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.
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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-
utedneurons—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