HIPPOCAMPUS 17:775–785 (2007)
COMMENTARY
Integration of the Sensory Inputs to Place Cells:
What, Where, Why, and How?
Kathryn J. Jeffery*
ABSTRACT:
The hippocampal place cells are a highly multimodal class
of neurons, receiving information from many different sensory sources to
correctly localize their firing to restricted regions of an environment. Evidence suggests that the sensory information is processed upstream of the
hippocampus, to extract both angular and linear metric information, and
also contextual information. These various kinds of information need to
be integrated for coherent firing fields to be generated, and the present
article reviews recent evidence concerning how this occurs. It is concluded that there is a functional dissociation of the cortical inputs, with
one class of incoming information comprising purely metric information
concerning distance and orientation, probably routed via the grid cells
and head direction cells. The other class of information is much more heterogeneous and serves, at least in part, to contextualize the spatial inputs
so as to provide a unique representation of the place the animal is in. Evidence from remapping studies suggests that the metric and contextual
inputs interact upstream of the place cells, perhaps in entorhinal cortex.
A full understanding of the generation of the hippocampal place representation will require elucidation of the representational functions of the
afferent cortical areas. V 2007 Wiley-Liss, Inc.
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KEY WORDS:
context
place cells; grid cells; sensory integration; space;
INTRODUCTION
Since O’Keefe’s discovery of place cells, in the early 1970s, research
attention has been fairly evenly divided between trying to understand
what these cells are for and in understanding how their firing properties
arise in the first place; that is, in how they determine where they are. Even
before their place-specific firing properties were elucidated, it was recognized that hippocampal neurons respond to sensory information arriving
from a variety of different modalities (Ranck, 1973). Once it was appreciated that these neurons were interested in ‘‘place,’’ the question arose as to
how they could construct a place representation from such varied information. What form does this information take? Is the ‘‘placiness’’ of place
cell firing locations (the place fields) inherent in the inputs, or does it
somehow arise from the computations that place cells perform? Indeed, is
space special to the hippocampus at all, or do place cells just look placey
Department of Psychology, Institute of Behavioural Neuroscience, University College London, London, United Kingdom
Grant sponsor: Biotechnology and Biological Sciences Research Council.
*Correspondence to: Dr K.J. Jeffery, Department of Psychology, Institute
of Behavioural Neuroscience, University College London, 26 Bedford
Way, London WC1H OAP, United Kingdom. E-mail: k.jeffery@ucl.ac.uk
Accepted for publication 1 May 2007
DOI 10.1002/hipo.20322
Published online 5 July 2007 in Wiley InterScience (www.interscience.
wiley.com).
C 2007
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WILEY-LISS, INC.
because rats experience spatial stimuli more than they
do other kinds of stimuli?
To answer this question it is necessary to have a clear
idea of what kinds of information place cells receive,
and of what they do with this information once they
receive it. The present article reviews data on how place
cells integrate their sensory inputs and examines
whether these inputs are specifically spatial or whether
there is another, superordinate category of description
that encompasses both the spatial and nonspatial inputs
known to influence place cells.
Adding fuel to the spatial/supraspatial debate has
been the emergence in recent years of many reports of
nonspatial determinants of place cell activity, raising
the question of whether place is really the fundamental
correlate of place cell activity at all. Observed nonspatial determinants of place cell activity have fallen into
two broad classes. The first comprises those kinds of
stimuli which although not strictly spatial (in the sense
of ‘‘metric’’: see below) in themselves, nevertheless may
contribute to the fleshing out of a place representation.
An example might be the ‘‘color’’ of the environment,
its texture, odor, or even (so it now appears) the expectations or intentions of the rat. These stimuli have in
common the property of being stable—that is, continuous in time and/or place—and of defining the environment in a way that may assist the animal not only in
knowing where it is but also what occurrences to expect
there or what actions it should perform. The second
class of stimuli is a much more ill-defined and contentious collection, consisting of discrete events such as
tone conditioned stimuli (Berger and Thompson,
1978), reward (Breese et al., 1989), shock (Moita et al.,
2003), and discrete odor cues (Wood et al., 1999). The
degree to which these discrete stimuli modulate place
cell activity independently of place is still debated
(O’Keefe, 1999), as is their functional significance.
For the remainder of this article, the term ‘‘spatial’’
will be replaced with ‘‘metric’’ to remove any ambiguity
about what is meant by this class of stimuli. As
described further in the next section, ‘‘metric’’ refers to
some kind of distance measure, either linear or angular.
Thus, by ‘‘metric inputs’’ to place cells is meant ‘‘inputs
to carrying information about distance and/or direction’’. Nonmetric inputs are those that do not carry
such information. It will be argued that there exists a
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functional and anatomical dissociation between metric inputs
and (what we shall call) contextual, nonmetric inputs with the
former being selected, or ‘‘gated’’, by the latter. The article will
conclude that evidence supports a functional differentiation of
spatial vs. nonspatial inputs to dorsal hippocampus, with spatial
inputs being primary.
METRIC INPUTS
A metric, in mathematics, is defined by the Oxford English
Dictionary as the ‘‘binary function of a topological space which
gives, for any two points of the space, a value equal to the distance between them, or a value treated as analogous to distance
for the purpose of analysis.’’ The critical thing about a metric is
thus that it provides, by one means or another, information
about the distances between two identified points in a given
space. For place cells, ‘‘distance’’ is taken to mean either ‘‘Euclidean distance’’ or ‘‘angular distance.’’ In the case of a rat in a recording box, this information might consist of information
determining how far apart (in Euclidean space), say, two place
fields are.
Although place fields undeniably occur in specific regions of
space, it was never a foregone conclusion that these locations
should be specified metrically. They might be specified by some
nonmetric means: for example, by some conjunction of cues
such as odors, visual cues, or a mixture that only occurs at a particular place. Alternatively they might be determined topologically, so that a given place field would primarily be localized by
the fields around it, with preservation of adjacency relationships
between all the fields in a given environment, but no explicit
representation of distance per se. In this case, there would of
course need to be some mechanism for anchoring the topological
sheet of fields, as it were, to the real world, but this need not be
a metric one. However it turns out, as described below, that a
growing body of evidence suggests that the environment does
indeed provide metric—distance and direction—information.
This evidence falls into three main classes, the first weakly circumstantial and the remaining two very strongly supportive of
the notion.
1. Place cells are sensitive to metric transformations of the
environment.
2. Place cells will accept replacement of one sensory stimulus
by another, totally different kind of stimulus, as long as
these stimuli are equivalent (or isomorphic) in the metric
domain.
3. Cells in brain areas afferent to the place cells have angular
and linear metric response properties.
Place Cells Respond Metrically to Metric
Transformations of the Environment
The first indication that place field locations might be determined by metric information came from Muller and Kubie’s
Hippocampus DOI 10.1002/hipo
classic scaling experiment (Muller and Kubie, 1987), in which
expansion of an environment was found to cause an expansion
of place field size. Interestingly, however, the scaling was not
quite proportional, suggesting an influence of other cues than
simple environment size.
In a more tightly-controlled test of scaling, O’Keefe and Burgess (1996) manipulated the metric properties of a recording
environment—a wooden box—in each of the two dimensions,
configuring the recording box as a small square, a rectangle oriented east–west or north–south, or a large square. Because all
the box configurations used the same physical walls, the changes
were subtle, and so the place cells did not reorganize their firing
as they do if large, noticeable changes are made to an environment. Place cells also reacted in a subtle way, usually just shifting
their fields slightly. Different cells reacted in different ways, as if
not all of the cells were privy to the same information. By and
large, fields tended to be most influenced by the walls closest to
them so that they ‘‘followed’’ those walls in preference to the
more distant ones. Taken together, the data suggested that a
given cell processed information primarily from a subset of
nearby walls. O’Keefe and Burgess proposed that a given cell was
in receipt of metric information from two or more orthogonal
walls in the form of a Gaussian tuning curve peaked at a set distance close to each of those walls. A combination of two such
Gaussians from two orthogonal walls proved very effective in
modeling the way in which the fields reacted to the changes in
the environment.
Although the O’Keefe and Burgess model postulated an explicitly metric input to place cells, the stretchy-box results might
still be explained by reference to some kind of sensory input
from the walls that varies linearly with distance but does not explicitly encode distance (that is, could not in itself be used to
perform metric computations such as trigonometry). Arguing
against this is the second indication that place field inputs have
an explicitly metric component.
Place Cells Will Accept Replacement of One
Sensory Stimulus by Another Totally Different
Kind of Stimulus if These Stimuli Are
Equivalent in the Metric Domain
It is a now well-established finding that place cells will accept
stimulus substitution if the stimuli are metrically equivalent. An
example of what this means is shown in the angular domain in
Figure 1. A place field, recorded in a rotationally symmetrical
environment, ‘‘followed’’ rotation of the visual directional cue, a
lighted cue card, irrespective of signals from the rat’s internal
sense of direction. If the card was removed so that no visual
polarizing landmarks remain, the cells now followed, equally
readily, rotations of the rat itself. Thus, the cells can use either a
visual landmark or the internal ‘‘direction sense’’ of the rat—
both polarizing stimuli—to orient their fields. Similarly,
although place cells generally follow visual polarizing cues, in a
sloping environment they can use the slope as an orienting cue if
the visual cue is removed (Jeffery et al., 2006). This is despite
INTEGRATION OF THE SENSORY INPUTS TO PLACE CELLS
FIGURE 1.
Stimulus substitution by place cells in the directional domain [see (Jeffery and O’Keefe, 1999) for details]. Place
cells were recorded as a rat foraged in a square box. Between trials,
the rat was covered and rotated, and the visual cue was either rotated
by a multiple of 908 or removed altogether. In the presence of the
cue card, the place field of the cell (shown in smoothed contour-plot
form) always rotated with the card, regardless of the rat’s internal
direction sense (top row). In the absence of the card, however, the
field rotated with the rat’s internal sense of direction (bottom row).
the fact that slope is determined via touch and proprioception
whereas the cue card is processed visually.
Stimulus substitution has also been found in the linear domain.
Quirk et al. (Quirk et al., 1990) found that place fields that were
well established in a lighted environment could persist in the dark
if the animal had been in the environment when the lights were
switched off but not if it had not been removed first, and Gothard
et al. found that place fields would be determined by movement
cues (by a process commonly known as ‘‘path integration’’, discussed below) if the rat was far from a visual landmark but visual
cues if it were close to it (Gothard et al., 1996).
This switch between two very different types of sensory information, such as between vision and path integration, is one of
the most compelling arguments to be made for the metric nature
of (at least some of ) the inputs to place cells. Put another way, in
the metric domain, and only in the metric domain, vision and
motion cues are equivalent, or isomorphic, and thus able to substitute for each other. Such data argue very convincingly for a
fundamentally metric basis to place field inputs.
Cells in Brain Areas Afferent to the Place Cells
Have Linear and Angular Metric Properties
The final demonstration that place cells receive metric information comes from observations that cells in brain areas surrounding
the hippocampus, and which are either directly or indirectly afferent
to it, have intrinsic metric properties that seem to be independent
of environmental cues, except insofar as external cues are needed to
anchor the metric to the real world in a reproducible way. These
observations have been made in both angular and linear domains.
In the angular domain, the relevant discovery is that of head
direction cells, made by Ranck and Taube in the late 1980s and
first published in 1990 (Taube et al., 1990a,b). Head direction
cells fire only when the animal’s head points in a particular direction, and are strongly controlled by visual landmarks. Like place
cells, and unlike more primary sensory neurons, the cells can
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switch easily between vision and path integration (Taube, 1998)
and thus seem to respond to information that is isomorphic in
the (angular) metric domain.
Evidence for linear metric encoding is only very recent, and
stems from the remarkable discovery made by Hafting et al. of a
class of cells in the entorhinal cortex known as ‘‘grid cells’’
(Hafting et al., 2005). Grid cells occur in dorso-medial EC, a
region of cortex afferent to the place cells, and while they have
place fields that individually resemble hippocampal place fields,
these fields are circular (rather than ovoid as place fields generally
are) and they recur at regularly spaced intervals across the environment. These repeating fields form a pattern long familiar to
physicists and mathematicians as hexagonal closest packing (this
being the most efficient way of tiling a planar surface with
circles). The pattern of fields of a given cell forms a grid covering
the whole of the traversable environment, and hence the name
‘‘grid cells’’ for these neurons. The constant interpeak interval of
grid cells is independent of the environment (with some caveats,
detailed later) and must therefore derive from some internal
property of the network, rather than from external landmarks
(though external cues must subsequently attach the grid to the
outside world).
The finding of something looking very much like a metric
grid (akin to a sheet of graph paper) in a cortical region directly
afferent to place cells strongly suggests that place cells receive information conveying something about the metric properties of
the environment the animal is in. Depending on which dorsoventral region of EC is examined, the interpeak spacing can vary
from about 30 cm dorsally to some number of meters in the
most ventral regions. Interestingly, in a given animal in a given
environment, the orientation of the grids appears to be the same,
although the exact location of peaks varies form cell to cell.
Thus, the grid cell network as a whole seems to receive the same
orientational information. The obvious source of this information is the nearby head direction network, known to project to
these areas (Sargolini et al., 2006).
INTEGRATION OF METRIC CUES
WITH EACH OTHER
Having established that place cells appear to receive metric
inputs, the question arises as to how the cells integrate these
inputs. There are two classes of metric input, directional and linear, and so the relevant interactions comprise directional inputs
with each other, linear inputs with each other, and directional
inputs with linear inputs. At this stage, the discussion is about
purely metric information—the question of how nonmetric information (including that from objects and landmarks) is incorporated into the representation will be dealt with later.
Integration of Directional Inputs
With Each Other
Directional inputs are those that provide information about
the orientation of the environment, or of the rat within the enviHippocampus DOI 10.1002/hipo
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JEFFERY
ronment, and probably, as discussed above, act via the head
direction system. Two main classes of directional input have
been described: visual and movement-related (sometimes called
idiothetic).
Visual landmarks seem to be powerful cues for orienting the
place and head direction cells. In the early studies of place cells,
it was found that rotation of a prominent cue card within an
otherwise rotationally symmetrical environment would cause
concomitant rotation of place fields (Muller and Kubie, 1987).
Later studies found that cue card rotation also caused head direction cells to rotate their firing orientations (Taube et al., 1990b),
and that place cell and head direction cell rotation rarely, if they
ever become uncoupled (Knierim et al., 1995; Hargreaves et al.,
2007, this issue), leading to the proposition that place fields
assume the orientation they do because of the head direction
cells. Head direction cells respond better to polarizing visual
stimuli that are located at some distance from the rat (Zugaro
et al., 2001), and it is assumed that the directional system has
evolved to prefer cues that are more reliable indicators of global
orientation (distant cues being better because their relative angular position changes little with the animal’s movements).
Relatively little is known about how information from cue
ensembles is integrated in the directional domain. In a study
using two cue cards, Fenton et al. found that moving the two
cards apart by a small amount caused a topological deformation
of place fields, with all fields being influenced by both cards to a
greater or lesser extent, but a given field being most affected by
the card closest to it (Fenton et al., 2000). This is an interesting
experiment and raises the question of how head direction cells
would react to such a manipulation—would head direction cell
firing directions also ‘‘pull apart’’, or would the representation
maintain coherence, as predicted by attractor models of the head
direction system (Zhang, 1996)? The latter would necessitate a
partial uncoupling of the head direction and place cells, which,
although surprising, is not inconsistent with recently reported
data from double cue dissociations (Yoganarasimha et al., 2006),
in which head direction cell coherence was maintained but place
fields dissociated.
Directional cues occur in modalities other than vision. Earlier
we discussed two other kinds: movement-based cues (path integration), revealed by slowly rotating an animal in darkness and
observing that place fields rotated too, and terrain slope, which
provides vestibular and proprioceptive information. Conflict
experiments provide useful information about how these different forms of information interact. When vision and path integration are placed in conflict, place cells generally prefer to follow
the visual stimulus (Jeffery, 1998). Two exceptions to this rule
have been noted. The first is that if the rats saw the cue card
move and the movement was large (i.e., introduced a big conflict), the cells are less likely to follow it (Rotenberg and Muller,
1997). The second is that if the rat had previously seen that the
cue card could move, then the cells are subsequently disinclined
to follow it even if the conflict is now introduced out-of-sight of
the rat (Jeffery and O’Keefe, 1999). These experiments show
that the place system somehow weights the two sources of information and makes a decision about which is more, as it were,
Hippocampus DOI 10.1002/hipo
‘‘trustworthy.’’ Furthermore, this weighting is plastic and can be
modified by experience.
It remains to be determined how and where these multifarious
sources of directional information are integrated, but it is likely
that the above conflicts are resolved in the head direction system,
which seems, as far as we can tell, always to generate an unambiguous, holistic directional signal.
Integration of Linear Inputs With Each Other
Place fields have a two-dimensional structure, and so their
locations must be determined by at least two one-dimensional
metric parameters. These might consist of a vector together with
the distance from some origin (a landmark, say), and thus take
the form of a polar coordinate system. Alternatively, they might
consist of two distance parameters aligned orthogonally, and
thus comprise a Cartesian coordinate system in which the two
alignment directions form a reference frame, analogous to x and
y axes. Although a polar coordinate system might seem more intuitive for a biological agent, particularly one known to possess
directionally selective neurons, evidence in fact is more supportive of the latter.
Support for the Cartesian hypothesis of place field localization
first came from O’Keefe and Burgess’s (1996) stretchy-box
experiment, described earlier, in which it appeared that place
fields were formed by the intersection of distance inputs coming
from orthogonally aligned walls. In an extension of this model,
Hartley et al. showed that a similar effect would arise from multiple inputs from irregularly oriented walls (Hartley et al., 2000),
and incorporation of plasticity into their model accounts for development of fields with experience (Barry and Burgess, 2007,
this issue). The implication is that the underlying coordinate
framework is generated by the intersection of linear metrics,
rather than combination of a single linear metric together with
an angular metric, that is, it is Cartesian rather than polar.
In a test of the polar vs. Cartesian hypotheses, we progressively
deconstructed a four-walled recording environment until only a
single landmark and a single directional cue remained. The
experiment was conducted in near-darkness and the rat was confined to the centre of the room by a ‘‘noise boundary’’ (i.e., it
was punished with aversive white noise if it ventured beyond the
boundary: Barry et al., 2006). Figure 2 shows what happened
when the walls were removed one by one: place fields progressively disintegrated. Although the remaining features carried
enough information to uniquely define any point in a polar
coordinate-defined space, this information was apparently insufficient to define stable place fields (with the occasional rare
exception, see below). This suggests that the coordinate system is
not inherently polar.
The wall-removal experiment above provides some support
for O’Keefe and Burgess’s boundary vector model, inasmuch as
removal of walls caused place fields to break down. It should be
noted, however, that in a number of cases a field was able—at
least for a short time—to be supported by a single wall, and
occasionally, though very rarely, even a single pillar, a finding
that remains to be explained.
INTEGRATION OF THE SENSORY INPUTS TO PLACE CELLS
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FIGURE 2.
Degradation of place fields as their metric determinants are progressively removed. (A) A four-walled environment
was deconstructed, wall by wall, until only a single wall remained.
The quality of the place field steadily deteriorated too. (B) For a
population of cells, place field quality, as measured by compactness
(are over which the cell fired, left) and coherence (degree to which
the cell’s activity was concentrated in one place, right), was related
to the number of walls in the arena. This supports the notion that
place fields are determined by summation from boundaries, rather
than from polar-coordinates requiring only an origin and a direction. Adapted from Barry et al., 2006.
In an attempt to isolate the individual inputs—the putative
boundary vectors—to place cells, an additional experiment was
conducted in which one of the walls of the four-walled box was
pulled out from the others just enough to let the rat escape into
the surrounding room (Barry et al., 2006), thus enabling a comparison of the enclosed space (surrounded by four walls, albeit
one slightly separated from the rest) and the open space (each
part bounded by a single wall). It was thought that this procedure might uncover the component vectors driving a given place
field. In one case it seemed as if this might be so (Fig. 3 Cell 3),
but generally, cells were reluctant to fire outside the box, while
continuing to maintain their fields inside it. It thus seems that
place cells prefer to fire in enclosed spaces. One possible explanation for this is that the boundedness offers more possibilities for
summation of inputs to above firing threshold. This is a far from
proven hypothesis, however. It may also be that the intrabox field
is somehow able to suppress other fields in the connected space
outside the box by a nonlocality mechanism discussed later.
Where do the above-described linear metrics come from? In
other words, how does a place cell determine how far the animal
is from a given boundary? In a familiar environment, much of
this distance information may be visual, for example, the animal
may know from experience how high the walls are, and thus be
able to use their apparent height to infer their distance. In a
novel environment, however, or in darkness, it must be able to
use something else. A great deal of evidence now suggests that
this ‘‘something else’’ comprises movement cues generated as the
animal moves through space; this is the process of ‘‘path integration’’ referred to earlier [see (Etienne and Jeffery, 2004), for
review]. Path integration has been shown to influence metric
integration in the linear domain, and this was initially suggested
by the well-established observation that place fields persist in the
absence of vision (Quirk et al., 1990; Save et al., 1998). It was
shown explicitly by Gothard et al. (Gothard et al., 1996), who
recorded place cells as rats shuttled on a linear track and found
that fields were determined by the closest wall, even if this was
the one behind the rat. However, it was not until the discovery
of grid cells (Hafting et al., 2005), however, that the importance
of path integration as the basis of the hippocampal metric really
became established beyond doubt, because it seemed that the
constancy of the grid cell metric across environments could only
be established by an internal, movement-based process. In other
words, because the grid spacing of a grid cell occurs at its charac-
FIGURE 3.
Recording of three place cells in a closed (left
maps) and a surrounded but slightly open (right maps) space
defined by four walls. Place fields tended to be compact within the
bounded area. When the south wall was moved to allow the rat to
escape into the surrounding area, the cells continued to fire as
strongly or even more strongly within the bounded region, and
only occasionally was a field seen in the unbounded region (e.g.,
Cell 3). This observation supports the boundary summation hypothesis of place cell integration. Adapted with permission from
Barry et al., Rev Neurosci, 2006, 17, 71–97, ' Freund Publishing
House.
Hippocampus DOI 10.1002/hipo
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JEFFERY
teristic spatial frequency even in a never-before-visited environment, and even in the dark, this spacing could not be determined by static environmental cues (which are arbitrary, and
unique to a given environment) but only by dynamic, motionderived information (which is constant for all environments).
Despite the evident importance of motion-derived cues for
establishing a grid cell’s grid, there is now overwhelming evidence that the grid cell firing pattern in a familiar environment
must arise from a learned interaction between the internal metric
and environmental cues. Some of this evidence was presented by
Hafting et al. in their original report, in which they showed that
rotation of a cue card caused a concomitant rotation of the grids.
This reveals landmark control for the angular domain, at least.
The fact that grids in repeated trials always have the same offset
with respect to the environment also implies some kind of
anchoring process that aligns the grids to the environment in the
same way upon repeated trials. Another clue comes from the observation that the grids tend not to be perfectly regular, and that
the irregularities also persist across trials, suggesting that a given
peak in a grid is somehow uniquely specified in a given
environment.
That this anchoring is learned, rather than hard-wired, has
now been shown by an experiment of ours (Barry et al., 2007) in
which grids that were established in an environment of one size
were able to be deformed (or ‘‘rescaled’’) by subtle changes in
the dimensions of the enclosure (Fig. 4). This rescaling could be
induced in either direction: if the rat was familiarized with a
large square enclosure, compression of one of the axes to form a
rectangle caused a partial compression of the grid in that axis;
conversely, if the rat was familiarized with a rectangle, expansion
along the short axis to form a square caused expansion of the
grid ion that axis, but not the other. Because the rescaling was
only partial, the implication is that deforming the environment
sets up a conflict between the intrinsic path integration metric
and the extrinsic learned boundary cues. Understanding how
both intrinsic and extrinsic cues can influence grid spacing in
tandem like this is a current priority.
Integration of Directional and Linear Inputs
Regardless of whether the coordinate system employed by
place cells is polar or Cartesian, it is necessary for there to be
some kind of integration of directional and linear inputs. In the
polar case, this is because direction is one of the two components
of the vector identifying locations, and in the Cartesian case it is
because identical walls can only be distinguished on the basis of
their directions. Additionally, the finding that place cells can
‘‘path integrate’’ in order to update their position calculations
indicates that somewhere, angular and linear distance information must be processed together.
The head direction cells are the obvious source of directional
information to place cells, but because the head direction areas
do not project directly to the hippocampus, it has always been
understood that there must be some mediating structure. It now
seems likely that the grid cells are the site of this mediation.
Grids have a clear and reproducible orientation in a specific enviHippocampus DOI 10.1002/hipo
FIGURE 4.
Deformation of grid cell grids by deformation of a
previously-learned environment. Rats were familiarized with either
a large square environment (A) or rectangular environment (B)
and grid cells recorded, first in the familiar environment and then
in a resized environment that was either compressed (A) or
extended (B) along one or both axes. The raw grid cell activity in
the familiar environments (outlined in red) versus the rescaled
environments is shown on the left: the spatial autocorrelation
functions (Hafting et al., 2005) on the right. The crosses in the
autocorrelation maps show the centers of the central peaks.
Observe that the peaks become closer together along the compressed axis and further apart along the extended axis, but by a
lesser amount than the actual rescaling. This provides evidence
that the grids, although intrinsic initially, have become partially
‘‘attached’’ to the walls of the environment. [Color figure can be
viewed in the online issue, which is available at www.interscience.
wiley.com.]
ronment (Hafting et al., 2005) and are in receipt of direct inputs
from head direction areas. In addition, many grid cells, most
prominently in the deeper layers of entorhinal cortex, have directional firing properties, and many straightforward head direction
cells are also found in these areas (Sargolini et al., 2006). Most
intriguing of all, inactivation of hippocampus, which feeds back
into the deep layers of entorhinal cortex, causes some grid cells to
lose their grids but retain their directionality, becoming in effect,
head direction cells (Bonnevie et al., 2007). These findings point
to the importance of directional information in the grid cell
network and support the notion that that linear and angular information are combined here and passed thence to the place cells.
NONMETRIC (OR ‘‘CONTEXTUAL’’) INPUTS
The foregoing discussion focused on how place cells integrate
the various sources of information that specify where a place field
should be located. However, as noted earlier, a growing body of
evidence points to the responsiveness of place cells to nonmetric
inputs. Are these inputs of a qualitatively different kind, or are
both metric and nonmetric inputs merely variants of some other
kind of superordinate category of information? For reasons of
INTEGRATION OF THE SENSORY INPUTS TO PLACE CELLS
781
the animal is performing in a given chamber (Markus et al.,
1995) or changing the route it is traveling (Wood et al., 2000;
Frank et al., 2000). These are particularly interesting because
they do not involve making physical changes to the environment—the change is all in the mind of the rat, so to speak. We
shall refer to these classes of stimuli as ‘‘external’’ and ‘‘internal,’’
respectively.
Contextual Stimuli External to the Animal
FIGURE 5.
Interaction of contexts in remapping. Place cells
were recorded in a square box that was located in either a north
position or a south position until they remapped between the two
locations. The box color was then changed (in this example, from
black to white) and recordings again made in the north and south
positions. It can be seen that cells that discriminated the locations
in the familiar box no longer discriminated in the unfamiliar
box—the discrimination is thus context-specific. This can only be
explained by assuming an interaction between the two kinds of
contextual cue, location, and color upstream of the place cell.
Adapted from Hayman et al., 2003.
clarity and conciseness, we shall refer to the nonmetric inputs,
discussed in this section, as ‘‘contextual’’.
Modulation of spatial activity by nonspatial cues is manifest as
the phenomenon known widely as ‘‘remapping,’’ an example of
which is shown in Figure 5. Remapping occurs when a highly salient change is made to an environment, and was originally considered to reflect recruitment of a new environmental representation
(Muller and Kubie, 1987). In classic, so-called ‘‘complete’’ remapping, all cells alter their activity simultaneously, some stop firing,
some previously silent cells develop fields and some, about 50%,
lose a field in one place and develop a new one in a different place.
More recent reports have suggested that the distribution of on–off
vs. location-shift remapping differs depending on where the cells
are located, with CA3 cells more likely to on–off (or ‘‘rate’’) remap
and CA1 cells more likely to shift their fields (Guzowski et al.,
2004; Leutgeb et al., 2004). The question remains open as to
whether these are qualitatively different phenomena.
Remapping can be induced by a variety of different kinds of
environmental change, including changing the color1 of a cue
card (Bostock et al., 1991), the shape of the box (Lever et al.,
2002), color and odor of the box (Anderson and Jeffery, 2003),
or placing the recording box in a new place (Hayman et al.,
2003) or room (Leutgeb et al., 2005). It can, however, also be
elicited by a different kind of stimulus such as changing the task
1
Rats have essentially monochromatic vision and so the visual stimuli in
the experiments described here have always involved variants of black,
white and grey. Strictly speaking, these are not ‘‘colors,’’ they are degrees
of brightness, or luminance. However, the term ‘‘color’’ is used here
because it is a term we more usually use to describe wall and floor coverings. If the reader is offended by this mild inaccuracy then please mentally replace ‘‘color’’ with ‘‘brightness’’ throughout.
External stimuli known to invoke remapping are those that
cause some kind of salient, easily sensed change to the environment. Some kinds of environmental change invoke complete
remapping, i.e., change to the whole observed place cell population simultaneously, whereas others involve only some cells (partial remapping). Initial studies of remapping looked mainly at
complete remapping. The strong sense of observers that the place
cell network was ‘‘flipping’’ into a new state during this process
prompted the development of attractor-based models (Samsonovich and McNaughton, 1997; Tsodyks, 1999) to explain the sudden, network-wide transition in activity patterns. In attractor
models, the assemblage of incoming sensory data is shared
among neurons in the highly interconnected CA3 network and
eventually settles to a stable state, specific to that environment
and generating a unique pattern of activity across all place cells.
By this view, because an individual CA3 neuron gets most of its
inputs not directly from sensory sources, but rather, from other
hippocampal neurons (Amaral and Witter, 1989), the sensory
correlates (such as responsiveness to particular cues) arise as
emergent phenomena from network processes, rather than from
direct sensory drive. More recent findings of partial remapping
(e.g. Anderson and Jeffery, 2003) challenge this view somewhat,
because they require at least two networks of neurons (in some
cases four), one of which remaps and the remainder which do
not, in each one of the environments.
The conditions under which partial vs. complete remapping
occur have not yet been defined. Our experience is that even in a
given environment with a given protocol, remapping patterns
can vary, sometimes frustratingly. The likely reason is that
because remapping is the output of network computations, relatively small changes in parameters, including animal-specific variations, can affect whether the system responds to the stimuli or
not. One such parameter is probably how salient the stimuli are
initially. If a context change is made highly salient by using more
than one cue (e.g., shape combined with color), remapping will
subsequently still occur even if the context change is now restricted to just one of those cues (Wills et al., 2005). One interpretation of this is that the initially salient changes enable the
network to carve out two attractor basins after which the system
more easily settles into one of these basins when nudged by just
one of the original cues. However, stimulus salience cannot be
the only factor, because we have observed that highly salient
stimuli (as ascertained by the reaction of the rat; for example,
rearing in response to a strong odor) do not always cause remapping (Caswell Barry, unpublished data). Another factor may be
how familiar the original environment was before the contextual
Hippocampus DOI 10.1002/hipo
782
JEFFERY
change was made: in a familiar environment a change may be
surprising, perhaps attracting attention from the rat. Indeed,
there is some evidence that attention may be a factor in whether
stimuli induce place cell changes (Kentros et al., 2004).
It is worth noting here that although a distinction has been
made above between metric and contextual cues, this distinction
may not be clear-cut; it may be that metric cues can also act as
contextual cues under some circumstances. An obvious example
is the two-box-location experiment of Hayman et al. (2003), in
which place cells remapped when the recording box was translated between a north and south location. Although the change
made was a spatial one, it could be that the effect was to change
the contextual aspects of the environment. We draw this conclusion because place fields never appeared to be dragged around
within the box by the environment translations (by analogy with
how they were dragged by walls in the O’Keefe and Burgess
1996 experiment); rather, they either remained in the same location relative to the box or switched on and off. A similar spaceas-context explanation may account for the findings of Lever
et al. (2002) that changing the box from a circle to a square
caused sudden transitions between states.
Contextual Stimuli Internal to the Animal
As mentioned earlier, another kind of stimulus can induce
remapping; these are stimuli that are not properties of the physical environment per se, but rather are more abstract stimuli,
such as the task the animal is performing at a given time
(Markus et al., 1995) or which of two overlapping routes it is
currently executing (Frank et al., 2000; Wood et al., 2000; Ferbinteanu and Shapiro, 2003). These kinds of stimuli are particularly interesting because they are not detectable to an observer
and must not therefore arrive at the hippocampus via a simple
sensory route. Because these observations have been made when
the animals are performing highly repetitive tasks, some observers have considered that the salient features of the stimuli are
their sequential nature: that is, the cells are considered to be
responding to temporally organized sequences of actions, and are
thus perhaps encoding a simple form of episodic memory
(Wood et al., 2000; Shapiro et al., 2006). An alternative viewpoint, however, is that the parameters in these tasks that control
remapping are those that could be considered to be a part of the
‘‘context’’, that is, they are aspects of the environment that are
relatively stable, ongoing (albeit switching back and forth), and
define it in terms of the actions that must be performed there.
By this view, the reason the hippocampus is interested (in the
sense of responding to) in these stimuli is because they form a
part of the spatial context, or, as it were, the situation the animal
is in. In support of the latter characterization, remapping to task
parameters is a fickle phenomenon and does not always occur in
situations where the rat is clearly executing a task with sequential
components (Lenck-Santini et al., 2001; Bower et al., 2005). By
the contextual-encoding view, the fickleness arises from the likelihood that for whatever reason, some conditions favor the formation of two attractors to represent the context, and other
superficially similar ones favor only one. The finding that interHippocampus DOI 10.1002/hipo
nal factors can cause environment-specific remapping leads to
the notion that the hippocampal encoding of ‘‘place’’ is highly
multidimensional, extending far beyond the mere physical layout
and metric parameters thereof.
INTEGRATION OF CONTEXTUAL
INPUTS WITH EACH OTHER
Because ‘‘context’’ is such a multifarious phenomenon, the
question of interest regarding integration of contextual inputs is
whether this integration occurs upstream of the hippocampus,
implying a ‘‘context processor’’ somewhere in the neocortex, or
whether the place cells themselves are the site of contextual integration. This is important for understanding how the fully
fleshed-out place representation is ultimately constructed.
We explored this issue by creating multielement contexts
(each a ‘‘color’’ paired with an odor) and observing remapping
patterns to determine whether the cells would respond to the
contexts en bloc, which would suggest that the inputs had already been integrated upstream, or whether they responded to
the elements individually (Anderson and Jeffery, 2003). We
found the latter; that is, the place cells responded heterogeneously to the contexts, each responding to some unique color/
odor combination. The implication is that rather than receiving
a preprocessed, holistic context signal, the contextual information must have arrived at the place cells in elemental form, to be
combined in the hippocampus.
It seems that place cells are not hard-wired with (all of ) their
contextual inputs: these can be learned. In a pilot experiment in
which a box enclosure was moved back and forth between two
locations, place cells acquired the ability to discriminate the locations over repeated trials spanning several days (Jeffery, 2001).
This discrimination, which we take to be a contextual discrimination (for reason discussed earlier), was subsequently found to
show a second-order context specificity, inasmuch as the contextual discrimination occurring in one context (e.g., a black box)
did not transfer to another context (a white box; Fig. 5)
(Hayman et al., 2003). The context-specific behavior (where
‘‘context’’ is location) was itself context-specific (where ‘‘context’’ now means color). Our interpretation of this surprising
finding is that the inputs enabling the place cells to discriminate
contexts in the locational domain were themselves selected by
the inputs in the color domain. This implies an interaction
between the contexts – location and color – upstream of the
place cells.
INTEGRATION OF METRIC AND
CONTEXTUAL CUES
How do metric and contextual cues interact and where? Is
there a reason to think these kinds of stimuli are functionally
different?
Although both metric and contextual cues influence place
cells, the phenomenology of their responses to contextual and
INTEGRATION OF THE SENSORY INPUTS TO PLACE CELLS
metric manipulations suggests that the nature of this influence is
different. Whereas variation of metric cues (by virtue of the animal’s walking around in the environment) causes a steady
increase or decrease of firing rate as the animal moves into and
out of a place field, variation of contextual cues often causes an
abrupt, discontinuous change (‘‘remapping’’): the cells start or
stop firing or the place field jumps from one part of the environment to another. It could be argued that these different behaviors
merely reflect the different way in which metric vs. nonmetric
cues vary in the real world. An animal is able to experience
smoothly varying metric cues because it locomotes smoothly
around the environment, whereas it usually only experiences
contextual changes in a sudden way. Perhaps, if we were able to
smoothly vary contextual cues, we would see that place cells
respond smoothly in the contextual dimension too. By extension, perhaps, as far as the hippocampus is concerned, contextual
cues are no different from metric cues.
Over the past several years we have begun to explore the issue
of whether contextual and metric cues are functionally dissociable, and our impression is that they are. In the first such experiment, we looked at the remapping induced when a recording
box was changed from black to white (Jeffery and Anderson,
2003). This simple, nonmetric change induced complete remapping, with approximately half of the cells switching off or on
and the remainder developing new firing locations, as usual. The
latter class of cells, which shifted fields, was especially interesting
in this experiment because the cells were expressing different spatial coordinates depending on the box color, which enabled us to
test how they might determine which coordinates to use. The
question we asked was why did changes occur to the spatial firing
of the cells when the box was changed in color? Was this because
the cells received inputs specifying how far they should fire from
black-walls and different inputs specifying how far they should
fire from white-walls? In other words, were they receiving ‘‘colorcoded’’ metric inputs? This proposition was tested by creating
hybrid boxes in which some of the walls were black and some
white. We hypothesized that new fields should develop, having
hybrid x–y coordinates, so to speak: that is, the new fields should
be determined in the x-dimension by walls of the x-color and in
the y-dimension by walls of the y-color. Thus, the new fields in
the hybrid boxes should be predictable on the basis of knowing
the field location in each of the single-colored boxes.
This manipulation was so singularly ineffective that it was
quickly abandoned; the fact was that despite their robust remapping to complete color changes, the cells responded little to the
partial wall color changes. We made changes to the floor color
instead and found that whatever combination of wall and floor
color we used, the cells nearly always, with rare exceptions,
adopted the place field pattern appropriate to the floor, irrespective of the walls. It is as if the cells did not even receive wall-color
information. And yet, they clearly receive wall information of
some sort, because place fields will readily follow walls if the
walls are moved. The seemingly inescapable conclusion is that
place cells use walls and floor differently; they use walls as a basis
for computing distances, but the floor to tell them which environment they are in, and therefore which field to express.
783
FIGURE 6.
Model of how spatial inputs might be gated by
contextual inputs. A place cell has two sets of metric inputs, one
determining each field. Different contexts (in this case, black vs.
white) activate different sets of metric inputs. Adapted from Jeffery
and Anderson, 2003.
On the basis of these, and other, findings, we have proposed a
functional distinction between contextual and metric inputs,
whereby metric inputs (from boundaries) specify the location of
a place field in the manner proposed by Burgess and colleagues
(O’Keefe and Burgess, 1996; Hartley et al., 2000), whereas contextual inputs select which of these spatial inputs will be active
in a given environment (Fig. 6). By this view, rate remapping
can be understood as a variation in the strength of the drive
through a given set of metric inputs produced by small changes
in context, and ‘‘global’’ (or locational) remapping as when large
context changes cause one set of inputs to be silenced altogether
(its contextual modulators no longer active at all) and a new set
(possibly) activated instead.
The above scheme is an abstract formulation of the underlying
logic of the input structure. How might such a scheme be implemented in reality, particularly in light of what we have recently
learned about grid cells and their interaction with place cells? An
interesting and relevant observation is that although place cells
frequently express independent fields in two different environments, such cells tend not ever to express both fields together in
any environment. (Note that place cells frequently do have more
than one place field; however these always remap together and
behave as if they are, in effect, the same field spread across two
locations. What place cells don’t seem to express is two independently-remappable fields at once). It is almost as if two the fields
‘‘know’’ about each other and thus know not to cooccur, and
yet, how can a place field in one location be suppressed by
another field which, though potentially active in that environment, is not actually active (because the rat is not at that location
at that moment)?
A possible mechanism for this, as it were, nonlocality, lies in
the grid cells, whose rigid metric spacing means that if a grid cell
is firing in one particular place at a given moment, its firing in a
different place at a future time is constrained by whether that
location also lies on a grid peak. This constrained firing pattern
means that distant parts of the environment are, in a sense, connected via the underlying grid structure. Suppose that place fields
are formed by the summation of overlaid grids; it thus follows if
Hippocampus DOI 10.1002/hipo
784
JEFFERY
a number of grid peaks overlie each other at one location, the
places at which they next overlie each other will be constrained,
to a distance specified by the period of the largest-spacing grid.
If the grids contributing to a given place field are sufficiently
large, then the point of next summation will lie outside the recording box. Given that the smallest reported grid spacing is
around 30 cm, and most boxes are somewhere between 1 and 2
m, this seems likely. This, therefore may explain why place cells
tend to have only one field in an environment (though not
always).
How could this grid-conferred nonlocality explain remapping?
In a recent experiment, Fyhn et al. found that following manipulations that induced global remapping in place cells, grid cells
would simultaneously shift and rotate their grids in concert
(Fyhn et al., 2007). On the assumption that the shift in grids
caused the shift in fields, an explanation put forward by these
authors (see their Supplementary Fig. 12) is that any manipulation that displaced and/or rotated the grids would shift the alignment point, described above, out of the range of the box enclosure and cause the place field to disappear from inside the box.
Were that shift simultaneously to move another alignment point
into the box, the result would be an apparent shift of the field.
By this view, the reason a place cell can never express both its
fields at once is that grid cells can only express one grid at once.
Such a scheme could explain complete remapping; explaining
partial remapping is a little more complex. In order for only some
place fields to respond to contextual changes, there must be some
degree of partial selection of the grid cell inputs by the contextual
cues, either because perhaps only some grids realign following a
partial context change, or because the subset of grid cells that
drives a given place cell can be switched (gated) by context cues.
Further experiments are needed to untangle these possibilities.
Putting all this together, we can postulate that the metric of
the grid forms the metric component of our metric/contextual
gating model, and some aspect of the mapping between grid cells
and place cells corresponds to the contextual component. The
question of how contextual cues drive place cells has thus
become a new question—how do contextual cues drive grid
cells? Recordings of grid cells in multielement contexts will be
needed to answer this question.
CONCLUSION
This article has argued for the view that metric and nonmetric
inputs to place cells are functionally dissociable, with metric
inputs specifying where a cell’s field should go, and nonmetric
(or ‘‘contextual’’) inputs specifying whether or not that field
should be expressed in a given environment. Metric inputs are
integrated from information arriving in a variety of sensory
modalities, and it is likely—based on the responses of grids to
contextual, metric, and directional manipulations—that much of
this integration occurs in entorhinal cortex. Similarly, contextual
inputs are also multimodal, sometimes to a high level of abstracHippocampus DOI 10.1002/hipo
tion (such as the parameters of a given task) and again, evidence
suggests this integration may take place in entorhinal cortex.
If so much integration occurs upstream of the place cells, then
what is the function of the place cells themselves? Evidence from
grid cell recordings, such as the hippocampal inactivation study
of Bonnevie et al. (2006), suggests that one function may be to
provide the anchoring that attaches the grids to the environment.
Another function may be to sculpt the attractor basins that
underlie the processes of pattern separation and completion
postulated to occur in dentate gyrus and CA3. And finally, it
may be that place cells provide the combinatorial functions that
allow multiple different contexts to be associated with the same
set of spatial parameters (Anderson et al., 2006).
While the details of multimodal integration are rapidly being
uncovered by studies of place and grid cells, one of the big mysteries is how these neurons contribute to the other function of
the hippocampal system, episodic memory. It seems clear that
the foundation of the place and grid cell representation is a spatial one, but whether the more detailed and transient parts of a
remembered episode are incorporated at this level, or by some
other brain region, remains to be determined.
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