation of interregional networks in working memory is
the synchrony of high-frequency (beta and gamma) os-
cillations in frontal and posterior regions of association
2056 Journal of Cognitive Neuroscience Volume 21, Number 11
cortex during high attention and working-memory per-
formance (Axmacher, Schmitz, Wagner, Elger, & Fell,
2008; Sehatpour et al., 2008; Buschman & Miller, 2007;
Saalmann et al., 2007; Brovelli et al., 2004; Gross et al.,
2004; Brovelli, Battaglini, Naranjo, & Budai, 2002; Tallon-
Baudry et al., 2001; Stein, Rappelsberger, Sarnthein, &
Petsche, 1999; Bressler, Coppola, & Nakamura, 1993). In
the next section, we see in neuroimages the reflection
of that synchrony in posterior and frontal regions during
working memory. In any case, taken as a whole, the
electrocortical evidence strongly supports the broader
principle of widely distributed and overlapping memory
networks.
NEUROIMAGING OF
MEMORY-NETWORK ACTIVATION
The judicious use of functional imaging methods in the
human has contributed mightily to support the network
paradigm of memory. It has also contributed to the under-
standing of the dynamics of memory networks. However,
the imaging methodology has considerable limitations. It
is essential to be aware of them before any review of imag-
ing data on memory.
The following are the most relevant limitations of PET
and fMRI: (1) The neurovascular coupling function is still
poorly understood. (2) Temporal resolution is inade-
quate to measure rapid changes in memory acquisition
and recall. (3) Large individual variability limits conclu-
sions on memory distribution or mechanisms. (4) Linear
models of blood-flow change may not be fully compat-
ible with memory functions inherently nonlinear. (5) To
the extent that memory uses the same cortical networks
Figure 7. Classes of oscillatory activity in the cortex. For each
frequency band, its range is shown as well as the common term for
it. Note the linearity of classes in logarithmic scale. Adapted from
Buzsáki and Draguhn (2004), with permission.
Figure 8. LFP and single-cell
discharge at three sites in
extrastriate cortex during a
working-memory task. (A) Raw
LFP signals simultaneously
recorded through baseline
pretrial, sample, delay, and
choice periods (separated by
vertical lines) of a delayed
matching-to-sample trial.
(B) Theta-band-filtered LFPs.
(C) Single-unit activity (SUA)
from each of the same three
recording channels. (D) Each
unit emits action potentials at a
preferred angle (radial line) of
the theta wave. Adapted from
Lee et al. (2005), with
permission.
Fuster 2057
as other related cognitive functions (e.g., attention, per-
ception, language), it is difficult to disambiguate mem-
ory activation from that of those other functions. And
(6) the neural inhibition of memory cannot be easily
differentiated, by imaging, from its activation. Because
of these limitations, the merits of any imaging study of
memory depend on the investigatorʼsabilitytomake
only indispensable assumptions and use appropriate
controls and analytical methods. The evidence summa-
rized in this section comes from studies that meet those
criteria.
Neuroimaging does not allow the precise tracing of the
boundaries of active memory networks. The conven-
tional assessment of cortical activation, in both intensity
and extensity, is essentially analogous to that of separat-
ing signal from noise. The investigator sets a threshold—
calculated from normalized baseline values—and deter-
mines the significant deviations that exceed it. Here the
problem is to distinguish the variance of active memory
from that of background noise. The presence of “default
networks,”active in the resting state (Fox & Raichle,
2007), as well as the increased variance and the dimin-
ished activation at the edges of an active memory net-
work makes the threshold setting critical yet somewhat
arbitrary. If that threshold is too low, the networks ap-
pear larger than they are; if it is too high, the networks
appear smaller.
Like microelectrode research, the functional neuroimag-
ing of working memory focused at first on the pFC, that
is, on the executive sector of memory networks. PET stud-
ies showed activation of lateral pFC, especially on the
right, in spatial working memory (Jonides et al., 1993;
Petrides, Alivisatos, Evans, & Meyer, 1993). That was sub-
sequently corroborated by fMRI studies (Ricciardi et al.,
2006; Ranganath, Cohen, Dam, & DʼEsposito, 2004).
Both PET (Swartz et al., 1995) and fMRI (Cohen et al.,
1994) showed also lateral prefrontal activation in visual
nonspatial working memory. A special case of the latter
is the memory for faces, which activates not only the lat-
eral prefrontal areas but also the cortex of the fusiform
gyrus, an area involved in face recognition (Rama &
Courtney, 2005; Gazzaley, Rissman, & DʼEsposito, 2004;
Ranganath et al., 2004; Mecklinger, Bosch, Gruenewald,
Bentin, & Von Cramon, 2000). The pFC, especially on the
left, is activated in verbal working memory (Buchsbaum,
Olsen, Koch, & Berman, 2005; Goldstein et al., 2005;
Narayanan et al., 2005; Crottaz-Herbette, Anagnoson, &
Menon, 2004; Paulesu, Frith, & Frackowiak, 1993; Petrides,
Alivisatos, Meyer, & Evans, 1993) and also in working mem-
ory for mental arithmetic (De Pisapia, Slomski, & Braver,
2007; Kondo et al., 2004).
Whatever the content in working memory, the amount
of prefrontal activation is directly proportional to the
memory load (Narayanan et al., 2005; Cairo, Liddle,
Woodward, & Ngan, 2004; Leung, Seelig, & Gore, 2004;
Jaeggi et al., 2003; Linden et al., 2003; Postle, Berger,
Goldstein, Curtis, & DʼEsposito, 2001). In other words,
prefrontal activation increases as a function of the num-
ber and complexity of items in memory. Practice, how-
ever, decreases load-related activation. Is this a sign of
memory consolidation, which entails economy of synap-
tic resources? Or, is it of the migration of executive mem-
ory to subcortical structures (e.g., basal ganglia)?
Practically all the relevant studies show that the pFC is
not the only region activated in working memory. Almost
invariably, one or more posterior cortical areas are con-
comitantly activated. Which posterior area or areas are
activated depends on the modality of the memorandum:
inferior temporal areas if it is visual (additionally fusi-
form cortex, if it is a face), posterior parietal if it is spa-
tial, superior temporal if it is auditory or verbal, and
anterior parietal if it is tactile. Homologous areas of cor-
tex appear activated in imaging records of the human
as in microelectrode records of the monkey. Prefrontal
areas are activated inasmuch as executive memory is in-
volved and posterior areas inasmuch as perceptual mem-
ory is involved.
Consistent with the microelectrode evidence of inter-
actions between prefrontal and posterior association
areas, several imaging studies indicate that those interac-
tions underlie the role of pFC in so-called “executive cog-
nitive control”or “top–down attention”(Roth, Serences,
& Courtney, 2006; Yoon, Curtis, & DʼEsposito, 2006;
Buchsbaum et al., 2005; Curtis, Sun, Miller, & DʼEsposito,
2005; Postle, 2005; Kondo et al., 2004; Li et al., 2004; Sakai
& Passingham, 2004). The medial pFC—especially ante-
rior cingulate—seems part of a so-called “anterior atten-
tion system,”dedicated to spatial attention (Lenartowicz
& McIntosh, 2005; Kondo et al., 2004; Petit, Courtney,
Ungerleider, & Haxby, 1998; Posner & Petersen, 1990).
However, the attribution of “control”to the pFC, in atten-
tion or any other cognitive function, implies for that cortex
aroleof“central executive,”which makes little sense in
biological terms and leads to an infinite regress (Fuster,
2008; McIntosh, 2000).
In the light of imaging data, the “central executive”role
of the pFC is in principle reducible to its role of integrat-
ing for prospective action a continuous flow of inputs
from the internal and the external environments. The
memory networks of posterior cortex are part of the in-
ternal environment, which in turn can be activated by ex-
ternal stimuli and the effects of action, all within the
perception–action cycle (below). In that same frame-
work, it is possible to understand the role that imaging
studies attribute to the pFC in the retrieval and encoding
of memory (Mitchell, Johnson, Raye, & Greene, 2004;
Ranganath et al., 2004; Rypma & DʼEsposito, 2003; Lee,
Robbins, & Owen, 2000; Buckner et al., 1995; Kapur
et al., 1994; Tulving, Kapur, Craik, Moscovitch, & Houle,
1994; Tulving, Kapur, Markowitsch, et al., 1994); both re-
trieval (except in involuntary or free recall) and encoding
are executive acts prompted by external stimuli.
Figures 9–12 illustrate schematically the trends of cor-
tical activation on the left hemisphere during perfor-
2058 Journal of Cognitive Neuroscience Volume 21, Number 11
mance of three working-memory tasks: visual, spatial, and
verbal. The activation images consist of stills extracted
from motion pictures constructed by graphic synthesis
of data in the following publications (those preceded by
an asterisk are based on quantitative meta-analysis of mul-
tiple studies): Buchsbaum et al., 2005; Goldstein et al.,
2005; *Rajah & DʼEsposito, 2005; Crottaz-Herbette et al.,
2004; *Wager & Smith, 2003; *Cabeza & Nyberg, 2000; DʼE-
sposito, Postle, & Rypma, 2000; *Duncan & Owen, 2000;
Mecklinger et al., 2000; Pollmann & Von Cramon, 2000;
*Casey et al., 1998; Petit et al., 1998; and Courtney, Ungerlei-
der, Keil, & Haxby, 1997. No attempt was made to normalize
quantitative differences. The time course of activation,
which is unavailable in the majority of publications, was
grossly estimated based on unit data from the primate in
working-memory tasks.
A reasonable explanation of the joint prefrontal–posterior
activation and functional interdependence in working mem-
ory is that in the course of behavior—as in reasoning and
language—a prefrontal network of executive memory in-
teracts with a posterior network of perceptual memory.
Both complement each other and cooperate in short-term,
long-term, and working memory. Both control each other
reciprocally at the top of the perception–action cycle. In
serial behavior, the control shifts successively in circular
fashion between the two. In working memory, that recip-
rocal interaction adopts the form of neural reverberation
Figure 9. Approximate location of various cortical areas on the
three-dimensional imaging maps of subsequent figures. Areas in
convexity cortex designated with white labels; those in medial cortex
with gray labels. SMA = supplementary motor area. Below, temporal
display of a trial in a typical visual working-memory ( WM) task (delayed
matching-to-sample) with faces. First upward inflexion of blue time
line marks the time of presentation of the sample face; second
inflexion, that of the choice faces. Delay—memory—period, between
sample and choice, lasts 20 sec. This and three subsequent figures
are made with the assistance of Allen Ardestani and personnel of the
UCLA Laboratory of Neuro Imaging: Arthur Toga (director), Amanda
Hammond, and Kim Haber.
Figure 10. Relative (above
baseline) cortical activation at
six moments in time (marked
by yellow triangle) in the course
of the visual (face) memory task
outlined in the previous figure.
Activations of convexity cortex
in red, of medial cortex in pink.
(1) At the sample, activation is
restricted to visual and
posterior inferotemporal cortex;
(2) in the early delay, it extends
to lateral pFC, anterior
cingulate, anterior
inferotemporal cortex, and
fusiform cortex; (3) in
mid-delay, it persists in
prefrontal, inferotemporal,
and fusiform cortex; (4) in
late delay, it migrates to
premotor areas, persisting in
inferotemporal and fusiform
cortex; (5) at the response
(choice of sample-matching
face), it covers visual,
inferotemporal, and fusiform
cortex in the back and extends
to motor areas (including FEFs),
SMA, and OFC in the front; and
(6) after the trial, activation
lingers in anterior cingulate
and OFC.
Fuster 2059
Figure 11. Activation in a
spatial memory task; the
memorandum, in 1, is a star
at a certain position on the
screen—eye fixation on center,
red cross. Activations of
convexity cortex in green,
of medial cortex in yellow.
(1) At the memorandum,
activation is restricted to visual
cortex; (2) in early delay, it
extends to lateral prefrontal,
anterior cingulate, and posterior
parietal cortex; (3) in mid-delay,
it persists in prefrontal and
posterior parietal cortex;
(4) in late delay, it migrates to
premotor areas (including SMA)
and FEFs, persisting in posterior
parietal cortex; (5) at the
response (eye saccade to
position of the cue), it covers
visual and inferior parietal
cortex in the back and extends
to FEFs, SMA, and OFC in the
front; and (6) after the trial,
activation lingers in anterior
cingulate and OFC.
Figure 12. Activation in a
verbal memory task; the
memorandum, in 1, is a word
through earphones. Activations
of convexity cortex in orange,
of medial or sulcal cortex
in yellow. (1) At the
memorandum, activation is
restricted to auditory cortex,
superior temporal gyrus, and
inferior frontal cortex; (2) in
early delay, it extends to lateral
prefrontal, anterior cingulate,
and superior-temporal and
parietal association cortex; (3)
in mid-delay, it persists in
prefrontal and temporo-parietal
cortex; (4) in late delay, it
persists in prefrontal and
migrates to premotor
areas while persisting in
temporo-parietal cortex;
(5) at the response (signaling
whether cue word is on the
screen), it covers visual and
temporo-parietal cortex in the
back and extends to FEFs, SMA,
inferior frontal, and OFC in the
front; and (6) after the trial,
activation lingers in anterior
cingulate, OFC, and language
areas.
2060 Journal of Cognitive Neuroscience Volume 21, Number 11
between—and within—the two to retain the memoran-
dum and its associations, including the expected response
and reward.
In summary, imaging shows that working memory ac-
tivates simultaneously a region of pFC and at least one
other region of posterior cortex. As indicated by the
other reviewed methodologies, reverberating reentry
between the two, at the top of the perception–action
cycle, is probably the key mechanism of working-memory
maintenance. The particular posterior region or regions
most activated in working memory roughly coincide with
the region(s) containing the most modality-specific
memory cells in the monkey. Neuropsychological data
implicate those regions in the learning, discrimination,
and long-term memory of modality-specific material.
Functional imaging, further, supports the conclusion
that working memory is based on the sustained activa-
tion of a widespread cortical network of long-term mem-
ory or cognit. That network unifies neuron assemblies in
noncontiguous cortical areas and represents the asso-
ciated aspects of the memorandum, executive as well
as sensory, including the ad hoc trial- or situation-specific
information.
HOW ARE MEMORY NETWORKS MADE
AND ORGANIZED?
At the foundation of all learning and memory, there are
certain changes in the membrane of nerve cells that are
common to all organisms (Kandel, 2000). These changes,
largely mediated by synaptic modulation, usually take
place in the protein structure of the postsynaptic mem-
brane and entail changes in the excitability of the cell.
Evidence mostly from invertebrate organisms and from
the mammalian hippocampus indicates that the synaptic
modulation of neural circuits in learning and memory
obeys the principles theoretically formulated in the mid-
20th century by Hayek (1952) and Hebb (1949). One such
principle is the facilitation of connection between two cells
when both fire repeatedly together, one exciting the
other. Another principle—emphasized by Hayek—is the
facilitation of the response of a cell to two stimuli arriving
to the cell simultaneously (synchronous convergence).
Arguably, those two principles are reducible to one, espe-
cially if recurrent axons are taken into account. One unify-
ing property of synaptic memory-forming mechanisms is
embodied in both principles: the temporal coincidence
or the near coincidence of synaptic events.
The hippocampus plays a major role in the consolida-
tion of new “declarative”memory (Squire, 1986; Cohen &
Squire, 1980), that is, memory accessible to conscious-
ness, which includes autobiographical and semantic mem-
ories (Tulving, 1987). It is widely assumed that, in the
process of consolidation, the hippocampus cooperates
with the neocortex, where long-term memory ultimately
settles. On the basis of psychological testing of hippo-
campal patients, however, the hippocampus has been
excluded from so-called procedural or “nondeclarative”
memory (Zola-Morgan & Squire, 1993). Two basic prob-
lems remain unresolved (Squire & Bayley, 2007; Frankland
& Bontempi, 2005). One is to construe in the hippocam-
pus a temporary “map”or depository of the complex in-
formation in autobiographical memories, with its mixture
of new and old, semantic and episodic, explicit and im-
plicit. The other is to reconcile a memory-consolidating
role of the hippocampus with its participation, at least in
rodents, in the encoding of spatial locations (Kjelstrup
et al., 2008; OʼKeefe & Recce, 1993) and olfactory mem-
ory (Eichenbaum, Fagan, Mathews, & Cohen, 1988; Staubli,
Fraser, Kessler, & Lynch, 1986). In lower mammals, the
hippocampus—ancient cortex—possibly plays with regard
to vitally adaptive memories (olfaction, spatial naviga-
tion, and touch) the same role that the neocortex plays
in higher mammals with regard to more elaborate percep-
tual memories.
Whereas the majority of neuropsychological memory
studies of the hippocampus concentrate on memory
acquired through the senses (perceptual or declarative),
the hippocampus is most probably also involved in the
formation of motor or executive memory, that is, the mem-
ory of actions. The anatomical connections of the hippo-
campus with frontal cortex are well developed (Amaral,
1987; Van Hoesen, 1982). One of the first formulations
of the synaptic concept of memory applied specifically to
executive memory (Cajal, 1923). In the absence of experi-
mental proof, it is reasonable to speculate that the prin-
ciple of temporal coincidence applies to the formation of
motor memory as well as to that of perceptual memory.
In the case of motor memory, the coinciding inputs may
be either proprioceptive or efferent copies of movement
(McCloskey, 1981).
In sum, the hippocampus enables memory formation
and consolidation in the neocortex. Here, the newly
formed cognits organize themselves (Kohonen, 1984) at
various hierarchical levels depending on their complexity
or abstraction: from the simplest and most concrete at the
bottom, in sensory and motor cortices, to the most com-
plex and abstract at the top, in the higher association
cortex of the occipital–temporal–parietal and prefrontal
regions. As it is acquired, each new memory or item of
knowledge develops from the bottom–up, from the lowest
sensory and motor levels to the highest level in cortex of
association. That development follows three largely coin-
ciding anatomical gradients: (1) a phylogenetic gradient
of increasing cortical volume (Northcutt & Kaas, 1995;
Rockel, Hiorns, & Powell, 1980) resulting from evolutionary
duplication of areas by genetic mutation (Fukuchi-Shimogori
& Grove, 2001; Rakic, 2001); (2) an ontogenetic gra-
dient of increasing myelination and maturation (Barkovich,
1995; Conel, 1963); and (3) a connectivity gradient along
either of the two ascending cortical hierarchies, motor
and sensory (Petrides & Pandya, 2002; Felleman & Van
Essen, 1991; Jones & Powell, 1970). That connectivity
Fuster 2061
is reciprocal at every step, with feed forward as well as
feedback.
The theoretical schema of Figure 13 shows the relative
position of long-term memories and cognits, after they
have been consolidated, in the perceptual and executive
cortical hierarchies. The schema is useful as a general
organization plan of cortical memory and as a graphic
statement of basic principles. Further, it leads to several
testable predictions (below). However, it is not intended
in any way as “memory map.”Also, memories and knowl-
edge are not neatly stacked in their hierarchies as the fig-
ure suggests. Indeed, most memories are to some degree
heterarchical; that is, they contain network components
at several hierarchical levels.
All individual memory derives from what I call phyletic
memory, in other words, from the “memory of the spe-
cies,”the structural phenotype of sensory and motor sys-
tems at birth. The primary sensory and motor cortex can
be rightfully called phyletic memory for the following rea-
sons: (a) it is a form of structural “memory,”acquired in
evolution, of the sensory and motor means to adapt to
the environment; (b) this primal memory, the cortical
sensory–motor apparatus, has to be used, “rehearsed,”
in certain critical periods after birth before individual
memory can be effectively formed over it; (c) thereafter,
phyletic memory is “recalled,”voluntarily or involuntarily,
in every sensation or motor action of the adult organism;
and (d), like other memories, phyletic memory is to
some degree plastic and recoverable after injury.
The upward fanning of memory networks in the scheme
of Figure 13 emphasizes the divergence of functional con-
nectivity from phyletic memory to higher, more abstract
and complex memories. As they consolidate, the latter
memory networks become widely distributed (the op-
posite of the proverbial “grandmother cell”). That or-
ganizational feature would also agree with the principle
that the larger networks (e.g., conceptual, semantic) are
formed to a large extent by the repeated coactivation
and instantiation of similar, more concrete (e.g., episod-
ic) memories. Upper cognits would thus derive and gener-
alize from lower cognits; the latter nested within the
former.
At all levels, the two hierarchies of networks are in-
terconnected by fibers that reciprocally link the cortex
of the frontal lobe with that of posterior regions. These
fibers establish the mutual associations between per-
ceptual and action networks. In behavior, language, and
reasoning, they support the active engagement of those
networks in such operations as working memory and
perception–action cycle.
Figure 13. General scheme of
the hierarchical organization of
cognits in the lateral cerebral
cortex of the left hemisphere.
Lower figure: Brodmannʼs
cytoarchitectonic map.
RF = Rolandic fissure. The
posterior cortex is gradually
shaded blue to white from
primary sensory to association
areas, the frontal cortex red to
white from primary motor to
pFC. Upper figure: Gradients of
development and organization
of cortical cognits (same color
code as below). Bidirectional
arrows symbolize: blue,
perceptual corticocortical
connectivity; red, executive
corticocortical connectivity;
green, reciprocal connectivity
between posterior and frontal
cortices. Note increasing span
and overlap of networks as they
develop from the bottom–up
(inverted cones). As they grow
upward in the hierarchy, nets
become more widespread and
represent progressively more
abstract memory and
knowledge.
2062 Journal of Cognitive Neuroscience Volume 21, Number 11
In summary, the structure of long-term memory in the
present network memory paradigm has the following
major features: (1) it is hierarchical but compatible with
a degree of heterarchical organization and dynamics; (2)
it contains perceptual cognits mainly in posterior cortex,
executive cognits mainly in frontal cortex; and (3) per-
ceptual and executive cortices—and their cognits—are
joined by long reentrant and reciprocal fibers, which serve
working memory and the dynamics of the perception–
action cycle.
The postulated functional architecture of memory
networks leads to the following predictions. Some of
them have already been partially tested and used to
support the argument for the new paradigm. They need,
however, expanded testing to confirm or reject this
paradigm.
A. Cortical lesions will induce memory deficits depend-
ing on the location and extent of the lesion. In the
posterior hierarchy, from sensory cortex to association
cortex, lesions will affect the formation, retrieval, and
working memory of progressively higher perceptual
content. Small lesions at low level (sensory cortex) will
affect simple sensory cognits. Larger lesions at higher
levels (temporal, parietal cortex) will affect larger,
more complex cognits (unimodal and polymodal agno-
sias, aphasias, and amnesias). Conversely, in the exec-
utive hierarchy, from motor to pFC, lesions will affect
progressively higher executive content. Small lesions
of motor cortex will affect the representation of simple
movements by discrete muscle groups. Larger lesions
of premotor cortex will affect the representation of
movements defined by goal and trajectory. Still larger
lesions in the pFC will affect the highest, most complex
executive memories and knowledge, including rules
and plans.
B. On the assumption of partial commonality of anatomi-
cal substrate for long-term and working memory, it can
be predicted that the activation of cognits in working
memory will elicit electrical and functional imaging
signals from the cortical areas representing the mem-
orandum. In those areas, during working memory, mi-
croelectrodes will record persistent unit discharge and
synchronous high-frequency LFP oscillations. Sustained
working memory will elicit imaging signal from those
areas. By manipulating the category and the context of
the memorandum, it will be possible to vary the source
and location of the signals and thus the spread and
location of the activated cognits. For example, a con-
crete sensory memorandum will activate a relatively
small region of sensory association cortex, such as the
superior (auditory) or the inferior (visual) temporal
gyrus. A more complex stimulus with associations of
more than one sensory modality will activate multiple
sensory association cortices. In all working-memory
tasks, the activation will be interregional, involving
simultaneously prefrontal and posterior cortex, as the
activated cognits will encompass perceptual as well as
executive networks.
BEHAVIORAL NETWORK DYNAMICS:
THE PERCEPTION–ACTION CYCLE
In the cortex, as in the rest of the brain, there are no
“systems of memory,”but there is the memory of sys-
tems. All cortical systems have their own memory, which
is inextricable from the operations they perform. The
substrate for process is inseparable from the substrate for
representation. Cognitive networks contribute to behavior
by performing the sensory and the motor functions they
represent.
From evidence reviewed, it can be reasonably inferred
that, in goal-directed behavior, posterior and frontal cognits
join together to coordinate the action. At high levels of
the cortical hierarchies, prefrontal networks, which rep-
resent broader actions and longer term executive goals,
successively activate subjacent networks that represent
shorter term, intermediate actions and goals. At every
step, action is guided by feedback from previous actions.
The entire sequence, with its subordinate steps, is gener-
ated and carried out continually by executive networks at
various levels, integrating stimuli from the environment
(internal and external) with feedback signals from that
environment, all within the framework of the perception–
action cycle.
The perception–action cycle is a basic biological prin-
ciple that governs the functional relationships of the
organism with its environment. As a process, itis the cyber-
netic circle of sensing and acting that guides the organism
to its goals. The concept originated in biology (Uexküll,
1926) and eventually entered neurology (Weizsäcker, 1950),
cognitive science (Neisser, 1976), and computational neu-
roscience (Arbib, 1985). The perception–action cycle op-
erates at all levels of the nervous system, from the spinal
cord to the cerebral cortex. In the course of complex
behavior, it engages neural networks at every hierarchical
level of the neocortex, following processing paths that
course through the environment and through connec-
tions between cortical areas (Figure 14). Action may be in-
itiated anywhere in the cycle, in the internal or external
environment. Once the cycle is engaged, its networks be-
come engaged in series as well as in parallel, with the qual-
ification that the interactions between networks may link
different levels heterarchically. Another qualification is
that the cycle is at all levels bidirectional: feed forward is
accompanied by internal feedback. That feedback serves
as a kind of corollary discharge (Teuber, 1972) to prepare
for impending perception as well as action.
Highly automated, overlearned, or instinctual behav-
iors and habits need not engage the cognits of the cerebral
cortex. They can be sequentially performed in chainlike
fashion through shunts at lower levels of the cycle. The
cortex becomes engaged in the cycle, however, when
Fuster 2063
there are discontinuities in the sequence, especially if the
latter requires temporal integration in the face of
uncertainty or ambiguity—as in working-memory tasks. In-
ternal feedback then serves perceptual as well as executive
attention.
Thus, in the course of a demanding temporal gestalt
of behavior, language, or reasoning toward a goal, the
perception–action cycle orderly recruits a series of mem-
ory networks, each modulated by internal feedback. With
the methods available, the order can be traced only coarsely.
A promising analytical method is the computing of Granger
causality on electrical signals. Essentially, this method
allows the study of information flow by analysis of mul-
tivariate changes in the interdependence of time series
from several sources (Blinowska, Kus, & Kaminski, 2004;
Brovelli et al., 2004). The application of an algorithmic
transfer function to the trains of electrical events makes
it is possible to predict the directionality of the events
between sources and thus to infer the causal relation-
ships between them. Granger causality analysis has been
successfully used in tracing electrocortical activity in net-
works of frontal and parietal areas controlling hand move-
ment in a visuomotor discrimination task (Brovelli et al.,
2004).
In neuroimaging, time-series analysis and correlation
methods provide further evidence of the spatial and dy-
namic characteristics of large-scale cognitive networks.
Bullmore et al. (1996) provided evidence of the activation
of vast networks during a task that requires visual and
semantic processing. Those networks show major foci
of activation in extrastriate cortex, angular gyrus, superior
and middle temporal gyri, premotor cortex, and Brocaʼs
area. Significantly, there are large functional distances
and discontinuities (negative connectivity) between some
of the foci. In other studies (Newman, Just, & Carpenter,
2002; Lowe, Dzemidzic, Lurito, Mathews, & Phillips, 2000),
correlation and synchronization gradients are detected
between the components of a large network in working
memory. As the collaboration of frontal and parietal re-
gions increases, the correlation and the synchronization
between them also increase.
Executive frontal networks are activated during the men-
tal planning of serial movement (Baker et al., 1996; Roland,
Larsen, Lassen, & Skinhøj, 1980; Ingvar & Philipson, 1977).
Those networks, therefore, seem to be the depositories of
so-called “memory of the future”(Ingvar, 1985). Fulfilling
Jacksonʼs prediction with regard to anterior frontal areas
( Jackson, 1882), those same networks are involved in
the coordination of the planned action, beginning with
the evocation of the objects leading to that action. For ex-
ample, whereas the viewing of animals activates posterior
areas, that of tools (“action objects”) activates premotor
cortex (Martin, Wiggs, Ungerleider, & Haxby, 1996). Fur-
ther, those networks are involved in the actual implemen-
tation of the actions, as in the performance of the Tower of
London, a test of planning ability (Morris, Ahmed, Syed, &
Toone, 1993).
Just as there is an orderly representation of actions in
frontal cortex, from the most abstract and complex in
pFC to the most concrete in motor cortex, the orderly
execution of actions follows that trend. Thus, the pro-
cessing in the executive memory side of the perception–
action cycle descends from the concept and plan of action
in prefrontal networks downward to specific action net-
works in motor cortex. Especially persuasive in this re-
spect are the studies by Badre and DʼEsposito (2007)
and Koechlin, Ody, and Kouneiher (2003). Both use behav-
ioral paradigms in which the motor response to a sensory
stimulus is contingent on progressively more remote asso-
ciations of the stimulus or the response.
In the 2003 study (Figure 15), responses depend (a) on
a simple feature (color), (b) on the presence of an addi-
tional feature (pattern) that provides the “context,”or (c)
on a recent instructional—visual—cue, a prior contin-
gency that the authors call “episode.”Thus, from condi-
tions a to c, the response to the stimulus is determined
by information of increasing complexity and associative
load. In condition c, the subject must also integrate infor-
mation across time. Condition a activates premotor cor-
tex; condition b, in addition, activates posterior pFC; and
condition c, in addition, activates anterior pFC. Further,
in the third task, the path coefficients of activation sug-
gest a processing “cascade”that originates in anterior
pFC and courses through premotor to motor cortex.
In sum, the activation of sensorimotor cognits pro-
gresses down the executive hierarchy. Two implicit quali-
fications seem necessary, however, to properly interpret
the results of those studies within the network paradigm.
One is that, according to the anatomy, the connectivity
is reciprocal between all stages of the executive hierar-
chy. The other is that the processing toward action is
not only serial, as most hierarchical models imply, but
also parallel.
At the end of the previous section, a set of empirical pre-
dictions has been presented that mainly derives from the
structural characteristics of the network paradigm discussed
in this review. I finish this section with another set of pre-
dictions deriving mainly from its dynamic characteristics:
A. Serial behaviors that require sequential decisions in-
formed by the environmental consequences of the sub-
jectʼs actions will reveal the temporal alternation of
Granger causality between frontal and posterior areas.
This alternation will be revealed not only by LFP rec-
ords but also by neuroimaging records. Because the
necessary behavioral and recording methods are best
suited to the nonhuman primate, those hypotheses
should be tested in monkeys. Neuroimaging records
will suitably be obtained by near-infrared spectroscopy
(NIRS), an optical-imaging method with less spatial res-
olution but considerably greater temporal resolution
than fMRI (Fuster et al., 2005).
B. Working memory will elicit sustained, widespread—
interregional—activation of frontal and posterior cortex,
2064 Journal of Cognitive Neuroscience Volume 21, Number 11
Figure 14. Flow of cortical
and subcortical connectivity
and processing in the
perception–action cycle.
Empty rhomboids represent
intermediate areas or subareas
of adjacent labeled regions.
Figure 15. Effective
connectivity deduced from the
fMRI activation of frontal areas
during the performance of
three levels of associative
response to a stimulus, as
described in the text. Circles
denote the approximate
locations of activation in the
regions indicated at the right of
the figure. Frontal activations
and path coefficients
significantly increasing with the
first task (stimulus alone), the
second task (stimulus and
context), and the third task
(“episode,”which includes a
prior contingency) are shown
in green, yellow, and red,
respectively. L = left; R = right;
LPFC = lateral pFC. Adapted
from Koechlin et al. (2003),
with permission.
Fuster 2065
and this will be manifest in multiple-unit, EEG, LFP, and
NIRS signals. On the assumption that the activation re-
flects reentrant excitation within and between cognits,
regions presumed to contain networks representing
the memorandum and the operant response will show
high degrees of covariance and coherence—especially
in the high-frequency range.
C.The reversible—for example, cryogenic—lesion of fron-
tal cortex will induce a deactivation of posterior cognits
and a deficit in working memory. These will be manifest
in multiple-unit and LFP records of posterior cortex and
in a concomitant behavioral memory deficit. These
manifestations of deficit will result from a dual revers-
ible effect of the procedure: (a) interruption of the
perception–action cycle at the top and (b) interruption
of reentry loops between frontal and posterior cortex
maintaining active memory.
Conclusions and Future Research
A large body of recent evidence endorses a new network
paradigm of the structure and dynamics of memory in
the cerebral cortex. That evidence suffices to establish
the paradigmʼs basic principles, which sharply distinguish
it from other models of cortical memory. According to it,
memories as well as items of knowledge consist of dis-
tributed and hierarchically organized cortical networks.
These memory networks or cognits consist of dispersed
neuron populations and the associative connections that
link them. Those connections, commonly bridging non-
contiguous areas of the cortex, are formed, enhanced,
and expanded by experience-dependent synaptic modu-
lation. Cognits overlap and interconnect extensively with-
in and between hierarchical levels (heterarchically). Thus,
a neuron or a neuron population can be part of multiple
memories or items of knowledge.
Whereas the paradigm is based on vast empirical evi-
dence, it is difficult to conceive of one single critical exper-
iment to prove it correct. Definitive proof would require
the simultaneous application to the entire cortex of analyt-
ical methods with multiple scales of spatial and temporal
resolution, which is now impractical. In the absence of
such capability, we can assume that the model is essen-
tially correct and attempt to strengthen experimentally
the principles outlined in this review. Nonetheless, one
of the merits of the paradigm is that even with current
methods, those principles can be proven wrong. Indeed,
the paradigm would be falsified if it were shown that (1)
specific memories or items or knowledge are exclusively
represented in discrete domains of cortex and not en-
dowed with experience-dependent plasticity; (2) memory
or knowledge representations are not interregional and
are deprived of hierarchical organization and nesting; (3)
anatomical relationships between cell groups are immate-
rial to the memory code; or (4) substantial numbers of
cells or cell groups in separate cortical locations are not
attuned to the associated constituent properties of a mem-
ory or an item of knowledge.
More research is needed for complete description of the
emerging paradigm in physiological and computational
terms. To that end, a number of predictions—advanced
in the previous two sections—need testing. Because of
the modelʼs distributed, associative, and integrative ar-
chitecture, special efforts must be made to explore its
constituent networks with a variety of different methods.
Especially useful will be the simultaneous recording of elec-
trical and imaging signals from multiple sites in memory
tasks. Unit discharge, LFPs, surface EEG, and NIRS should
be recorded from monkeys performing working-memory
tasks. This combination of signal-recording methods, to-
gether with advanced computational methods, should
allow the exploration of active memory networks with
varying scales and resolution. Signals of different origin
should be submitted to computational analysis in multiple
spatial and temporal scales, with emphasis on coherence,
covariance, and Granger causality. As heretofore, working-
memory tasks, with their time-bracketed active memory,
should continue to provide the ideal setting for this research.
Finally, a pressing issue is the accessibility of long-term
memory, in the active state, to consciousness. The available
evidence indicates that conscious cognition is a graded
function, largely dependent on synaptic strength and
degree of cognit activation. These inferences open new
avenues for understanding priming and preconscious phe-
nomena. Moreover, research on these issues may lead to
new procedures for memory enhancement as well as the
rehabilitation of old associative paths or the opening of
new ones.
Acknowledgments
I wish to thank Thomas Albright, Mark DʼEsposito, and Steven
Bressler for their valuable comments on earlier versions of the
manuscript.
Reprint requests should be sent to Joaquín M. Fuster, UCLA Semel
Institute, 740 Westwood Plaza, Los Angeles, CA 90095, or via
e-mail: joaquinf@ucla.edu.
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