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The hippocampal system is critical for storage and retrieval of declarative memories, including memories for locations and events that take place at those locations. Spatial memories place high demands on capacity. Memories must be distinct to be recalled without interference and encoding must be fast. Recent studies have indicated that hippocampal networks allow for fast storage of large quantities of uncorrelated spatial information. The aim of the this article is to review and discuss some of this work, taking as a starting point the discovery of multiple functionally specialized cell types of the hippocampal-entorhinal circuit, such as place, grid, and border cells. We will show that grid cells provide the hippocampus with a metric, as well as a putative mechanism for decorrelation of representations, that the formation of environment-specific place maps depends on mechanisms for long-term plasticity in the hippocampus, and that long-term spatiotemporal memory storage may depend on offline consolidation processes related to sharp-wave ripple activity in the hippocampus. The multitude of representations generated through interactions between a variety of functionally specialized cell types in the entorhinal-hippocampal circuit may be at the heart of the mechanism for declarative memory formation. Copyright © 2015 Cold Spring Harbor Laboratory Press; all rights reserved.
Remapping in place cells and grid cells. (Top left) John Kubie and Bob Muller in 1983. (Top right) Colorcoded firing rate map for a hippocampal place cell from an early remapping experiment ( purple, high rate; yellow, low rate). The cell fired at different locations in different versions of the recording cylinder, one with a black cue card and one with a white cue card. (Bottom left) Realignment of entorhinal grid cells under conditions that generate global remapping in the hippocampus. The rat was tested in boxes with square or circular surfaces. The left panel shows color-coded rate maps for three grid cells (t5c2, t6c1, and t6c3) (color coded as in Fig. 1). The right panel shows cross-correlation maps for pairs of rate maps (same grid cells as in the left panel; repeated trials in A or one trial in A and one trial in B). The cross-correlation maps are color-coded, with red corresponding to high correlation and blue to low (negative) correlation. Note that the center of the cross-correlation map is shifted in the same direction and at a similar distance from the origin in all three grid cells, suggesting that all grid cells in an ensemble respond coherently to changes in the environment very much unlike the remapping that is observed in the hippocampus. (Bottom right) Response to a change in the environment (darkness) in a simultaneously recorded pair of grid and place cells. (Top left photo courteously provided by John Kubie; top right image is modified from data in Bostock et al. 1991; bottom image from Fyhn et al. 2007; reprinted, with permission, from the authors and Nature Publishing Group # 2007.)
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Place Cells, Grid Cells, and Memory
May-Britt Moser, David C. Rowland, and Edvard I. Moser
Centre for Neural Computation, Kavli Institute for Systems Neuroscience, Norwegian University of Science
and Technology, 7489 Trondheim, Norway
The hippocampal system is critical for storage and retrieval of declarative memories, includ-
ing memories for locations and events that take place at those locations. Spatial memories
place high demands on capacity. Memories must be distinct to be recalled without interfer-
ence and encoding must be fast. Recent studies have indicated that hippocampal networks
allow for fast storage of large quantities of uncorrelated spatial information. The aim of the
this article is to reviewand discuss some of this work, taking as a starting point the discovery
of multiple functionally specialized cell types of the hippocampal– entorhinal circuit, such
as place, grid, and border cells. We will show that grid cells provide the hippocampus with a
metric, as well as a putative mechanism for decorrelation of representations, that the forma-
tion of environment-specific place maps depends on mechanisms for long-term plasticity in
the hippocampus, and that long-term spatiotemporal memorystorage may depend on offline
consolidation processes related to sharp-wave ripple activity in the hippocampus. The mul-
titude of representations generated through interactions between a variety of functionally
specialized cell types in the entorhinal–hippocampal circuit may be at the heart of the
mechanism for declarative memory formation.
The scientific study of human memory started
with Herman Ebbinghaus, who initiated the
quantitative investigation of associative memo-
ry processes as they take place (Ebbinghaus
1885). Ebbinghaus described the conditions
that influence memory formation and he deter-
mined several basic principles of encoding and
recall, such as the law of frequency and the effect
of time on forgetting. With Ebbinghaus, higher
mental functions were brought to the laborato-
ry. In parallelwith the human learning tradition
that Ebbinghaus started, a new generation of
experimental psychologists described the laws
of associative learning in animals. With behav-
iorists like Pavlov, Watson, Hull, Skinner, and
Tolman, a rigorous program for identifying the
laws of animal learning was initiated. By the
middle of the 20th century, a language for asso-
ciative learning processes had been developed,
and many of the fundamental relationships be-
tween environment and behavior had been de-
scribed. What was completely missing, though,
was an understanding of the neural activity un-
derlying the formation of the memory. The be-
haviorists had deliberately shied away from
physiological explanations because of the in-
tangible nature of neural activity at that time.
Then the climate began to change. Karl
Lashley had shown that lesions in the cerebral
cortex had predictable effects on behavior in
Editors: Eric R. Kandel, Yadin Dudai, and Mark R. Mayford
Additional Perspectives on Learning and Memory available at
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animals (Lashley 1929, 1950), and Donald Hebb
introduced concepts and ideas to account for
complex brain functions at the neural circuit
level, many of which have retained a place in
modern neuroscience (Hebb 1949). Both Lash-
ley and Hebb searched for the engram, but they
found no specific locus for it. A significant turn-
ing point was reached when Scoville and Milner
(1957) reported severe loss of memory in an
epileptic patient, patient H.M., after bilateral
surgical removal of the hippocampal formation
and the surrounding medial temporal lobe ar-
eas. After operation this young man could no
longer recognize the hospital staff nor find his
way to the bathroom, and he seemed to recall
nothing of the day-to-day events of his hospital
life. (Scoville and Milner 1957). This tragic mis-
fortune inspired decades of research on the func-
tion of the hippocampus in memory. H.M.s
memory impairment could be reproduced in
memory tasks in animals and studies of H.M.,
as well as laboratory animals, pointed to a crit-
ical role for the hippocampus in declarative
memory—memory, which, in humans, can be
consciously recalled and declared, such as mem-
ories of experiences and facts (Milner et al. 1968;
Mishkin 1978; Cohen and Squire 1980; Squire
1992; Corkin 2002). What was missing from
these early studies, however, was a way to address
the neuronal mechanisms that led information
to be stored as memory.
The aim of this article is to show how studies
of hippocampal neuronal activity during the
past few decades have brought us to a point at
which a mechanistic basis of memory forma-
tion is beginning to surface. An early landmark
in this series of investigations was the discovery
of place cells, cells that fire selectively at one or
few locations in the environment. At first, these
cells seemed to be part of the animal’s instanta-
neous representation of location, independent
of memory, but gradually, over the course of
several decades, it has become clear that place
cells express current as well as past and future
locations. In many ways, place cells can be used
as readouts of the memories that are stored in
the hippocampus. More recent work has also
shown that place cells are part of a wider net-
work of spatially modulated neurons, including
grid, border, and head direction cells, each with
distinct roles in the representation of space and
spatial memory. In this article, we shall discuss
potential mechanisms by which these cell types,
particularly place and grid cells, in conjunction
with synaptic plasticity, may form the basis of a
mammalian system for fast high-capacity de-
clarative memory.
The growing interest in hippocampal function
and memory led John O’Keefe and John Dos-
trovsky (O’Keefe and Dostrovsky 1971) and Jim
Ranck (Ranck 1973) to introduce methods for
recording activity from hippocampal neurons
in awake and freely moving animals. Using min-
iaturized electrodes for extracellular single-cell
recording, they were able to show reliable links
between neural activity and behavior. The most
striking relationship was noted by O’Keefe and
Dostrovsky, who found that hippocampal cells
responded specifically to the current location of
the animal. They called these cells “place cells”
(Fig. 1). Different place cells were found to have
different firing locations, or place fields (O’Keefe
1976). Place was mapped nontopographically in
the sense that place fields of neighboring cells
were no more similar than those of cells that
were far apart (O’Keefe 1976; Wilson and Mc-
Naughton 1993), although the size of the fir-
ing fields increased from dorsal to ventral hip-
pocampus (Jung et al. 1994; Kjelstrup et al.
2008). The combination of cells that were active
at each location in the environment was unique,
despite the lack of location topography, leading
O’Keefe and Nadel (1978) to suggest that the
hippocampus is the locus of the brain’s internal
map of the spatial environment, a manifestation
of the cognitive map proposed from purely be-
havioral experiments by Edward Tolman several
decades earlier (Tolman 1948).
The discovery of place cells changed the way
many experimental neuroscientists thought
about hippocampal functions. Clinical studies
starting with patient H.M. pointed to a role
for the hippocampus in declarative memory
(Squire 1992), but the fact that hippocampal
M.-B. Moser et al.
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neurons were so strongly modulated by location
suggested that space was primary. Moreover, for
the most part, place cells represented current
space, not as expected if the function of the hip-
pocampus was purely mnemonic. Reconciling
space and memory functions remained a chal-
lenge for several decades after the discovery of
place cells.
A framework that accounts for both lines of
observation has now emerged. Converging evi-
dence has suggested that hippocampal neurons
respond also to nonspatial features of the envi-
ronment, such as odors (Eichenbaum et al.
1987; Wood et al. 1999; Igarashi et al. 2014),
tactile inputs (Young et al. 1994), and timing
(Hampson et al. 1993). The same cells that re-
spond to nonspatial stimuli fire like place cells
when animals move around in space, suggest-
ing that place cells express the location of the
animal in combination with information about
events that take place or took place there (Leut-
geb et al. 2005b; Moser et al. 2008). The repre-
sentation of space does not exclude a central role
of the hippocampus in declarative memory, as
space is a central element of all episodic and
many semantic memories (Buzsa
´ki and Moser
A role for place cells in hippocampal mem-
ory was apparent already in the earliest studies
of place cells. It was shown in these studies that
ensembles of place cells represent not only the
animal’s current location but also locations that
the animal had visited earlier. In maze tasks,
place cells fired when the animal made errors,
as if the animal was in the locationwherethe cell
fired normally (O’Keefe and Speakman 1987).
In spatial alternation tasks, firing patterns re-
flected locations that the animal came from, as
well as upcoming locations (Frank et al. 2000;
Woods et al. 2000; Ferbinteanu and Shapiro
2003), and during sequential testing in multiple
environments, place-cell activity was found to
carry over from one environment to the next
(Leutgeb et al. 2004, 2005a). Moreover, sequenc-
es of spatial firing during exploration were
shown to be replayed during rest or sleep subse-
quent to the behavioral experience, as if those
patterns were stored in the hippocampal net-
work during exploration and retrieved later in
offline mode, when the animal was not acquir-
Grid Place
Figure 1. Grid cells and place cells. (Left) A grid cell from the entorhinal cortex of the rat brain. The black trace
shows the trajectory of a foraging rat in part of a 1.5-m-diameter-wide square enclosure. Spike locations of the
grid cell are superimposed in red on the trajectory. Each red dot corresponds to one spike. Blue equilateral
triangles have been drawn on top of the spike distribution to illustrate the regular hexagonal structure ofthe grid
pattern. (Right) Grid cell and place cell. (Top) Trajectory with spike locations, as in the left part. (Bottom) Color-
coded rate map with red showing high activity and blue showing low activity. Grid cells are thought to provide
much, but not all, of the entorhinal spatial input to place cells.
Place Cells, Grid Cells, and Memory
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ing new information (Pavlides and Winson
1989; Wilson and McNaughton 1994; Foster
and Wilson 2006; O’Neill et al. 2006).
The fact that place cells express past experi-
ence raises the question whether ensembles of
place cells are completely formed by experience
or if there is an underlying component that is
hardwired in the circuit. Hill (1978) sought to
address this issue by recording place fields as
rats entered a novel environment. Of the 12 cells
that he recorded, 10 appeared to have spatial
firing fields immediately, supporting the idea
that the place-cell map was largely predeter-
mined. Subsequently, studies with larger en-
sembles of cells found that place fields often
took several minutes of exploration before set-
tling into a stable firing field (McNaughton and
Wilson 1993; Frank et al. 2004) and the forma-
tion of new and stable place fields was depen-
dent on the animal’s behavior and attention to
the spatial features of the environment (Kentros
et al. 2004; Monaco et al. 2014). These results
point to a critical role for experience in forming
the hippocampal map of space. However, the
plasticity can occur extremely rapidly (Leutgeb
et al. 2006) and, just as Hill observed, some
place cells show stable firing fields immediately
(Frank et al. 2004). Thus, place maps are ex-
pressed, in some form, from the very moment
when animals are put into an environment for
the first time, although the map may evolve fur-
ther with experience. The findings raise the pos-
sibility that a skeletal map of a novel environ-
ment is drawn from a set of preexisting maps,
and then gets modified to fit the specifics of
the environment through experience-depen-
dent plasticity (Samsonovich and McNaughton
1997; Dragoi and Tonegawa 2011, 2013).
The role of synaptic plasticity in the forma-
tion of place maps has been tested experi-
mentally. In agreement with the proposed ex-
istence of prewired maps, neither systemic
pharmacological blockade of N-methyl-D-as-
partate (NMDA) receptors, nor subfield-specif-
ic targeted knockouts of such receptors, have
a large effect on the basic firing patterns of
place cells in familiar or novel environments
(McHugh et al. 1996; Kentros et al. 1998), sug-
gesting that place-field expression is quite inde-
pendent of at least one major form of long-term
synaptic plasticity. However, cellular mecha-
nisms involved in long-term plasticity are clear-
ly required for the long-term stability of newly
formed maps (Rotenberg et al. 1996; Kentros
et al. 1998). These studies suggest that the
place-cell map of the environment is stored
and stabilized through changes in synaptic
weights, similar to other memory systems (Kan-
del and Schwartz 1982).
NMDA receptors also play a role in more
subtle forms of experience-dependent modifi-
cations of place fields. One example is the ex-
perience-dependent asymmetric expansion of
place fields observed following repeated travers-
als of place fields on a linear track (Mehta et al.
1997, 2000). It was suggested in theoretical
studies in the 1990s that as a rat moves through
locations A, B, and C along a linear track, the
cells coding for location A will repeatedly acti-
vate the cells coding for location B and the cells
coding for location B will, in turn, activate cells
coding for location C. By the logic of Hebbian
plasticity, the connections from A to B and B
to C should become strengthened, with the re-
sult that place fields of cells A, B, and C are
shifted forward on the track, against the direc-
tion of motion (Abbott and Blum 1996; Blum
and Abbott 1996). Experimental evidence for
such experience-dependent asymmetric expan-
sion was obtained by Mehta and colleagues
(1997, 2000). Subsequently, studies found that
the asymmetric shift depends on NMDA recep-
tor activation (Ekstrom et al. 2001), consistent
with the suggestion that place maps are refined
by experience-dependent long-term synaptic
What are the factors that determine whether
new place maps are stabilized? One of the hall-
marks of episodic memories is that attended
information is more likely to be encoded and
stored long term (Chun and Turk-Browne
2007). It is simply impossible to remember ev-
erything, and as Ebbinghaus’s curve of memory
shows, most memories will fade over time.
However, some particularly meaningful mem-
M.-B. Moser et al.
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ories become permanent. On this background,
Kentros et al. (2004) considered whether atten-
tion to spatial cues could improve the long-term
stability of place fields. They trained mice to
find an unmarked goal location in a cylinder
(similar to the Morris water maze) while re-
cording hippocampal place cells. The mice that
learned the task had more stable place fields
than mice that were simply running in the same
cylinder with no task requirements. To test
whether the driving force was true selective at-
tention, as opposed to general arousal, Muzzio
et al. (2009) trained mice to attend to odor cues
and ignore spatial cues or vice versa. When the
odors were the relevant cues, the hippocampal
neurons acquired stable odor representations,
but had less stable spatial representations. The
reverse was true when space was relevant. Taken
together with recent evidence suggesting that
place fields can be induced by attentive scann-
ing (Monaco et al. 2014), the findings point to
selective attention, and not merely general
arousal, as a major determinant of experience-
dependent stabilization of hippocampal place
What could be the mechanisms for selective
attention in the hippocampus? Recently, Igara-
shi et al. (2014) recorded simultaneously from
the lateral entorhinal cortex and CA1 region of
the rat hippocampus as the animals learned an
odor–place association. As the animals learned
the association, the two structures showed an
increasing degree of synchronous oscillatory ac-
tivity in the 20- to 40-Hz range and a corre-
sponding increase in spiking activity to the re-
warded odors. The development of temporal
coherence between activity in the hippocampus
and entorhinal cortex may allow CA1 cells to
respond to particular entorhinal inputs at the
same time as the cells are closest to firing thresh-
old (Singer 1993). The 20- to 40-Hz oscillation
is substantially lower than the fast (60-100 Hz)
gamma oscillation found in the medial entorhi-
nal cortex (Colgin et al. 2009). The two subdi-
visions of the entorhinal cortex may, therefore,
convey relevant information to the hippo-
campus via distinct frequency channels, each
leading to a different firing pattern in the hip-
Once encoded, the memories must be consoli-
dated. In an early theoretical paper, Buzsa
(1989) proposed that hippocampal memory
formation occurs in two stages. First, there is a
stage in which memory is encoded via weak syn-
aptic potentiation in the CA3 network when the
network is in theta-oscillation mode during ex-
ploratory behavior. Then, there is a memory
consolidation stage, which can take place hours
later during sharp-wave activity, associated with
sleep and resting. In this stage, synapses that were
weakly potentiated during the preceding explo-
ration participate in sharp-wave activity that, in
turn, evokes ripple activity in the CA1 area of the
hippocampus. Ripples occur at a frequency that
is optimal for induction of long-term potentia-
tion (LTP) in efferent synapses of CA1 cells, pos-
sibly including long-distance targets in the cor-
tex. By this mechanism, memory was thought to
be slowly induced in the neocortex, consistent
with a large body of evidence pointing to gradual
recruitment of neocortical memory circuits in
long-term storage of hippocampal memories
(McClelland et al. 1995; Squire and Alvarez
1995; Frankland et al. 2001). Over the years,
considerable evidence has accumulated to point
to a role for sharp waves and ripples in the for-
mation of hippocampus-dependent long-term
memories. Selectively disrupting sharp-wave
ripple activity during posttraining rest periods
impairs learning, providing a causal link be-
tween sharp-wave ripples and consolidation
(Girardeau et al. 2009; Ego-Stengel and Wilson
2010). Moreover,it is now clear that sequences of
firing among place cells are replayed during sub-
sequent sharp-wave ripples in the same or re-
verse order that the cells were active during ex-
perience (Wilson and McNaughton 1994; Foster
and Wilson 2006; Diba and Buzsa
´ki 2007).
Structured replay is seen across many brain re-
gions (Hoffman and McNaughton 2002), indi-
cating that the sequence information from the
hippocampus may be conferred on downstream
cortical targets.
Recent work points to awider role for replay
in which replay may contribute not only to con-
Place Cells, Grid Cells, and Memory
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solidation and recall of memory, but also to
planning of future behavior. Studies in human
subjects show that overlapping hippocampal
networks are activated during episodic recall
and imagination of fictitious experiences (Has-
sabis et al. 2007). In animals, sharp-wave ripples
can activate cells along both past and future
trajectories (Karlsson and Frank 2009; Gupta
et al. 2010; Pfeiffer and Foster 2013). Pfeiffer
and Foster (2013), for example, trained rats to
find a rewarded well within a large environment
while sharp-wave ripple-associated replay events
were recorded in the hippocampus. In many of
the events, the sequence of active cells began at
the current location and ended at the goal loca-
tion, followed by the animal taking the path
defined by the place-cell activity. Although the
sequence of activated cells clearly preceded be-
havior, the phenomenon also depended on pre-
vious experience with the environment and the
rules of the task. Thus, the replay can either lead
or follow the behavior once the map of space is
established. In that sense, the replay phenome-
non may support “mental time travel” (Sudden-
dorf and Corballis 2007) through the spatial
map, both forward and backward in time.
Whether the sharp-wave ripple-mediated replay
in rats represents conscious recall is impossible
to know, but observations in humans during
free recall provide a clue (Gelbard-Sagiv et al.
2008; Miller et al. 2013). Miller et al. (2013), for
example, recorded from the medial temporal
lobe of human subjects as they navigated a vir-
tual town (the subjects were awaiting surgery
for epilepsy and had electrodes placed in their
medial temporal lobe to localize the origin of
the seizures, affording Miller et al. the rare op-
portunity to record place cells in humans). After
an initial familiarization period, subjects were
asked to deliver items to one of the stores in the
town and when all the deliveries were complete,
the subjects were asked to recall only the items
they delivered. Remarkably, the place cells re-
sponsive to the area where the item was deliv-
ered became active during recall of the item,
closely mirroring the reactivation of place cells
during replay events in rodents. Although free
recall in humans is not likely to correspond to
sharp-wave ripple events (Watrous et al. 2013),
the time course of reactivation was similar to a
typical sharp-wave ripple event in rodents, and
may therefore reflect a qualitatively similar phe-
nomenon. The place cell activity during recall of
events or items likely brings to mind the spatial
context in which the events and items were ex-
perienced, creating a fully reconstructed mem-
ory for what was experienced, along with where
it was experienced.
To get a better insight into the mechanisms of
memory formation in hippocampal place-cell
circuits, it may pay off to consider how place
cells interact with cells in adjacent brain systems.
The origin of the place-cell signal was long
thought to be intrahippocampal, considering
that early recordings upstream in the entorhinal
cortex showed only weak spatial modulation
(Barnes et al. 1990; Quirk et al. 1992; Frank
et al. 2000). At the turn of the millennium, we
started a series of experiments aimed at localiz-
ing the sources of the place signal. First, we iso-
lated the CA1 region of the hippocampus from
the earlier parts of the hippocampal excitatory
circuit, that is, the dentate gyrus and the CA3
(Brun et al. 2002). Activity was then recorded
from the remaining CA1. Place cells were still
present, suggesting that intrahippocampal cir-
cuits are not necessary for spatial signals to de-
velop. The findings pointed to direct inputs
from the entorhinal cortex as an alternative
source of incoming spatial information to the
hippocampus. Thus, in a subsequent study, we
recorded directly from the entorhinal cortex,
not in the deep ventral areas where cells had
been recorded in previous studies, but in the
dorsal parts that projected directly to the hip-
pocampal recording locations used by O’Keefe
and others (Fyhn et al. 2004). Electrodes were
placed in the medial part of the entorhinal cor-
tex. We found that many neurons in this area
were as sharply modulated by position as place
cells in the hippocampus. Entorhinal neurons
had multiple firing fields with clear regions of
silence between the fields. In a third study, we
expanded the size of the recording environment
M.-B. Moser et al.
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to determine the spatial structure of the many
firing fields (Hafting et al. 2005). The multiple
firing fields of individual entorhinal neurons
formed a regularly spaced triangular or hexag-
onal grid pattern, which repeated itself across
the entire available space. We named these cells
“grid cells. Grid cells were organized in a non-
topographic manner, much like place cells. The
firing fields of neighboring grid cells were no
more similar than those of grid cells recorded
at different brain locations. However, the scale
of the grid increased from dorsal to ventral me-
dial entorhinal cortex (Fyhn et al. 2004; Hafting
et al. 2005), suggesting that the earliest record-
ings in the entorhinal cortex had missed the grid
pattern because the period of the firing pattern
was too large for repeated fields to be observed in
conventionally sized recording boxes. The dis-
coveryof grid cells was followed by studies show-
ing that these cells were part of a wider spatial
network comprising other cell types as well,
such as head direction–modulated cells (Sargo-
lini et al. 2006) and cells that fire specifically along
one or several borders of the local environment
(border cells) (Savelli et al. 2008; Solstad et al.
2008). Head direction cells had previously been
observed in a number of brain systems, from the
dorsal tegmental nucleus in the brain stem to
the pre- and parasubiculum in the parahippo-
campal cortex (Ranck 1985; Taube et al. 1990;
Taube 2007). Border cells were described at the
same time in the subiculum (Barry et al. 2006;
Lever et al. 2009). Thus, by the end of the first
decade of the new millennium, it was clear that
place and grid cells were part of a diverse and
entangled network of cell types with distinct
functions in spatial representation.
How place cells are formed from the diver-
sity of cell types remains to be determined. An
obvious possibility is that place cells are gener-
ated by transformation of spatial input from
grid cells. The presence of grid cells in the super-
ficial layers of the entorhinal cortex, the main
cortical input to the hippocampus, led investi-
gators to propose that place fields form by linear
combination of periodic firing fields from grid
cells with a common central peak, but different
grid spacing and orientation (O’Keefe and Bur-
gess 2005; Fuhs and Touretzky 2006; McNaugh-
ton et al. 2006; Solstad et al. 2006). The sugges-
tion was that, because the wavelength of the
individual grid patterns is different, the patterns
cancel each other except at the central peak,
which becomes the place field of the receiving
cell (Fig. 3).
Experimental observations have suggested
that the mechanisms are more complex, howev-
er. If place cells were generated exclusively from
grid cells, grid and place cells would be expected
to appear simultaneously in developing animals
or with a faster time course for grid cells than
place cells. Recordings from rat pups suggest
that this is not the case (Langston et al. 2010;
Wills et al. 2010). When pups leave the nest for
the first time at 2–2.5 weeks of age, sharp and
confined firing fields are present in a large pro-
portion of the hippocampal pyramidal-cell
population. In contrast, grid cells show only
weakly periodic fields at that age. Strong peri-
odicity is not expressed until 3–4 weeks of age.
The delayed maturation of the grid cells offers at
least two interpretations. First, weak spatial in-
puts may be sufficient for place-cell formation.
Sharply confined firing fields may be generated
by local mechanisms in the hippocampal net-
work, such as recurrent inhibition (de Almeida
et al. 2009; Monaco and Abbott 2011), Hebbian
plasticity (Rolls et al. 2006; Savelli and Knierim
2010), or active dendritic properties (Smith et
al. 2013). Alternatively, place cells may be gen-
erated from other classes of spatially modulated
cells, such as border cells, which have adult-like
properties from the very first day of exploration
outside the nest (Bjerknes et al. 2014). Retro-
grade labeling studies suggest that border cells
have projections to the hippocampus that may
be equally dense as those from grid cells, al-
though the latter are more abundant (Zhang
et al. 2013). A potential role for border cells in
place-cell formation would be consistent with
early models, suggesting that place cells arise by
linear combination of inputs from cells with
firing fields defined by their proximity to geo-
metric boundaries (O’Keefe and Burgess 1996;
Hartley et al. 2000). Recordings in the medial
entorhinal cortex have, so far, identified such
cells only near the boundaries of the environ-
ment (Solstad et al. 2008; Zhang et al. 2013;
Place Cells, Grid Cells, and Memory
Advanced Online Article. Cite this article as Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a021808 7
Bjerknes et al. 2014), suggesting that a contri-
bution by these cells may be limited to place
cells with peripheral firing fields.
The exact function of different entorhinal
cell types in place-cell formation remains to be
determined, but it is not unlikely that individual
place cells receive inputs from both grid and
border cells, possibly with grid cells providing
self-motion-based distance information and
border cells providing position in relation to
geometric boundaries (Bush et al. 2014; Zhang
et al. 2014). The strongest input may originate
from grid cells, which, in the superficial layers of
the medial entorhinal cortex, are several times
more abundant than border cells (Sarg olini et al.
2006; Solstad et al. 2008; Boccara et al. 2010).
Under most circumstances, the two classes of
input are likely to be coherent and redundant.
If one is absent, the other may oftenbe sufficient
to generate localized firing in the hippocampus.
One of the events that pointed to place cells as an
expression of declarative memory was the dis-
covery of remapping, or the fact that any place
cell is part of not one, but many independent
representations. In 1987, Bob Muller and John
Kubie found that place cells can alter their firing
patterns in response to minor changes in the
experimental task, such as alterations in the
shape of the recording enclosure (Fig. 3) (Muller
and Kubie 1987; Bostock et al. 1991). Place cells
may begin firing, stop firing, or change their
firing location. The changes are expressedw idely
across the place-cell population, such that a new
map is installed for each occasion. Remapping
could also be induced by changes in motivation-
al state or behavioral context (Markus et al.
1995; Frank et al. 2000; Wood et al. 2000; Moita
et al. 2004).
White WhiteBlack Black
t6c1 t6c1
1 m
1 m
21 Hz
11 Hz
14 Hz 12 Hz 14 Hz
12 Hz 22 Hz
18 Hz 27 Hz
AAB Rat 11554
10 Hz
22 Hz
Spatial correlation
Time (min)
4 5 6 7 8 9 10 11
2 Hz
1 Hz
Figure 2. Schematic illustration of how periodic grid cells could be transformed to nonperiodic place cells by
linear summation of output from grid cells with overlapping firing fields, but different spacing and orientation,
and how differential responses among modules of grid cells might give rise to remapping in the hippocampus.
(Left) Map 1, grid cells with different spacing converge to generate place cells in a subset of the hippocampal
place-cell population. Each grid cell belongs to a different grid module. (Right) Map 2, differential realignment
of each of the grid maps induces recruitment of a new subset of place cells. (From images in Solstad et al. 2006
and Fyhn et al. 2007; modified, with permission, from the authors and Nature Publishing Group #2006 and
2007, respectively.)
M.-B. Moser et al.
8Advanced Online Article. Cite this article as Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a021808
This is
for fig 3.
The remapping experiments showed that
place cells participate in multiple spatial maps.
Different maps could be recruited not only in
different environments, but also when animals
are tested under different conditions in the same
location (Markus et al. 1995; Leutgeb et al.
2005b). Maps for different conditions or places
were often completely uncorrelated (global re-
mapping) (Leutgeb et al. 2004; Fyhn et al. 2007),
as if a pattern-separation process takes place
when information enters the hippocampus
from the surrounding cortex (Marr 1971; Mc-
Naughton and Morris 1987; Leutgeb et al. 2004,
2007). The discovery of remapping and the un-
correlated nature of place maps was important
because it showed that place cells participate in
multiple orthogonal representations, as expect-
ed if the hippocampus plays a role in accurate
storage and retrieval of high-capacity declarative
memory. The numberof place maps stored in the
hippocampus is not known, but if place maps are
expressions of individual memories, that num-
ber should be very large. Remapping is, thus, a
necessity if place cells express memories.
Do spatial inputs from medial entorhinal
cortex contribute to remapping in the hippo-
Map #1 Map #2
Figure 3. Remapping in placecells and grid cells. (Top left) John Kubie and Bob Muller in 1983. (Top right) Color-
coded firing rate map for a hippocampal place cell from an early remapping experiment ( purple, high rate;
yellow, low rate). The cell fired at different locations in different versions of the recording cylinder, one with a
black cue card and one with a white cue card. (Bottom left) Realignment of entorhinal grid cells under conditions
that generate global remapping in the hippocampus. The rat was tested in boxes with square orcircular surfaces.
The left panel shows color-coded rate maps for three grid cells (t5c2, t6c1, and t6c3) (color coded as in Fig. 1).
The right panel shows cross-correlation maps for pairs of rate maps (same grid cells as in the left panel; repeated
trials in Aor one trial in Aand one trial in B). The cross-correlation maps are color-coded, with red corre-
sponding to high correlation and blue to low (negative) correlation. Note that the center of the cross-correlation
map is shifted in the same direction and at a similar distancefrom the origin in all three grid cells, suggesting that
all grid cells in an ensemble respond coherently to changes in the environment very much unlike the remapping
that is observed in the hippocampus. (Bottom right) Response to a change in the environment (darkness) in a
simultaneously recorded pair of grid and place cells. (Top left photo courteously provided by John Kubie; top
right image is modified from data in Bostock et al. 1991; bottom image from Fyhn et al. 2007; reprinted, with
permission, from the authors and Nature Publishing Group #2007.)
Place Cells, Grid Cells, and Memory
Advanced Online Article. Cite this article as Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a021808 9
this is the
for figure
campus? The first clue to the underlying mech-
anism is that remapping is unique to the hip-
pocampus. The orthogonal nature of place-cell
maps is not shared by any of the known spatial
cell types upstream of the hippocampus. In the
hippocampus, and particularly in the CA3 sub-
field, different subsets of the place-cell popula-
tion are active in different environments. The
overlap between active subsets in two environ-
ments is not larger than expected by chance
(Leutgeb et al. 2004). The apparent indepen-
dence of the place-cell maps contrasts with the
functional rigidity of the grid-cell population.
Changes in the environment, which lead to
global remapping in the hippocampus, induce
changes in the firing locations of simultane-
ously recorded grid cells, but these changes are
always coherent among numbers of grid cells
(Fyhn et al. 2007). Among grid cells with similar
grid spacing, the firing locations of the grid cells
shift in the xy plane from one environment to
the other, but the distance and direction of grid
displacements are similar across the cell popu-
lation. Similarly, internal coherence is observed
in head direction and border cells. When ani-
mals are moved from one task to another, head
direction cells in the presubiculum and anterior
nuclei of the thalamus rotate coherently such
that the magnitude of the difference in direc-
tional preference among any pair of head direc-
tion cells is retained from one condition to the
next (Taube et al. 1990; Taube and Burton 1995;
Yoganarasimha et al. 2006). A similar spatial
coherence is seen among border cells (Solstad
et al. 2008). Pairs of cells that fire along the same
wall in one environment also fire along the same
wall in another environment; cells that fire along
opposite walls in one box fire along opposite
walls in another box. Changes in orientation
are coherent also across entorhinal cell types;
if border fields switch to the opposite wall,
this is accompanied by a 180-degree change in
the orientation of head direction cells, as well as
grid cells (Solstad et al. 2008). Taken together,
these observations suggest that remapping is
generated not in the entorhinal cortex, but in
the hippocampus itself.
The findings do not rule out, however, that
inputs from realigned or reoriented entorhinal
cells give rise to remapping in the hippocampus.
Two classes of explanations were put forward
when we observed that remapping in the hip-
pocampus is accompanied by coherent realign-
ment in the grid-cell population (Fyhn et al.
2007). The first class assumed a continuous
map of space in the medial entorhinal cortex.
In this scenario, different portions of a universal
entorhinal map would be activated in different
environments. Different subsets of hippocam-
pal cells would be activated from independent
portions of the entorhinal map and global re-
mapping would be seen in the hippocampus.
The second class of explanation assumes that
grid cells have a modular organization and that
different modules of grid cells respond indepen-
dently to changes in the environment. Place
cells were thought to receive input from several
modules. Differential realignment across mod-
ules would lead to different overlap of incoming
grid signals in hippocampal target cells; the sub-
set of hippocampal cells activated by entorhinal
grid-cell inputs would be entirely dependent on
the difference in realignment between different
Subsequently, experimental studies have
provided evidence for a modular organization
of grid cells, consistent with the second expla-
nation (Stensola et al. 2012). For many years,
the low number of simultaneously recorded
grid cells prevented a clear answer to the ques-
tion of whether grid cells were modular or not,
although early studies pointed in that direction
(Barry et al. 2007). With a more than 10-fold
increase in the number of grid cells from the
same animal, it was possible to show that grid
cells cluster into modules with distinct grid scale
and grid orientation (Stensola et al. 2012). Four
modules could be detected in most animals, but
the number may be larger, considering that only
a part of the medial entorhinal cortex was sam-
pled. It was not only the properties of the grid
pattern that differed between modules, howev-
er; they also responded independently to chang-
es in the environment (Stensola et al. 2012).
When the recording environment was com-
pressed, changing it from a square to a rectan-
gle, grid cells in the module with the smallest
grid spacing maintained their firing locations,
M.-B. Moser et al.
10 Advanced Online Article. Cite this article as Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a021808
(Fig. 2)
whereas cells in the larger modules rescaled
completely and consistently, firing at shorter
spatial wavelengths in the compressed direction,
but maintaining wavelengths in the orthogonal
unaltered direction. The apparent indepen-
dence between grid modules contrasts with the
strong coherence observed in earlier recordings
from grid cells (Fyhn et al. 2007). The difference
is likely to reflect the fact that the earlier record-
ings were all made from the same location and,
probably, mostly from a single module.
The new data suggest that modules respond
with different degrees of displacement and re-
orientation when animals move from one envi-
ronment to another. Computational simula-
tions have shown that independent realignment
in four or fewer modules is sufficient to generate
complete or global remapping in the hippo-
campus (Monaco and Abbott 2011). Indepen-
dent responses among only a handful of grid
modules may be sufficient to create an enor-
mous diversity of firing patterns in the hippo-
campus because the number of displacements or
phases that each module may take is large. The
mechanism would be similar to that of a com-
bination lock in which 10,000 combinations
may be generated with only four modules of
10 possible values each (Rowland and Moser
2014), or that of an alphabet in which all words
of a language can be generated by combining
only 30 letters or less. The proposed mechanism
is only a hypothesis, however. Whether hippo-
campal remapping actually requires indepen-
dent realignment among grid modules remains
to be determined. It should also be noted that a
possible connection between grid modules and
remapping does not rule out roles for other cell
types, such as border cells, in inducing hippo-
campal remapping, although modular organi-
zation has not yet been observed in any of the
other functional cell populations (Giocomo
et al. 2014).
Finally, we would like to emphasize that, up
to this point, we have mostly discussed the en-
torhinal–hippocampal space circuit as if inter-
actions between cell types were constant over
time. However, the connectivity of this network
is dynamic (Buzsa
´ki and Moser 2013). Whether
entorhinal and hippocampal neurons influence
each other depends strongly on the state of theta
and gamma oscillations, which, during active
awake behavior, predominates frequency spec-
tra in both regions (Buzsa
´ki et al. 1983; Bragin
et al. 1995; Chrobak and Buzsa
´ki 1998; Csicsvari
et al. 2003; Colgin et al. 2009). Theta oscillations
are generally coherent across most of the ento-
rhinal–hippocampal network, but the coher-
ence of beta and gamma oscillations is more
local and fluctuates at subsecond timescales
(Colgin et al. 2009; Igarashi et al. 2014). Such
fluctuations may enable place cells to interact
with different entorhinal subpopulations at dif-
ferent times. Coincidence of pre- and postsyn-
aptic activity may be a prerequisite not only for
synaptic strengthening of connections between
entorhinal and hippocampal cell pairs (Singer
1993; Bi and Poo 1998), but also for pattern-
completion processes during retrieval of al-
ready-stored information. Whether a place cell
responds to inputs from grid or border cells may
change with time, as may the influence of dif-
ferent modules of grid cells. Recordings from
CA1 and lateral entorhinal cortex suggest that
place cells also respond dynamically to nonspa-
tial inputs, such as odors, with learned relation-
ships to locations in the environment (Igarashi
et al. 2014). Beta and gamma oscillations may
enable place cells to respond temporarily to in-
formation about the content of locations in the
spatial environment.
We have known for almost six decades that cer-
tain types of memory depend on the hippocam-
pus and surrounding areas. The discovery of
place cells showed that space is a critical element
of the information that is stored and expressed
by neurons in the hippocampus; however, it is,
perhaps, with studies of place cells at the ensem-
ble or population level and interventions that
selectively change synaptic plasticity in specific
brain circuits, that the mechanisms of memory
processing have become accessible. Today, we
know that hippocampal networks can rapidly
store a multitude of uncorrelated representa-
tions, a property that any high-capacity episod-
ic memory network must have. We know that
Place Cells, Grid Cells, and Memory
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place cells are only one element of a wider net-
work for spatial mapping. Place cells coexist
with grid, head direction, and border cells, all
likely to interact with each other to yield a global
representation of the animal’s changing posi-
tion, which may be used to guide the animal
to particular locations in the environment.
With a modular organization of grid cells, the
network may be able to generate not only one
map of the external environment, but thou-
sands or millions. Whether and how these
maps contribute to declarative memory remains
to be determined, but the investigation of the
hippocampal–entorhinal circuit is now at a
stage in which the computational mechanisms
underlying specific memory processes are fully
Abbott LF, Blum KI. 1996. Functional significance of long-
term potentiation for sequence learning and prediction.
Cereb Cortex 6: 406– 416.
Barnes CA, McNaughton BL, Mizumori SJ, Leonard BW,
Lin LH. 1990. Comparison of spatial and temporal char-
acteristics of neuronal activity in sequential stages of hip-
pocampal processing. Prog Brain Res 83: 287– 300.
Barry C, Lever C, Hayman R, Hartley T, Burton S, O’Keefe J,
Jeffery K, Burgess N. 2006. The boundary vector corpcell
model of place cell firing and spatial memory. Rev Neuro-
sci 17: 71–97.
Barry C, Hayman R, Burgess N, Jeffery KJ.2007. Experience-
dependent rescaling of entorhinal grids. Nat Neurosci 10:
682– 684.
Bi GQ, Poo MM. 1998. Synaptic modifications in cultured
hippocampal neurons: Dependence on spike timing, syn-
aptic strength, and postsynaptic cell type. J Neurosci 18:
10464– 10472.
Bjerknes TL, Moser EI, Moser MB. 2014. Representation of
geometric borders in the developing rat. Neuron 82: 71–
Blum KI, Abbott LF. 1996. A model of spatial map formation
in the hippocampus of the rat. Neural Comput 8: 85– 93.
Boccara CN, Sargolini F, Thoresen VH, Solstad T, WitterMP,
Moser EI, Moser M-B. 2010. Grid cells in pre- and para-
subiculum. Nat Neurosci 13: 987–994.
Bostock E, Muller RU, Kubie JL. 1991. Experience-depen-
dent modifications of hippocampal place cell firing. Hip-
pocampus 1: 193– 205.
Bragin A, Jando
´G, Na
´dasdy Z, Hetke J, Wise K, Buzsa
´ki G.
1995. Gamma (40 –100 Hz) oscillation in the hippocam-
pus of the behaving rat. J Neurosci 15: 47–60.
Brun VH, Otnass MK, Molden S, Steffenach HA, WitterMP,
Moser MB, Moser EI. 2002. Place cells and place recog-
nition maintained by direct entorhinal– hippocampal
circuitry. Science 296: 2243–2246.
Bush D, Barry C, Burgess N. 2014. What do grid cells con-
tribute to place cell firing? Trends Neurosci 37: 136–145.
´ki G. 1989. Two-stage model of memory trace forma-
tion: A role for “noisy” brain states. Neuroscience 31:
´ki G, Moser EI. 2013. Memory, navigation and theta
rhythm in the hippocampal-entorhinal system. Nat Neu-
rosci 16: 130– 138.
´ki G, Leung LW, Vanderwolf CH. 1983. Cellular bases
of hippocampal EEG in the behaving rat. Brain Res 287:
Chrobak JJ, Buzsa
´ki G. 1998. Gamma oscillations in the
entorhinal cortex of the freely behaving rat. J Neurosci
18: 388– 398.
Chun MM, Turk-Browne NB. 2007. Interactions between
attention and memory.Curr Opin Neurobiol 17: 177–184.
Cohen NJ, Squire LR. 1980. Preserved learning and reten-
tion of pattern analyzing skill in amnesia: Dissociation of
knowing how and knowing that. Science 210: 207– 209.
Colgin LL, Denninger T, Fyhn M, Hafting T, Bonnevie T,
Jensen O, Moser MB, Moser EI. 2009. Frequency of gam-
ma oscillations routes flow of information in the hippo-
campus. Nature 462: 353– 357.
Corkin S. 2002. What’s new with the amnesic patient H.M.?
Nature Rev Neurosci 3: 153– 160.
Csicsvari J, Jamieson B, Wise KD, Buzsa
´ki G. 2003. Mecha-
nisms of gamma oscillations in the hippocampus of the
behaving rat. Neuron 37: 311– 322.
de Almeida L, Idiart M, Lisman JE. 2009. The input –output
transformation of the hippocampal granule cells: From
grid cells to place fields. J Neurosci 29: 7504– 7512.
Diba K, Buzsaki G. 2007. Forward and reverse hippocampal
place-cell sequences during ripples. Nat Neurosci 10:
Dragoi G, Tonegawa S. 2011. Preplay of future place cell
sequences by hippocampal cellular assemblies. Nature
469: 397– 401.
Dragoi G, Tonegawa S. 2013. Distinct preplay of multiple
novel spatial experiences in the rat. Proc Natl Acad Sci
110: 9100– 9105.
Ebbinghaus H. 1885. U
¨ber das Geda
¨chtnis Untersuchungen
zur Experimentellen Psychologie [Memory: A contribution
to experimental psychology]. von Duncker and Humber,
Leipzig, Germany.
Ego-Stengel V, Wilson MA. 2010. Disruption of ripple-
associated hippocampal activity during rest impairs spa-
tial learning in the rat. Hippocampus 20: 1– 10.
Eichenbaum H, Kuperstein M, Fagan A, Nagode J. 1987.
Cue-sampling and goal-approach correlates of hippo-
campal unit activity in rats performing an odor-discrim-
ination task. J Neurosci 7: 716– 732.
Ekstrom AD, Meltzer J, McNaughton BL, Barnes CA. 2001.
NMDA receptor antagonism blocks experience-depen-
dent expansion of hippocampal “place fields. Neuron
31: 631– 638.
Ferbinteanu J, ShapiroML. 2003. Prospective and retrospec-
tive memory coding in the hippocampus. Neuron 40:
M.-B. Moser et al.
12 Advanced Online Article. Cite this article as Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a021808
Foster DJ, Wilson MA. 2006. Reverse replay of behavioural
sequences in hippocampal place cells during the awake
state. Nature 440: 680– 683.
Frank LM, Brown EN, Wilson M. 2000. Trajectory encoding
in the hippocampus and entorhinal cortex. Neuron 27:
169– 178.
Frank LM, Stanley GB, BrownEN. 2004. Hippocampal plas-
ticity across multiple days of exposure to novel environ-
ments. J Neurosci 24: 7681– 7689.
Frankland PW, O’Brien C, Ohno M, Kirkwood A, Silva AJ.
2001. a-CaMKII-dependent plasticity in the cortex is
required for permanent memory. Nature 411: 309–313.
Fuhs MC, Touretzky DS. 2006. A spin glass model of path
integration in rat medial entorhinal cortex. J Neurosci 26:
4266– 4276.
Fyhn M, Molden S, Witter MP, Moser EI, Moser M-B. 2004.
Spatial representation in the entorhinal cortex. Science
305: 1258–1264.
Fyhn M, Hafting T, Treves A, Moser M-B, Moser EI. 2007.
Hippocampal remapping and grid realignment in ento-
rhinal cortex. Nature 446: 190–194.
Gelbard-Sagiv H, Mukamel R, Harel M, Malach R, Fried I.
2008. Internally generated reactivation of single neurons
in human hippocampus during free recall. Science 322:
Giocomo LM, Stensola T, Bonnevie T, Van Cauter T, Moser
M-B, Moser EI. 2014. Topography of head direction cells
in medial entorhinal cortex. Curr Biol 24: 252– 262.
Girardeau G, Benchenane K, Wiener SI, Buzsaki G, Zugaro
MB. 2009. Selective suppression of hippocampal ripples
impairs spatial memory. Nat Neurosci 12: 1222– 1223.
Gupta AS, van der Meer MA, Touretzky DS, Redish AD.
2010. Hippocampal replay is not a simple function of
experience. Neuron 65: 695– 705.
Hafting T, Fyhn M, Molden S, Moser M-B, Moser EI. 2005.
Microstructure of a spatial map in the entorhinal cortex.
Nature 436: 801–806.
Hampson RE, Heyser CJ, Deadwyler SA. 1993. Hippocam-
pal cell firing correlates of delayed-match-to-sample per-
formance in the rat. Behav Neurosci 107: 715– 739.
Hartley T, Burgess N, Lever C, Cacucci F, O’Keefe J. 2000.
Modeling place fields in terms of the cortical inputs to the
hippocampus. Hippocampus 10: 369– 379.
Hassabis D, Kumaran D, Maguire EA. 2007. Using imagina-
tion to understand the neural basis of episodic memory. J
Neurosci 27: 14365– 14374.
Hebb DO. 1949. The organization of behavior. Wiley, New
Hill AJ. 1978. First occurrence of hippocampal spatial firing
in a new environment. Exp Neurol 62: 282– 297.
Hoffman KL, McNaughton BL. 2002. Coordinated reacti-
vation of distributed memory traces in primate neocor-
tex. Science 297: 2070– 2073.
Igarashi KM, Lu L, Colgin LL, Moser M-B, Moser EI. 2014.
Coordination of entorhinal– hippocampal ensemble ac-
tivity during associative learning. Nature 510: 143– 147.
Jung MW, Wiener SI, McNaughton BL. 1994. Comparison
of spatial firing characteristics of units in dorsal and ven-
tral hippocampus of the rat. J Neurosci 14: 7347–7356.
Kandel ER, Schwartz JH. 1982. Molecular biology of learn-
ing: Modulation of transmitter release. Science 218: 433
Karlsson MP, Frank LM. 2009. Awake replay of remote ex-
periences in the hippocampus. Nat Neurosci 12: 913
Kentros C, Hargreaves E, Hawkins RD, Kandel ER, Shapiro
M, Muller RV. 1998. Abolition of long-term stability of
new hippocampal place cell maps by NMDA receptor
blockade. Science 280: 2121–2126.
Kentros CG, Agnihotri NT, Streater S, Hawkins RD, Kandel
ER. 2004. Increased attention to spatial context increases
both place field stability and spatial memory. Neuron 42:
Kjelstrup KB, Solstad T, Brun VH, Hafting T, Leutgeb S,
Witter MP, Moser EI, Moser M-B. 2008. Finite scales of
spatial representation in the hippocampus. Science 321:
Langston RF, Ainge JA, Couey JJ, Canto CB, Bjerknes TL,
Witter MP, Moser EI, Moser M-B. 2010. Development of
the spatial representation system in the rat. Science 328:
Lashley KS. 1929. Brain mechanisms and intelligence: A qual-
itative study of injuries to the brain. University of Chicago
Press, Chicago.
Lashley KS. 1950. In search of the engram. In Symposium of
the society for experimental biology, Vol. 4. Cambridge
University Press, New York.
Leutgeb S, Leutgeb JK, Treves A, Moser M-B, Moser EI.
2004. Distinct ensemble codes in hippocampal areas
CA3 and CA1. Science 305: 1295– 1298.
Leutgeb JK, Leutgeb S, Treves A, Meyer R, Barnes CA, Mc-
Naughton BL, Moser M-B, Moser EI. 2005a. Progressive
transformation of hippocampal neuronalrepresentations
in “morphed” environments. Neuron 48: 345–358.
Leutgeb S, Leutgeb JK, Barnes CA, Moser EI, McNaughton
BL, Moser M-B. 2005b. Independent codes for spatial
and episodic memory in hippocampal neuronal ensem-
bles. Science 309: 619–623.
Leutgeb S, Leutgeb JK, Moser EI, Moser MB. 2006. Fast rate
coding in hippocampal CA3 cell ensembles. Hippocam-
pus 16: 765– 774.
Leutgeb JK, Leutgeb S, Moser MB, Moser EI. 2007. Pattern
separation in the dentate gyrus and CA3 of the hippo-
campus. Science 315: 961–966.
Lever C, Burton S, Jeewajee A, O’Keefe J, Burgess N. 2009.
Boundary vector cells in the subiculum of the hippocam-
pal formation. J Neurosci 29: 9771– 9777.
Markus EJ, Qin YL, Leonard B, Skaggs WE, McNaughton
BL, Barnes CA. 1995. Interactions between location and
task affect the spatial and directional firing of hippocam-
pal neurons. J Neurosci 15: 7079–7094.
Marr D. 1971. Simple memory: A theory for archicortex.
Philos Trans R Soc Lond B Biol Sci 262: 23– 81.
McClelland JL, McNaughton BL, O’Reilly RC. 1995. Why
there are complementary learning systems in the hippo-
campus and neocortex: Insights from the successes and
failures of connectionist models of learning and memory.
Psychol Rev 102: 419– 457.
McHugh TJ, Blum KI, Tsien JZ, Tonegawa S, Wilson MA.
1996. Impaired hippocampal representation of space in
Place Cells, Grid Cells, and Memory
Advanced Online Article. Cite this article as Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a021808 13
CA1-specific NMDAR1 knockout mice. Cell 87: 1339–
McNaughton BL, Battaglia FP, Jensen O, Moser EI, Moser
M-B. 2006. Path integration and the neural basis of the
“cognitive map. Nature Rev Neurosci 7: 663– 678.
Mehta MR, Barnes CA, McNaughton BL. 1997. Experience-
dependent, asymmetric expansion of hippocampal place
fields. Proc Natl Acad Sci 94: 8918–8921.
Mehta MR, Quirk MC, Wilson MA. 2000. Experience-de-
pendent asymmetric shape of hippocampal receptive
fields. Neuron 25: 707– 715.
Miller JF, Neufang M, Solway A, Brandt A, Trippel M, Mader
I, Hefft S, Merkow M, Polyn SM, Jacobs J, et al. 2013.
Neural activity in human hippocampal formation reveals
the spatial context of retrieved memories. Science 342:
1111– 1114.
Milner B, Corkin S, Teuber HL. 1968. Further analysis of the
hippocampal amnesic syndrome: 14-year follow-up
study of H.M. Neuropsychologia 6: 215– 234.
Mishkin M. 1978. Memory in monkeys severely impaired by
combined but not by separate removal of amygdala and
hippocampus. Nature 273: 297–298.
Moita MA, Rosis S, Zhou Y, LeDoux JE, Blair HT. 2004.
Putting fear in its place: Remapping of hippocampal
place cells during fear conditioning. J Neurosci 24:
7015– 7023.
Monaco JD, Abbott LF. 2011. Modular realignment of en-
torhinal grid cell activity as a basis for hippocampal re-
mapping. J Neurosci 31: 9414– 9425.
Monaco JD, Rao G, Roth ED, Knierim JJ. 2014. Attentive
scanning behavior drives one-trial potentiation of hip-
pocampal place fields. Nat Neurosci 17: 725– 731.
Moser EI, Kropff E, Moser M-B. 2008. Place cells, grid cells,
and the brain’s spatial representation system. Annu Rev
Neurosci 31: 69– 89.
Muller RU, Kubie JL. 1987. The effects of changes in the
environment on the spatial firing of hippocampal com-
plex-spike cells. J Neurosci 7: 1951– 1968.
Muzzio IA, Levita L, Kulkarni J, Monaco J, Kentros C, Stead
M, Abbott LF, Kandel ER. 2009. Attention enhances the
retrieval and stability of visuospatial and olfactory rep-
resentations in the dorsal hippocampus. PLoS Biol 7:
O’Keefe J. 1976. Placeunits in the hippocampus of the freely
moving rat. Exp Neurol 51: 78– 109.
O’Keefe J, Burgess N. 1996. Geometric determinants of the
place fields of hippocampal neurons. Nature 381: 425–
O’Keefe J, Burgess N. 2005. Dual phase and rate coding in
hippocampal place cells: Theoretical significance and re-
lationship to entorhinal grid cells. Hippocampus 15: 853–
O’Keefe J, Dostrovsky J. 1971. The hippocampus as a spatial
map. Preliminary evidence from unit activity in the free-
ly-moving rat. Brain Res 34: 171– 175.
O’Keefe J, Nadel L. 1978. The hippocampus as a cognitive
map. Clarendon, Oxford.
O’Keefe J, Speakman A. 1987. Single unit activity in the rat
hippocampus during a spatial memory task. Exp Brain
Res 68: 1 –27.
O’Neill J, Senior T, Csicsvari J. 2006. Place-selective firing of
CA1 pyramidal cells during sharp wave/ripple network
patterns in exploratory behavior. Neuron 49: 143–155.
Pavlides C, Winson J. 1989. Influences of hippocampal place
cell firing in the awake state on the activity of these cells
during subsequent sleep episodes. J Neurosci 9: 2907–
Pfeiffer BE, Foster DJ. 2013. Hippocampal place-cell se-
quences depict future paths to remembered goals. Nature
497: 74–79.
Quirk GJ, Muller RU, Kubie JL, Ranck JB Jr. 1992. The
positional firing properties of medial entorhinal neu-
rons: Description and comparison with hippocampal
place cells. J Neurosci 12: 1945–1963.
Ranck JB Jr. 1973. Studies on single neurons in dorsal hip-
pocampal formation and septum in unrestrained rats:
I. Behavioral correlates and firing repertoires. Exp Neurol
41: 461– 531.
Ranck JB. 1985. Head direction cells in the deep cell layer of
dorsal presubiculum in freely moving rats. In Electrical
activity of the archicortex (ed. Buzsa
´ki G, Vanderwolf
CH), pp. 217– 220. Akademiai Kiado, Budapest.
Rolls ET, Stringer SM, Elliot T. 2006. Entorhinal cortex grid
cells can map to hippocampal place cells by competitive
learning. Network 17: 447– 465.
Rotenberg A, Mayford M, Hawkins RD, Kandel ER, Muller
RU. 1996. Mice expressing activated CaMKII lack low
frequency LTP and do not form stable place cells in the
CA1 region of the hippocampus. Cell 87: 1351–1361.
Rowland DC, Moser M-B. 2014. From cortical modules to
memories. Curr Opin Neurobiol 24C: 22– 27.
Samsonovich A, McNaughton BL. 1997. Path integration
and cognitive mapping in a continuous attractor neural
network model. J Neurosci 17: 5900– 5920.
Sargolini F, Fyhn M, Hafting T, McNaughton BL, Witter MP,
Moser M-B, Moser EI. 2006. Conjunctive representation
of position, direction and velocity in entorhinal cortex.
Science 312: 754–758.
Savelli F, Knierim JJ. 2010. Hebbian analysis of the transfor-
mation of medial entorhinal grid-cell inputs to hippo-
campal place fields. J Neurophysiol 103: 3167–3183.
Savelli F, Yoganarasimha D, Knierim JJ. 2008. Influence of
boundary removal on the spatial representations of the
medial entorhinal cortex. Hippocampus 18: 1270– 1282.
Scoville WB, Milner B. 1957. Loss of recent memory after
bilateral hippocampal lesions. J Neurol Neurosurg Psychi-
atry 20: 11 –21.
Singer W. 1993. Synchronization of cortical activity and its
putative role in information processing and learning.
Annu Rev Physiol 55: 349–374.
Smith SL, Smith IT, Branco T, Ha
¨usser M. 2013. Dendritic
spikes enhance stimulus selectivity in cortical neurons in
vivo. Nature 503: 115– 120.
Solstad T, Moser EI, Einevoll GT. 2006. From grid cells to
place cells: A mathematical model. Hippocampus 16:
Solstad T, Boccara CN, Kropff E, Moser M-B, Moser EI.
2008. Representation of geometric borders in the ento-
rhinal cortex. Science 322: 1865– 1868.
M.-B. Moser et al.
14 Advanced Online Article. Cite this article as Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a021808
Squire LR. 1992. Memory and the hippocampus: A synthesis
from findings with rats, monkeys, and humans. Psychol
Rev 99: 195 –231.
Squire LR, Alvarez P. 1995. Retrograde amnesia and memory
consolidation: A neurobiological perspective. Curr Opin
Neurobiol 5: 169– 177.
Stensola H, Stensola T, Solstad T, Frøland K, Moser M-B,
Moser EI. 2012. The entorhinal map is discretized. Na-
ture 492: 72– 78.
Suddendorf T, Corballis MC. 2007. The evolution of fore-
sight: What is mental time travel, and is it unique to
humans? Behav Brain Sci 30: 299– 313.
Taube JS. 2007. The head direction signal: Origins and
sensory-motor integration. Annu Rev Neurosci 30: 181–
Taube JS, Burton HL. 1995. Head direction cell activity
monitored in a novel environment and during a cue con-
flict situation. J Neurophysiol 74: 1953– 1971.
Taube JS, Muller RU, Ranck JB Jr. 1990. Head-directioncells
recorded from the postsubiculum in freely moving rats:
I. Description and quantitative analysis. J Neurosci 10:
420– 435.
Tolman EC. 1948. Cognitive maps in rats and men. Psychol
Rev 55: 189 –208.
Watrous AJ, Tandon N, Conner CR, Pieters T, Ekstrom AD.
2013. Frequency-specific network connectivity increases
underlie accurate spatiotemporal memory retrieval. Nat
Neurosci 16: 349– 356.
Wills TJ, Cacucci F, Burgess N, O’Keefe J. 2010. Develop-
ment of the hippocampal cognitive map in preweanling
rats. Science 328: 1573– 1576.
Wilson MA, McNaughton BL. 1993. Dynamics of the hip-
pocampal ensemble code for space. Science 261: 1055–
Wilson MA, McNaughton BL. 1994. Reactivation of hippo-
campal ensemble memories during sleep. Science 265:
Wood ER, Dudchenko PA, Eichenbaum H. 1999. The global
record of memory in hippocampal neuronal activity. Na-
ture 397: 613– 616.
Wood ER, Dudchenko PA, Robitsek RJ, Eichenbaum H.
2000. Hippocampal neurons encode information about
different types of memory episodes occurring in the same
location. Neuron 27: 623–633.
Yoganarasimha D, Yu X, Knierim JJ. 2006. Head direction
cell representations maintain internal coherence during
conflicting proximal and distal cue rotations: Compari-
son with hippocampal place cells. J Neurosci 26: 622– 631.
Young BJ, Fox GD, Eichenbaum H. 1994. Correlates of hip-
pocampal complex-spike cell activity in rats performing a
nonspatial radial maze task. J Neurosci 14: 6553– 6563.
Zhang SJ, Ye J, Miao CL, Tsao A, CerniauskasI, Ledergerber
D, Moser M-B,Moser EI. 2013. Optogenetic dissection of
entorhinal-hippocampal functional connectivity. Science
340: 1232627.
Zhang S-J, Ye J, Couey JJ, Witter MP, Moser EI, Moser M-B.
2014. Functional connectivity of the entorhinal-hippo-
campal space circuit. Philos Trans R Soc Lond B Biol Sci
369: 20120516.
Zola-Morgan S, Squire LR, Amaral DG. 1986. Human am-
nesia and the medial temporal region: Enduring memory
impairment following a bilateral lesion limited to field
CA1 of the hippocampus. J Neurosci 6: 2950– 2967.
Place Cells, Grid Cells, and Memory
Advanced Online Article. Cite this article as Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a021808 15
Place Cells, Grid Cells, and Memory
May-Britt Moser, David C. Rowland, and Edvard I. Moser
TOC Blurb: Hippocampal networks allow for fast storage of large quantities of uncorrelated
spatial information. Functionally specialized cell types (e.g., place cells and grid cells) may
form the basis of this system.
... In both studies, manipulating theta physiology led to memory defects, yet the tuning of hippocampal neurons to spatiotemporal features remained unaltered, providing a clear example of a dissociation between theta, memory, and spatial tuning. Spatiotemporal coding has long been proposed to serve as a substrate for memory (Kentros et al., 2004;Eichenbaum, 2014;Moser et al., 2015;Lisman et al., 2017), making this dissociation between hippocampal representations and memory performance perplexing. In the following sections, we will explore potential explanations for this phenomenon and in particular why sequential activities of hippocampal neurons can be uncoupled from theta oscillations. ...
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Oscillations in neural activity are widespread throughout the brain and can be observed at the population level through the local field potential. These rhythmic patterns are associated with cycles of excitability and are thought to coordinate networks of neurons, in turn facilitating effective communication both within local circuits and across brain regions. In the hippocampus, theta rhythms (4–12 Hz) could contribute to several key physiological mechanisms including long-range synchrony, plasticity, and at the behavioral scale, support memory encoding and retrieval. While neurons in the hippocampus appear to be temporally coordinated by theta oscillations, they also tend to fire in sequences that are developmentally preconfigured. Although loss of theta rhythmicity impairs memory, these sequences of spatiotemporal representations persist in conditions of altered hippocampal oscillations. The focus of this review is to disentangle the relative contribution of hippocampal oscillations from single-neuron activity in learning and memory. We first review cellular, anatomical, and physiological mechanisms underlying the generation and maintenance of hippocampal rhythms and how they contribute to memory function. We propose candidate hypotheses for how septohippocampal oscillations could support memory function while not contributing directly to hippocampal sequences. In particular, we explore how theta rhythms could coordinate the integration of upstream signals in the hippocampus to form future decisions, the relevance of such integration to downstream regions, as well as setting the stage for behavioral timescale synaptic plasticity. Finally, we leverage stimulation-based treatment in Alzheimer's disease conditions as an opportunity to assess the sufficiency of hippocampal oscillations for memory function.
... For example, switching reward probability learning is impaired by prefrontal cortex lesions [94][95][96]. Processing of context, such as spatial environment, is performed by the hippocampus [97][98][99][100]. Thus, one class of reinforcement learning models allows the agent to create an internal model of the environment [101][102][103][104]. ...
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A major advance in understanding learning behavior stems from experiments showing that reward learning requires dopamine inputs to striatal neurons and arises from synaptic plasticity of cortico-striatal synapses. Numerous reinforcement learning models mimic this dopamine-dependent synaptic plasticity by using the reward prediction error, which resembles dopamine neuron firing, to learn the best action in response to a set of cues. Though these models can explain many facets of behavior, reproducing some types of goal-directed behavior, such as renewal and reversal, require additional model components. Here we present a reinforcement learning model, TD2Q, which better corresponds to the basal ganglia with two Q matrices, one representing direct pathway neurons (G) and another representing indirect pathway neurons (N). Unlike previous two-Q architectures, a novel and critical aspect of TD2Q is to update the G and N matrices utilizing the temporal difference reward prediction error. A best action is selected for N and G using a softmax with a reward-dependent adaptive exploration parameter, and then differences are resolved using a second selection step applied to the two action probabilities. The model is tested on a range of multi-step tasks including extinction, renewal, discrimination; switching reward probability learning; and sequence learning. Simulations show that TD2Q produces behaviors similar to rodents in choice and sequence learning tasks, and that use of the temporal difference reward prediction error is required to learn multi-step tasks. Blocking the update rule on the N matrix blocks discrimination learning, as observed experimentally. Performance in the sequence learning task is dramatically improved with two matrices. These results suggest that including additional aspects of basal ganglia physiology can improve the performance of reinforcement learning models, better reproduce animal behaviors, and provide insight as to the role of direct- and indirect-pathway striatal neurons.
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The authors postulate that their recently published theory explaining the essence of human self-awareness is useful to consider whether a personal planetary consciousness is emerging on our planet. Their earlier published theory posits that the feeling of self-awareness can be effectively explained when it is assumed that it arises as a result of the interaction of three processes, namely the feeling of qualia, the recurrent activity of neural circuits realizing the self-image, and the formation of the brain's electromagnetic field, important for the sense of subjectivity. This allows the authors in the next stage of inference to consider whether it is possible to find analogical elements and processes on a planetary scale. The authors specify the layers of distributed intelligence emerging on Earth. This allows them to consider the concept of a planetary global brain. The authors believe that the recent, quite unexpected, widespread use of new global-scale artificial intelligence systems such as Chat/GPT is an argument in favor of the formation of a global brain. Next, the authors mention the first known published intuitions related to planetary consciousness, especially Teilhard de Chardin's concept of the noosphere. The authors also indicate the observable manifestations of existence of alleged planetary consciousness. They believe that people's transcendent feelings should be considered as such its manifestation. They hypothesize that the recently observed polarization of worldviews is also magnified by alleged emerging planetary consciousness. The authors, drawing on the analogy between brain hemisphere specialization and the planet's two ideological blocks, conclude that it's vital to patiently moderate conflicts and accept that altering this pattern is unfeasible. Some other practical conclusions are also formulated.
Neuronal activity during experience is thought to induce plastic changes within the hippocampal network that underlie memory formation, although the extent and details of such changes in vivo remain unclear. Here, we employed a temporally precise marker of neuronal activity, CaMPARI2, to label active CA1 hippocampal neurons in vivo, followed by immediate acute slice preparation and electrophysiological quantification of synaptic properties. Recently active neurons in the superficial sublayer of stratum pyramidale displayed larger post-synaptic responses at excitatory synapses from area CA3, with no change in pre-synaptic release probability. In contrast, in vivo activity correlated with weaker pre- and post-synaptic excitatory weights onto pyramidal cells in the deep sublayer. In vivo activity of deep and superficial neurons within sharp-wave/ripples was bidirectionally changed across experience, consistent with the observed changes in synaptic weights. These findings reveal novel, fundamental mechanisms through which the hippocampal network is modified by experience to store information.
Impairments in spatial navigation in humans can be preclinical signs of Alzheimer's disease. Therefore, cognitive tests that monitor deficits in spatial memory play a crucial role in evaluating animal models with early stage Alzheimer's disease. While Chinese tree shrews (Tupaia belangeri) possess many features suitable for Alzheimer's disease modeling, behavioral tests for assessing spatial cognition in this species are lacking. Here, we established reward-based paradigms using the radial-arm maze and cheeseboard maze for tree shrews, and tested spatial memory in a group of 12 adult males in both tasks, along with a control water maze test, before and after bilateral lesions to the hippocampus, the brain region essential for spatial navigation. Tree shrews memorized target positions during training, and task performance improved gradually until reaching a plateau in all 3 mazes. However, spatial learning was compromised post-lesion in the 2 newly developed tasks, whereas memory retrieval was impaired in the water maze task. These results indicate that the cheeseboard task effectively detects impairments in spatial memory and holds potential for monitoring progressive cognitive decline in aged or genetically modified tree shrews that develop Alzheimer's disease-like symptoms. This study may facilitate the utilization of tree shrew models in Alzheimer's disease research.
The classical notion of a 'language of thought' (LoT), advanced prominently by the philosopher Jerry Fodor, is an influential position in cognitive science whereby the mental representations underpinning thought are considered to be compositional and productive, enabling the construction of new complex thoughts from more primitive symbolic concepts. LoT theory has been challenged because a neural implementation has been deemed implausible. We disagree. Examples of critical computational ingredients needed for a neural implementation of a LoT have in fact been demonstrated, in particular in the hippocampal spatial navigation system of rodents. Here, we show that cell types found in spatial navigation (border cells, object cells, head-direction cells, etc.) provide key types of representation and computation required for the LoT, underscoring its neurobiological viability.
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Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task understanding and behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.
Introduction: The relationship between the entorhinal cortex (EC) and the hippocampus has been studied by different authors, who have highlighted the importance of grid cells, place cells, and the trisynaptic circuit in the processes that they regulate: the persistence of spatial, explicit, and recent memory and their possible impairment with ageing. Objective: We aimed to determine whether older age causes changes in the size and number of grid cells contained in layer III of the EC and in the granular layer of the dentate gyrus (DG) of the hippocampus. Methods: We conducted post-mortem studies of the brains of 6 individuals aged 56-87 years. The brain sections containing the DG and the adjacent EC were stained according to the Klüver-Barrera method, then the ImageJ software was used to measure the individual neuronal area, the total neuronal area, and the number of neurons contained in rectangular areas in layer III of the EC and layer II of the DG. Statistical analysis was subsequently performed. Results: We observed an age-related reduction in the cell population of the external pyramidal layer of the EC, and in the number of neurons in the granular layer of the DG. Conclusion: Our results indicate that ageing causes a decrease in the size and density of grid cells of the EC and place cells of the DG.
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The cellular generation and spatial distribution of gamma frequency (40-100 Hz) activity was examined in the hippocampus of the awake rat. Field potentials and unit activity were recorded by multiple site silicon probes (5- and 16-site shanks) and wire electrode arrays. Gamma waves were highly coherent along the long axis of the dentate hilus, but average coherence decreased rapidly in the CA3 and CA1 directions. Analysis of short epochs revealed large fluctuations in coherence values between the dentate and CA1 gamma waves. Current source density analysis revealed large sinks and sources in the dentate gyrus with spatial distribution similar to the dipoles evoked by stimulation of the perforant path. The frequency changes of gamma and theta waves positively correlated (40-100 Hz and 5-10 Hz, respectively). Putative interneurons in the dentate gyrus discharged at gamma frequency and were phase-locked to the ascending part of the gamma waves recorded from the hilus. Following bilateral lesion of the entorhinal cortex the power and frequency of hilar gamma activity significantly decreased or disappeared. Instead, a large amplitude but slower gamma pattern (25-50 Hz) emerged in the CA3-CA1 network. We suggest that gamma oscillation emerges from an interaction between intrinsic oscillatory properties of interneurons and the network properties of the dentate gyrus. We also hypothesize that under physiological conditions the hilar gamma oscillation may be entrained by the entorhinal rhythm and that gamma oscillation in the CA3-CA1 circuitry is suppressed by either the hilar region or the entorhinal cortex.
Ensemble recordings of 73 to 148 rat hippocampal neurons were used to predict accurately the animals' movement through their environment, which confirms that the hippocampus transmits an ensemble code for location. In a novel space, the ensemble code was initially less robust but improved rapidly with exploration. During this period, the activity of many inhibitory cells was suppressed, which suggests that new spatial information creates conditions in the hippocampal circuitry that are conducive to the synaptic modification presumed to be involved in learning. Development of a new population code for a novel environment did not substantially alter the code for a familiar one, which suggests that the interference between the two spatial representations was very small. The parallel recording methods outlined here make possible the study of the dynamics of neuronal interactions during unique behavioral events.
The experiments reported here are among the simplest experiments one might do on the nervous system. We simply record action potentials from a single neuron in the dorsal hippocampal formation and septum in an unrestrained rat and study the correlation between the firing of the neuron and the behavior of a rat. However, there is no reason to believe that this approach is going to be valuable. If one were to record from a single element in a computer and try to correlate voltages at this element with either the input or the output of the computer, one would, in general, not learn much of use, except perhaps about the input and output devices. Clearly, the firing of some neurons in the brain has something to do with overt behavior or with information coming into the brain, especially for those neurons within a few synapses of a receptor or effector. Equally clearly, there are some things going on in the brain which do not have a simple relation to overt behavior or inputs to brain. The hippocampus is many synapses away from sensory or motor neurons, so simple relations cannot be expected.
Electrophysiological recording studies in the dorsocaudal region of medial entorhinal cortex ( dMEC) of the rat reveal cells whose spatial firing fields show a remarkably regular hexagonal grid pattern ( Fyhn et al., 2004; Hafting et al., 2005). We describe a symmetric, locally connected neural network, or spin glass model, that spontaneously produces a hexagonal grid of activity bumps on a two-dimensional sheet of units. The spatial firing fields of the simulated cells closely resemble those of dMEC cells. A collection of grids with different scales and/or orientations forms a basis set for encoding position. Simulations show that the animal's location can easily be determined from the population activity pattern. Introducing an asymmetry in the model allows the activity bumps to be shifted in any direction, at a rate proportional to velocity, to achieve path integration. Furthermore, information about the structure of the environment can be superimposed on the spatial position signal by modulation of the bump activity levels without significantly interfering with the hexagonal periodicity of firing fields. Our results support the conjecture of Hafting et al. (2005) that an attractor network in dMEC may be the source of path integration information afferent to hippocampus.
Conference Paper
Accumulating evidence points to cortical oscillations as a mechanism for mediating interactions among functionally specialized neurons in distributed brain circuits. A brain function that may use such interactions is declarative memory-that is, memory that can be consciously recalled, such as episodes and facts. Declarative memory is enabled by circuits in the entorhinal cortex that interface the hippocampus with the neocortex. During encoding and retrieval of declarative memories, entorhinal and hippocampal circuits are thought to interact via theta and gamma oscillations, which in awake rodents predominate frequency spectra in both regions. In favour of this idea, theta-gamma coupling has been observed between entorhinal cortex and hippocampus under steady-state conditions in well-trained rats; however, the relationship between interregional coupling and memory formation remains poorly understood. Here we show, by multisite recording at successive stages of associative learning, that the coherence of firing patterns in directly connected entorhinal-hippocampus circuits evolves as rats learn to use an odour cue to guide navigational behaviour, and that such coherence is invariably linked to the development of ensemble representations for unique trial outcomes in each area. Entorhinal-hippocampal coupling was observed specifically in the 20-40-hertz frequency band and specifically between the distal part of hippocampal area CA1 and the lateral part of entorhinal cortex, the subfields that receive the predominant olfactory input to the hippocampal region. Collectively, the results identify 20-40-hertz oscillations as a mechanism for synchronizing evolving representations in dispersed neural circuits during encoding and retrieval of olfactory-spatial associative memory.