Figures

Border/boundary cells. The plots show heatmaps, similar to the ones in Fig. 1, from various studies showing firing of cells concentrated near boundaries in the environment, irrespective of other contextual cues and of spatial location. Top row: boundary cells recorded in the subiculum by Lever et al. (2009 adapted from Fig. 2) and more recently by Stewart et al. (2014 adapted from Fig. 2). It is clear that cells in this region respond to boundaries, firing along them. This is best demonstrated using the classical 'barrier' test, as shown in the righthand plots. These cells can also fire at a distance to boundaries, as can be seen in the second plot from the left. Second row: border cells recorded in the medial entorhinal cortex by Savelli et al. (2008 adapted from Fig. 4), Solstad et al. (2008 adapted from Fig. 1) and more recently by Bjerknes et al. (2014 adapted from Fig. 1). As in the subiculum, these cells respond to boundaries, but few respond at a distance to them. Third row: boundary cells recorded by Jankowski and O'Mara (2015 adapted from Fig. 3) in the anterior claustrum. Unlike the more classical boundary cells, these seem to respond to all boundaries in the environment. Bottom row: Weible et al. (2012 adapted from Fig. 3) demonstrated that similar 'all-boundary' responses can be observed in the anterior cingulate cortex, they termed these cells 'annulus' or 'bulls-eye' depending on whether they fired along boundaries or away from them. Jankowski et al. (2015 adapted from Fig. 4) also observed annulus cells in the nucleus reuniens of the thalamus.
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Grieves and Jeffery | The representation of space in the brain: author accepted manuscript
The representation of space in the brain
Roddy M. Grieves* and Kate J. Jeffery
University College London, Institute of Behavioural Neuroscience, Department of Experimental Psychology,
London, UK
*RMG is the corresponding author: r.grieves@ucl.ac.uk
This is an author accepted manuscript, the final version can be found at:
http://dx.doi.org/10.1016/j.beproc.2016.12.012
Behavioural Processes, Volume 135, February 2017, Pages 113-131
Abstract
Animals can navigate vast distances and often display behaviours or activities that indicate a detailed,
internal spatial representation of their surrounding environment or a ‘cognitive map’. Over a century of behavioural
research on spatial navigation in humans and animals has greatly increased our understanding of how this highly
complex feat is achieved. In turn this has inspired half a century of electrophysiological spatial navigation and
memory research which has further advanced our understanding of the brain. In particular, three functional cell
types have been suggested to underlie cognitive mapping processes; place cells, head direction cells and grid cells.
However, there are numerous other spatially modulated neurons in the brain. For a more complete understanding
of the electrophysiological systems and behavioural processes underlying spatial navigation we must also examine
these lesser understood neurons. In this review we will briefly summarise the literature surrounding place cells,
head direction cells, grid cells and the evidence that these cells collectively form the neural basis of a cognitive
map. We will then review literature covering many other spatially modulated neurons in the brain that perhaps
further augment this cognitive map.
Spatial representations in the brain
The study of how organisms are able to
navigate their environment is in many ways the study
of survival; all animals must navigate to find mates,
shelter, food and water. For wild animals this often
means navigating large expanses of land, perhaps also
with limited cues. For example, Peters (1978) reports
that in wooded areas masked by snow, wolves often
take long, complex, winding, unplanned paths when
hunting - but they can still return directly to the distant
location of their pups. Elephants have similarly been
recorded navigating distances over roughly 100 km a
month (Leggett, 2006); during these trips they
frequently visit isolated and distant waterholes
(Viljoen, 1989) despite navigating environments
devoid of any stable landmarks. Navigating large
distances is often not enough however, as animals must
also be able to navigate flexibly and efficiently.
Moustached tamarin (Saguinus mystax) and brown-
mantled tamarin (Saguinus fuscicollis) living in the
South American rainforest for instance, often move
between foraging sites in such a way as to minimise the
distance travelled between trees, even taking into
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account the timing that particular trees begin to fruit
(Garber, 1988; Janson et al., 1981; Milton and
Katharine, 1981). Efficient navigation also
encompasses being able to take unplanned routes
however. Powell (1977) for instance, reports that
fishers (Martes pennanti) have a home range through
which they have been observed taking novel, direct
paths between the nesting sites of prey animals. Often
animals are even expected to navigate in completely
unknown territory. For example, captured Burmese
pythons (Python molurus bivittatus) driven more than
30 kilometres into the everglades of South Florida have
been tracked heading directly from their displacement
site back to their home territory (Pittman et al., 2014).
Similar results have also been observed in Egyptian
fruit bats (Rousettus aegyptiacus), these animals
frequently fly large distances to forage for food and
when they are displaced from their territory by
distances of over 80 km they are able to navigate
directly back to their home cave (Tsoar et al., 2011).
The purpose of laboratory experimentation is
not to replace ethological observations such as these,
but to further advance our knowledge of how these
processes may unfold and particularly, how they may
be represented in the brain. These experiments are
always in much smaller environments than those
described above and almost always use rodents such as
rats and mice. Despite some criticism (Geva-Sagiv et
al., 2015), this approach is indeed informative about the
processes at work in larger environments and rats do
form a good basis for researching them. Experiments
have shown that, like other animals, rats can navigate
back to their home territory after being displaced (Innes
et al., 2011), and radio tracking experiments have
demonstrated that wild rats navigate considerable
distances (over half a kilometre) in search of food and
will also spend considerable time in open environments
away from cover (Taylor, 1978). This is likely an
underestimation of their navigation ability, though, as
these animals were restricted to a small territory. In
contrast, a rat released on an uninhabited island near
New Zealand navigated the entire island before settling
on a home territory spanning a hectare. Later, this same
rat swam 100m to a nearby island (Russell et al., 2005).
Having observed these complex navigational feats in
the wild, we are thus faced with the question of how
these animals, and others, navigate such large trips: as
it turns out, laboratory research suggests that the
biological processes underpinning this spatial
navigation can be very complex.
Early behaviourist psychological research into
the behaviour of animals and humans, at the start of the
last century, concentrated on the relationship between
stimuli and the responses they evoke (Hull, 1950;
Watson, 1919). It was not until forty years later that
Edward Tolman (1948) suggested the idea that there
might be an internal representation of space, which he
called a ‘cognitive map’. He based this proposal on a
wealth of experimental evidence pertaining to studies
of the processes underpinning spatial navigation in rats.
A debate ensued between those researchers in support
of this map hypothesis and those who instead embraced
the stimulus-response interpretation of behaviour,
culminating essentially in a combination of these two
schools (Restle, 1957). Thirty years after Tolman’s
influential work, John O’Keefe and Lynn Nadel (1978)
revived Tolman’s cognitive map hypothesis. This time
it was built on a firmer bedrock of behavioural
experiments, but also on tantalising
electrophysiological work conducted over the
preceding decade (O’Keefe and Dostrovsky, 1971).
This combination of behavioural and
electrophysiological research began largely with John
O’Keefe, who in 1971 demonstrated, together with
Jonathan Dostrovsky, the existence of cells in the
hippocampus of freely behaving rats that fired
preferentially for specific spatial locations in the
animal’s environment (O’Keefe and Dostrovsky,
1971). Later, in 1978, O’Keefe published a highly-cited
book with Lynn Nadel detailing how these ‘place cells’
could form the neural basis of a spatial representation
in the brain, or a ‘cognitive map,’ and went on to
elucidate the characteristics of these cells (O’Keefe and
Nadel, 1978). A few years later a second class of cell,
first reported by James Ranck Jr. in 1984, and heavily
researched since the 1990s by Jeffrey Taube and
colleagues, was demonstrated to respond purely to the
direction an animal is facing, in an almost compass-like
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manner (Taube et al., 1990a, 1990b). This seemed to
further support O’Keefe and Nadel’s proposal about a
mapping function for the hippocampal system. Most
recently, in 2005, May-Britt and Edvard Moser,
together with Marianne Fyhn, Torkel Hafting and
colleagues, published findings demonstrating the
existence of cells that fire in a continually repeating
hexagonal grid of fields across the surface of an
animal’s environment (Fyhn et al 2004; Hafting et al.,
2005). Many of the properties of these ‘grid cells’ have
since been explored, and their relationship to place cells
is a complex one (Rowland et al., 2016), but it seems
likely that at least one of their functions is to provide
metric information about how far the animal has been
walking and in what direction. This confirmation of the
truly spatial nature of the entorhinal-hippocampal
system led to the award of the 2014 Nobel Prize in
Physiology or Medicine to O’Keefe and the Mosers for
their discovery of “a positioning system in the brain.”
Thus, rather than fulfilling different niches,
experimental psychology and cognitive neuroscience
have always been intertwined, each building on the
progress of the other. Now we know that many of the
highly complex behavioural processes observed in
humans and animals reflect an equally complex world
of cellular processes, which have observable
electrophysiological correlates. The three major spatial
cell types described above appear to form the backbone
of a complex system that we are only just beginning to
understand. However, in the ensuing decades since
place cells were discovered, a number of other cell
types have been encountered, both cortically and
subcortically, that may also contribute to spatial
encoding. Many of these are less well described and
less well known than the 'big three’, but may be no less
important. In this review we aim to concentrate on
these, with the hope that illuminating them may provide
insight into how the brain organizes its cognitive
representation of space. We will focus largely on the
functional aspects of these neurons, for a more detailed
review of the anatomical aspects see Knierim (2006).
First, however, we review the hippocampus and place,
head direction and grid cells.
The hippocampus
Initial interest in the hippocampus was sparked
by the observed impact of hippocampal damage, both
accidental and intentional, on behaviour. One of the
most influential cases in this respect was that of a
patient, Henry Molaison, known for many years only
as patient H.M., who suffered from epileptic seizures
which were found to arise from structures in his medial
temporal lobe. In 1953 surgeons removed, bilaterally,
H.M.’s hippocampal formation and a number of
adjacent structures (Corkin et al., 1997; Penfield and
Wilder, 1958). Tragically, but of great interest to many
researchers, after this surgery H.M. was afflicted with
severe anterograde amnesia (Gabrieli et al., 1988;
Scoville and Milner, 1957) which stayed with him until
his death in 2008. During his life, H.M.’s long-term
memory and language skills were largely unaffected
(Kensinger et al., 2001), however, he was unable to
form new episodic memories. It was subsequently also
discovered that he could not use a physical map to
navigate in unfamiliar surroundings (Corkin, 2013,
1984) and was impaired on a number of spatial tasks
(Corkin, 1979; Morris, 1999; Teuber and Weinstein,
1956) for a review see (Corkin, 1984, 2002 or 2013).
This finding implicated the hippocampus as a structure
which is crucial for the formation of new memories,
including (it later transpired) those that are spatial in
nature. Subsequent to H.M.’s affliction, interest grew
in whether the hippocampus is related to the formation,
consolidation or retrieval of these memories, a question
that is still not fully resolved.
Motivated by the findings of H.M. and other
similar cases, animal researchers began to search for
animal models of hippocampal amnesia, resulting in
the development by Olton and colleagues of the radial
maze, which was initially developed as a test of
working memory (Olton et al., 1980). Using the radial
maze, Olton and colleagues showed that the task is
highly dependent on the hippocampus (Olton et al.,
1978). While this could have suggested a role for
working memory in episodic memory formation, in fact
O’Keefe and Nadel, motivated by the findings
described in the next section, suggested that the
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hippocampal dependence could be due to the spatial
nature of the task (O’Keefe and Nadel, 1978). The
debate between working-memory and spatial-map
explanations for the effects of hippocampal lesions was
finally settled when Richard Morris developed the
water-maze (Morris, 1984), and used it to show that the
hippocampus was necessary to solve the task even
though it is mostly spatial and has no working memory
component (Morris et al., 1982).
Place cells
The excitement generated by the case report of
patient H.M. led researchers to embark upon
Figure 1 Recording of the three first-discovered spatial cell types. A, A typical experimental setup for recording single neurons from freely
exploring rats implanted with chronically indwelling electrodes. B, Data from a single hippocampal place cell in a single, 4-min recording
trial. The left plot shows the action potentials (“spikes” - red squares) superimposed on the cumulative path of the rat across the 4 mins. Note
that the spikes mostly occurred in the northeast part of the environment. The right plot shows the same data depicted, as it commonly is, as
a heat plot of dwell-time-adjusted firing rate (see colour bar) between peak (100%) and 20% (peak). Action potentials and dwell time are
binned, smoothed and divided to give a spatial map of the cell’s firing rate as a function of spatial location. The patch of spatially localised
firing is known as a place field, or firing field. C, Two trials recorded from a post-subicular head direction cell, in a symmetrical apparatus
having a single polarising landmark. Each polar plot shows firing rate as a function of head direction. In the left plot, when the landmark is
to the north, the cell fires maximally when the rat’s head faces south. In the right plot, the landmark was rotated to the east when the rat was
not in the compartment - now, when the rat returns, the cell fires to the west, maintaining the same relationship to the landmark. This shows
that the cells are influenced by local cues and not by geocentric ones such as the Earth’s magnetic field. D, Recording of an entorhinal grid
cell, firing depicted as in A. Note that instead of a single region of spiking, the cell spikes in multiple places that form a close-packed
hexagonal array, like the one shown in the inset. The constant spacing between firing fields is characteristic of a given cell and is relatively
constant in different environments, leading to suggestions that these cells function to encode distances.
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electrophysiological investigations of the
hippocampus. When O’Keefe and Dostrovsky (1971)
took advantage of a newly developed technique to
record single, complex spiking (Fox and Ranck, 1975;
Ranck, 1973), pyramidal (Henze et al., 2000) neurons
in the rat hippocampus (Fig. 1A and Fig. 2) they found
that the firing rate of many of these cells was modulated
purely by spatial location (Fig. 1B), and named them
‘place cells’. Similar place-encoding neurons were
subsequently found in several other species including
mice (Rotenberg et al., 1996), bats (Ulanovsky and
Moss, 2007), monkeys (Cahusac, Miyashita and Rolls,
1989; Rolls et al., 1998) and humans (Ekstrom et al.,
2003), suggesting a certain generality of the
phenomenon, although place cells have not been
reported yet in non-mammals.
Place cells were tremendously exciting
because they revealed the formation, by the brain, of an
abstract, cognitive representation. Their properties
have led to a great deal of theorizing about how
mammalian brains might construct a representation of
space. Place cells recorded in exploring rats fire
maximally when the rat’s head is in one specific region
of the environment, regardless of which way it is facing
(and therefore what it can see). This area of high firing
rate is known as the cell’s ‘place field’ (O’Keefe, 1979;
O’Keefe and Conway, 1978; O’Keefe and Nadel,
1978): typically, firing outside of the place field is
absent. Place cells recorded simultaneously, and
therefore near to each other in the brain, often have
place fields in different areas of an environment,
suggesting that the population of cells as a whole
represent the entire surface of an environment
(O’Keefe, 1976; Wilson and McNaughton, 1994).
Furthermore, once the representation of an
environment has formed it is stable across days (Hill,
1978; Muller et al., 1987) and even weeks (Thompson
and Best, 1990), although recent evidence suggests that
not all place cells are always stable (Mankin et al.,
2015; Ziv et al., 2013) possibly for interesting reasons
to do with charting the passage of time.
What makes place cells fire where they do?
Visual information is important, as the location of a
place cell’s place field is often influenced by the distal
cues or landmarks surrounding the environment
(Muller and Kubie, 1987; O’Keefe and Conway, 1978;
Yoganarasimha and Knierim, 2005). However, place
cells still fire in the same locations in the dark provided
the rat remains in the apparatus (Save, Nerad, &
Poucet, 2000; Zhang, Schönfeld, Wiskott, & Manahan-
Vaughan, 2014; Markus et al., 1994; Quirk et al., 1990)
and blind rats also have relatively stable place fields
(Save, Cressant, Thinus-Blanc, & Poucet, 1998). This
indicates that the place cell representation can be built
in the total absence of visual information provided the
availability of other sensory cues such as olfaction and
tactility. We now know that these different sensory
modalities are integrated both within and outside the
hippocampus (Jeffery, 2007). Thus, place fields
represent higher order constructs assembled from more
primitive spatial ones such as direction, boundaries,
and self-motion information (see below). If the
environment is altered or completely novel, the cells
may completely change their firing relationship and
represent this environment in a unique way (Anderson
and Jeffery, 2003; O’Keefe and Conway, 1978); this
process of place field alteration between different
environments is known as ‘remapping’ (Muller and
Kubie, 1987). Together, these results further support
the idea that place cells may underlie spatial navigation
and memory; place cells as a population uniquely
represent entire environments and these specific
representations are recalled whenever the animal
encounters the environment in the future.
Although, as mentioned above, place cells
have been found in several species, it remains unclear
how much of what has been discovered in rats and mice
is universal, even within mammals. Bats, whose
ancestral lineage diverged from that of rodents around
65 million years ago, have well-formed place fields that
resemble rodent ones in most important respects
(Ulanovsky and Moss, 2007). However, the situation
for primates appears to be slightly different. Humans
and other primates primarily use visual cues, such as
landmarks, when navigating (Ekstrom, 2015) and
neuroimaging studies implicate the hippocampus and
the parahippocampal region in human navigation
(Aguirre et al., 1996; Maguire et al., 1998; Spiers and
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Barry, 2015). However, in humans and other primates,
spatially modulated cells appear to make up the
minority of cells in these structures. Initial reports
suggested that cells in the primate hippocampus might
instead respond to objects (Eifuku et al., 1995; Rolls,
2005; Rolls et al., 1989), whole-body motion (O’Mara,
Rolls, Berthoz and Kesner, 1994) or relate to the
direction of ‘spatial view’ where the animal is
looking, rather than where it is; (Rolls, 1999; Rolls et
al., 1997; Rolls and O’Mara, 1995). In humans only
around 11-25% of neurons in the hippocampus and
parahippocampal regions appear to respond purely to
spatial location, the majority of these being in the
hippocampus (Ekstrom et al., 2003; Miller et al., 2013).
However, most cells in that region of the temporal lobe
encode many aspects of space conjunctively,
combining features such as current location, current
view, current spatial goal or heading direction
(Ekstrom et al., 2003; Miller et al., 2013). Spatial view
cells have not been observed in rodents (but see de
Araujo et al., 2001) perhaps because rodents have
particularly poor eyesight or because they explore
environments directly rather than visually. Place cells
in rats are unable to form a stable representation for an
environment unless it has been directly explored
(Rowland et al., 2011) which suggests that these
animals may not have an inferred allocentric
representation for remote space, although recent
research suggests that observing a conspecific explore
a novel environment can improve the stability of place
cell representations when later exploring the same
environment (Mou and Ji, 2016). Instead, rats may have
the reverse representation; in the rodent superior
colliculus (Cooper et al., 1998) and area V1 of the
visual cortex (Haggerty and Ji, 2015) cells fire in
spatial locations where visual cues appear the same.
The firing of the latter cells seems to lead place cells
temporally, suggesting that the information from these
may guide the activity of place cells. Thus, rats
probably use remote visual cues to inform the spatial
activity of place cells, whereas the activity of spatial
view cells carries information about the remote visual
cues being observed.
Head direction cells
The discovery of head direction (HD) cells
came about in the aftermath of the original place cell
report, when researchers were still trying to understand
the source of their spatial firing. Ranck, Jr. (1985,
1984) reported finding, in the dorsal presubiculum of
the rat, cells that were modulated by the facing
direction of the head, and a detailed description of the
activity of these ‘head direction’ cells was published
shortly after (Taube et al., 1990a, 1990b, 1987). Head
direction cells fire maximally when an animal’s head
faces a particular direction in the azimuthal (horizontal)
plane (Fig. 1C and Fig. 2), this ‘preferred direction’ is
independent of the animal’s current behaviour or
position. Different head direction cells have different
preferred firing directions, equally distributed, such
that as a population there is equal representation of all
directions, and no overall preferred direction (Taube et
al., 1990b). Like place cells, the firing of head direction
cells has been shown to rely on the angular position of
environmental cues (Goodridge and Taube, 1995;
Taube, 1995a; Taube et al., 1990b; Zugaro et al., 2000)
if these cues are stable (Knierim et al., 1995), but such
cues are not necessary (Mizumori and Williams, 1993;
Yoder et al., 2011). As with place cells, if distal cues
are rotated or if the animal is moved between
environments then the preferred firing direction of all
head direction cells realign or rotate (Fig. 1C), and they
do this coherently (Skaggs et al., 1995; Yoganarasimha
and Knierim, 2005).
Head direction cells are found in a
comparatively greater number of brain regions than
grid cells and place cells, and these structures seem to
essentially comprise the classic Papez limbic circuit
(Taube, 1998), originally thought to be an emotion
circuit and then later a memory one. In rats the head
direction ‘signal’ is thought to initially arise within the
brainstem in the dorsal tegmental nuclei (DTN) and
lateral mammillary nuclei (LMN), where neurons
sensitive to angular head velocity can also be found.
From here, the signal projects to the anterior dorsal
thalamus (ADN) and then to a variety of regions in
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thalamus and cortex. Regions in the thalamus include
antero-dorsal, lateral dorsal and reuniens nuclei, and
cortical regions include postsubiculum, entorhinal and
retrosplenial cortices, parasubiculum, posterior parietal
cortex (see Fig. 8 for a schematic and Yoder et al., 2011
for a review). In other species, these cells have also
been observed in the primate presubiculum (Robertson
et al., 1999), drosophila central complex (Seelig and
Jayaraman, 2015) and there is some evidence of
directionally sensitive neurons in the human entorhinal
cortex (Jacobs et al., 2010), retrosplenial cortex and
thalamus (Shine et al., 2016).
Grid cells
Unlike place cells in the hippocampus, many
cells in the mEC fire in multiple discrete and regularly
spaced locations which form a triangular lattice or
tessellated grid (Fig. 1D). These ‘grid cells’ are found
close to the border between the mEC and postrhinal
cortex (Fyhn et al., 2004; Hafting et al., 2005) and in
the pre- and parasubiculum (Boccara et al., 2010)(Fig.
2). Like place cells, the firing of grid cells is partially
dictated by external cues; when distal landmarks are
rotated, grid cell firing fields rotate by a corresponding
amount (Hafting et al., 2005), and deformation of the
environment often causes partial deformation of the
grid (Barry et al., 2007; Stensola et al., 2012).
However, unlike with place cells, in new environments
the firing of different grid cells remains coordinated,
such that the grid patterns of grid cells rotate and move
together, maintaining a stable relationship (Fyhn et al.,
2007). This has led to suggestions that grid cells
function cooperatively, by virtue of an interconnected
matrix known as an attractor network (McNaughton et
al., 2006).
Grid cells do not appear to depend on
extramaze cues to maintain a stable firing pattern. In
complete darkness the grid pattern persists undisturbed,
provided (as with place cells) the rat has not been
disoriented; this has led many to suggest that grid cells
may be involved in the computations underlying self-
motion calculation, also known as ‘path integration’
(Fuhs and Touretzky, 2006; Fyhn et al., 2007; Hafting
et al., 2005; McNaughton et al., 2006). This idea is
supported by the finding that lesions of the mEC result
in path integration impairments (Allen et al., 2014; Van
Cauter et al., 2013). Given their mathematical and
geometric properties it is almost hard to believe these
cells exist at all: however, they are not confined to the
Figure 2 Example cells and a graphic representation of their
anatomical distribution in the rat brain. A, left, the firing rate heat
map of a place cell (adapted from Grieves et al., 2016a) recorded as
a rat explored a circular arena. Middle, an example head direction
cell firing rate plot (adapted from Bjerknes et al., 2015). These
‘polar’ plots show the action potentials emitted by a cell, binned in
terms of the animal’s head direction at the time and divided by the
amount of time spent facing that direction overall. This cell fires at
a high rate when the animal is facing to the north east of the
environment: this direction is the cell’s ‘preferred firing direction’.
Right, an example firing rate map of a grid cell (Casali and Jeffery,
2015), this is produced using the same method as for the place cell.
Multiple firing fields can be observed which form a triangular or
hexagonal grid that spans the environment. B, a graphic
representation of the location these cells occupy in the rat brain
(white outline). Black lines highlight the region where each cell was
discovered but they may be found in multiple regions. Brain regions
are denoted by abbreviations, these are: HPC = hippocampus; Sub
= subiculum, RSC = retrosplenial cortex; PrS = presubiculum; PaS
= parasubiculum; mEC = medial entorhinal cortex; lEC = lateral
entorhinal cortex; PFC = prefrontal cortex; OFC = orbitofrontal
cortex.
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rat brain and are also found in mice (Fyhn et al., 2008)
and have recently been observed in bats (Yartsev et al.,
2011) and possibly also in humans (Jacobs et al., 2013).
Evidence for their existence in humans has also been
demonstrated using functional magnetic resonance
imaging techniques (fMRI) (Doeller et al., 2010). The
function and origin of these cells is still not greatly
understood. However, their activity is likely informed
by two other cell types found in the mEC; speed cells,
which encode the movement velocity of the animal
(Kropff et al., 2015), and a recent report of ‘band cells’
(Krupic et al., 2012 but see; Navratilova et al., 2016 and
Krupic et al., 2015) which fire in discrete bands of
activity and may aid in the formation of the hexagonal
grids of grid cells.
Place, head direction and grid cells have turned
out to be only the beginning of what promises to be a
large array of diverse spatially modulated neurons,
some of which have clear real-world correlates but
many of whose properties are obscure, or ‘conjunctive’
(combine several types of information). We turn next
to some of these other cells, to see how their properties
could contribute to the building of a cognitive map, but
first we look at a class of neuron found throughout the
brain, whose firing is more spatially diffuse, and yet
seem to have a critical role to play in spatial
computations: interneurons.
Interneurons
Interneurons are a morphologically diverse
class of typically high firing-rate neurons that use the
inhibitory transmitter gamma-Aminobutyric acid
(GABA). They usually form local networks of neurons
that mostly control their neighbours via short-range,
inhibitory connections, and for a long time were
thought only to have the relatively uninteresting role of
modulating local network activity so as to prevent
runaway excitation and epileptic seizures. It is now
thought that some of them may have a much more
computationally interesting role.
In each of the brain structures associated with
spatial navigation there tend to be a minority (around
10% of all neurons, for example, in CA1 region of the
hippocampus) of high firing neurons which are
spatially insensitive (Freund and Buzsáki, 1998), and
these are typically interneurons. When recording in the
CA1 region of the hippocampus of awake animals,
these cells are usually treated as a single class and are
often ignored, on the basis that they are modulatory
rather than signal-carrying. However, hippocampal
interneurons have been suggested to consist of at least
21 distinct types (Klausberger and Somogyi, 2008;
Somogyi and Klausberger, 2005), each having specific
temporal relationships to the local field potential
oscillatory signal known as theta rhythm, and so it
seems increasingly likely that they have an important
computational role. It has become clear that
understanding or modelling neural networks will be
impossible without an understanding of these
intervening cell types (Sik et al., 1997).This has been
examined in two domains - space and time.
We begin with spatial information. Although
initial reports described interneurons as having no
discernible spatial properties (Christian and
Deadwyler, 1986), Kubie, Muller and Bostock (1990)
showed that hippocampal interneurons do have a coarse
but repeatable spatial specificity. This is relatively
stable and even rotates with a visual cue like the place
fields of place cells. Similar results were also reported
in different apparatus (Ego-Stengel et al., 2007; Frank
et al., 2001; McNaughton et al., 1983). Wilent and Nitz
(2007) for example not only confirmed that the firing
of interneurons is spatially modulated, but that this
firing can be as informative as place cells. They also
observed that some interneurons exhibit ‘off fields’, or
an area of significantly lower firing, that resembles an
inverted place field; indeed the authors suggest that this
firing may result from interneuron and pyramidal cell
interactions.
Is the information carried by interneuron firing
more than might be expected from the combined input
of multiple place cells? Marshall et al. (2002) found
that stimulation of a place cell intracellularly can
modulate the firing of monosynaptically connected
interneurons and that the spatial specificity of these
interneurons is often the result of place cell inputs,
suggesting that this may not be the case. However,
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Hangya, Muller and Czurko (2010) simultaneously
recorded pairs of interneurons and CA1 place cells and
found that even monosynaptically connected neurons
could have both similar and distinct spatial
representations. These results provide evidence that the
spatial specificity of hippocampal interneurons is
greater than could be expected based purely on their
inputs from place cells. It is the case however that in
many correlated cell pairs, interneurons have an off
field, as reported by Wilent and Nitz (2007), that
corresponds to the location of a connected place cell’s
place field. This suggests that although interneurons
may possess spatial specificity of their own, a
subpopulation of these cells are probably involved with
place cell firing in an inhibitory or excitatory way.
The other important domain in which
interneurons have been extensively investigated is in
control of the timing of activity in networks.
Klausberger and Somogyi (2008) found fine-grained
relationships between interneuron activity and theta
rhythm, suggesting that the coordinated activity of
spatial neurons might be regulated by an orchestra of
interneurons, organised by a central conductor, the
forebrain structure known as the medial septum. The
medial septum probably controls the timing of theta
rhythm via long-range inhibitory projections into
hippocampus (Melzer et al., 2012). Timing is
potentially important for spatial coding because space
and time are linked by velocity, since travel at a certain
velocity for a certain time translates a subject by a
particular distance. It may be that one function of theta
rhythm, which varies in frequency with running speed
(Jeewajee et al., 2008; McFarland et al., 1975), is to
convey a speed signal into the hippocampal formation.
The question of whether this is due to explicit encoding
of running speed or is a byproduct of the sensory drive
associated with increased speed of movement through
the sensory world has been debated. However,
stimulation of the medial septum, which drives the
theta rhythm in hippocampus (Vertes and Kocsis,
1997) directly affects running speed (Fuhrmann et al.,
2015), and altering theta frequency artificially has been
shown to lead to a change in the running speed of mice
(Bender et al., 2015). These results suggest a specific
locomotor role for theta, though whether this is on the
sensory/encoding side as well as the motor side remains
to be determined.
As well as the local field potential, cells that
are modulated by running speed are common
throughout the brain, but again, this might be due to the
increased rate of sensory drive rather than a functional
role in speed encoding per se. Place cells for example
show a considerable amount of modulation by running
speed (Czurkó et al., 1999; Kubie et al., 1990;
McNaughton et al., 1996, 1983; O’Keefe et al., 1998;
Rivas et al., 1996; Sharp et al., 1990; Whishaw, 1998;
Wiener et al., 1989; Zhang et al., 1998); they oscillate
at a higher frequency and emit more spikes per theta
cycle (Geisler et al., 2007). In the medial entorhinal
cortex, some of the grid cells in the deeper layers are
modulated by speed as well as space - they are known
as ‘conjunctive’ (Sargolini et al., 2006; Wills et al.,
2012). Primary visual neurons are also - surprisingly -
modulated by running speed (Niell and Stryker, 2010;
Saleem et al., 2013). However, it has also been
suspected that some cells in the brain might code more
purely for speed. For example, O’Keefe reported an
observation in hippocampus of a solitary ‘speed cell’
(O’Keefe et al., 1998) whose activity was linearly
related to running speed and could be decoupled from
effort, but attempts to find the source of this signal were
not successful for many years. Recently, however,
Kropff et al reported that a large proportion of neurons
in mEC are tuned solely for running speed (Kropff et
al., 2015). These cells are thought to be interneurons.
Putting this all together, a function for hippocampal
interneurons in regulating spatial processing might be
to provide a speed signal that the system can use to
update the self-localization signal. One influential
hypothesis for how this could be done is the oscillatory
interference model first suggested by O’Keefe and
Recce (1993) and subsequently elaborated by Burgess,
O’Keefe and colleagues into a model that could also
account for grid cell grids (Burgess et al., 2007).
The above ideas aside, the function of
interneurons is still little understood, but it seems clear
that the brain has not created such a vast array of
neuronal cell types for trivial reasons. The next few
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years should see major advances in our understanding
of these ubiquitous neurons.
Extrahippocampal place cells
This review is primarily concerned with spatial
representations in the brain other than the ‘big three’
cell types, the simplest examples of which are perhaps
neurons analogous to place cells in brain areas outside
the hippocampus (HPC). For example; both Quirk et al.
(1992) and Hargreaves et al. (2005) reported finding
cells resembling hippocampal place cells in the medial
entorhinal cortex (mEC): the same structure where grid
cells can predominantly be found (Fig. 3). However, it
is not known if these cells were truly place-specific
units like those in the hippocampus or merely the
isolated vertices of a grid cell’s firing structure (Savelli
et al., 2008). Indeed, Hargreaves et al. (2005) were also
able to record a grid cell, suggesting that their
electrodes were at least in a location where they can be
found. Nonetheless, more recent studies have
confirmed the existence of what appear to be spatially
modulated non-grid cells which resemble hippocampal
place cells in the mEC (Savelli et al., 2008) as well as
spatially modulated cells with multiple firing fields that
do not seem to conform to the traditional grid cell
classification (Krupic et al., 2012; Latuske et al., 2015;
Pérez-Escobar et al., 2016; Zhang et al., 2013),
especially in the deeper layers of the structure (Tang et
al., 2015).
Spatial correlates have also been observed in
the striatum, a structure closely connected and
compared to the hippocampus (discussed later). A
small number of cells here appear to encode the head
direction (Mizumori et al., 2000; Ragozzino et al.,
2001; Wiener, 1993) or relative location of the animal
(Wiener, 1993), but more recent studies have failed to
find such spatial encoding (Berke et al., 2009) instead
suggesting that neurons here encode specific motor
responses or behaviours which may themselves occur
in a specific spatial location. Nonetheless, the activity
of proposed striatal location-sensitive neurons closely
follows that of hippocampal cells (Yeshenko et al.,
2004). Although, the striatum is thought to utilise
contextual information differently to the hippocampus
as cells there remap more when the lights are switched
off during a navigation task (Mizumori et al., 2000).
However, this is not the case when visual cues are
simply reorganised, suggesting that these cells may
integrate visual information or that they are more
sensitive to the navigation strategy the animal is using
(Shibata et al., 2001; Yeshenko et al., 2004). In any
case, the evidence of true place encoding anywhere
near the sensitivity of the hippocampus appears to be
lacking in the striatum, this is compounded by the fact
that the few reports of spatial activity there always
involve a spatial task where animals execute specific
responses for specific rewards, both of which we know
are important correlates of striatal activity.
The medial septum has received considerable
attention recently, mainly because inhibiting activity
there disrupts entorhinal theta modulation (Jeffery et
al., 1995) and grid cell firing (Brandon et al., 2011;
Koenig et al., 2011). However, cells in the nearby
lateral septum (LS) are reported to have spatial
characteristics similar to those of place cells (Leutgeb
and Mizumori, 2002; Ryoi et al., 1998; Zhou et al.,
1999)(Fig. 3). In contrast to place cells however, these
cells initially form orthogonal representations for
different environments but later represent them
similarly, perhaps indicating a role in pattern
completion processes. Evidence suggests that spatially
modulated neurons may also reside in the primate
septal area (Kita et al., 1995; Nishijo et al., 1997).
However, damage specifically to the LS does not seem
to impact behaviour (Bland and Bland, 1986; Oddie et
al., 1996) and so the function of these cells is still
largely unknown.
Some cells in the subiculum have similarly
been likened to hippocampal place cells (Sharp, 2006,
1997)(Fig. 3) but these seem to be more responsive to
environment boundaries (Stewart et al., 2014), opening
the possibility that these cells were boundary vector
cells (discussed later). In any case, spatially modulated
cells in the subiculum certainly seem to have much less
spatial selectivity than hippocampal place cells
(Anderson and O’Mara, 2004; Barnes et al., 1990;
Martin and Ono, 2000) and may instead often encode a
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combination of positional, directional and speed
information (Muller et al., 1991; Sharp and Green,
1994). This finding, coupled with the fact that the firing
of these cells is relatively stable in complete darkness
(Brotons-Mas et al., 2010), might mean that these cells
are more involved in path integration processes than the
specific place representations of hippocampal place
cells.
Jankowski et al. (2015), however, report the
observation of much more spatially selective units,
resembling hippocampal place cells, in the rostral
thalamus (parataenial, anteromedial, and nucleus
reuniens), an area associated with episodic memory in
humans (Clarke et al., 1994; Mitchell et al., 2014; Van
der Werf et al., 2003) and long term memory in rats
(Loureiro et al., 2012)(Fig. 3). Mink et al. (1983) also
previously reported individual neurons that seemingly
responded to spatial correlates in the anteromedial
thalamus. These cells are stable within a session, but it
is unknown if they maintain a consistent representation
over time or if they remap between environments or
contexts. Further characterisation will be needed before
these cells can be fully compared to hippocampal place
units (Jankowski et al., 2015). Similarly, Jankowski
and O’Mara (2015) report the existence of spatially
modulated units resembling hippocampal place cells in
the anterior claustrum (Fig. 3). However, these
represent a low proportion of the cells recorded there
(4.3%) and like many subicular cells some of them are
directionally modulated, suggesting a different role to
hippocampal place cells that is perhaps more closely
related to visual processes.
Deshmukh and Knierim (2011) report the
existence of units with spatial fields resembling those
of place cells in the lateral entorhinal cortex (lEC):
however, these cells require the presence of objects
within the environment and are not well spatially
modulated when objects are not present (Deshmukh
and Knierim, 2011; Hargreaves et al., 2005;
Yoganarasimha et al., 2010). These cells may thus
provide hippocampal place cells with non-spatial
information (Deshmukh and Knierim, 2013; Manns
and Howard, 2006) rather than form a unique spatial
representation outside the hippocampus. In rodents, the
postrhinal cortex has been suggested to fulfil a similar
role to the parahippocampal area in primates (Burwell,
Witter and Amaral, 1995) which is associated with
processing spatial scenes (Epstein and Kanwisher,
1998). In rodents its connectivity supports processing
Figure 3 Place cells outside the hippocampus. The plots show
heatmaps, similar to the ones in Figure 1, from various studies
demonstrating the existence of highly spatially tuned cells
outwith the hippocampus. Top row: Sharp et al. (2006
adapted from figure 3) reported observing cells with spatial
characteristics very similar to hippocampal place cells in the
subiculum, place cells have perhaps also been observed in the
medial entorhinal cortex (Quirk et al., 1992 adapted from
figure 2) and anterior claustrum (Jankowski and O’Mara,
2015 adapted from figure 1). Middle row: Jankowski and
O’Mara (2015 adapted from figure 1) found evidence for
spatially selective cells throughout the rostral thalamus
(Jankowski et al., 2015 adapted from figure 2). Bottom row:
Spatially selective cells have also been observed in the lateral
septum (Leutgeb and Mizumori, 2002 adapted from figure 3;
Zhou et al., 1999 adapted from figure 4).
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both spatial and non-spatial information (Whitlock,
Sutherland, Witter, Moser and Moser, 2008) and within
the structure cells have been observed to fire in a
spatially modulated manner. However, these
representations are not stable (Fyhn, Molden, Witter,
Moser and Moser, 2004) and do not rotate predictably
when visual cues are rotated, but have instead been
suggested to represent an initial step in the progression
towards true place selectivity (Burwell and Hafeman,
2003). In another cortical structure, Lipton, Alvarez
and Eichenbaum (1999) report location-sensitive
neurons in the orbitofrontal cortex (OFC). The firing of
many of these cells (72% of recorded neurons)
discriminated multiple odour ports based on their
location. These cells did not usually encode purely
location as many of them would also modulate their
firing rate based on the behaviour of the rat or the phase
of the task, suggesting that they may be integrating
visuospatial processing and behavior. Further results
from Feierstein et al. (2006) suggest that pure location-
dependent firing is unlikely in the OFC and that cells
there may have a locomotor rather than a purely spatial
representation. Thus we see that the precise spatial
modulation of the hippocampal place cell may not be
common outside the hippocampus.
Boundary/border cells
Perhaps the relatively unique spatial
representation in the hippocampus is due to the variety
of spatial inputs that project there. For example,
another widely researched, but often overlooked cell
type responds purely to environmental boundaries (Fig.
4) - these cells have a complex relationship with both
place and grid cells that is still not greatly understood.
Early observations demonstrated that hippocampal
place cell firing often appears to be determined, at least
partly, by the geometric constraints of an environment.
By elongating a square environment into a rectangle,
place fields which were previously small and round
were seen to stretch in response to the wall changes,
becoming long and distended in the same dimension,
albeit by a smaller amount (O’Keefe and Burgess,
1996). This led a number of researchers to formulate a
model of place cell firing which employed a class of
cells known as Boundary Vector Cells (BVCs): these
cells were predicted to fire in relation to environmental
boundaries, with place cell firing arising as a result of
a threshold sum activity of a subpopulation of these
BVCs (Barry et al., 2006; Burgess et al., 1997; Hartley
et al., 2000).
Initial reports suggested that cells in the
subicular formation of the rat may be responsive to
environment boundaries (Sharp, 1997; Sharp and
Green, 1994) albeit with a weak overall spatial
modulation. Later studies have since revealed cells
which at least partially fit the description of the
hypothesised BVCs, in areas as diverse as the
subiculum (Barry et al., 2006), presubiculum and
parasubiculum (Boccara et al., 2010), mEC (Bjerknes
et al., 2014; Savelli et al., 2008; Solstad et al., 2008)
and recently in the anterior claustrum (Jankowski and
O’Mara, 2015) and rostral thalamus (Jankowski et al.,
2015)(Fig. 4). These cells have a preferred firing
direction, much like head direction cells, but instead of
firing maximally when the animal’s head is facing this
direction a BVC will fire when the animal encounters
an environmental boundary in that direction from the
animal. This firing is driven by the memory of the
boundary’s position relative to the animal, based on
self-motion information and not simply by perceptual
cues (Lever et al., 2009; Raudies et al., 2012; Raudies
and Hasselmo, 2012). This consistent firing is observed
in every environment in which the cell is observed, if
the animal’s sense of direction remains the same in
each (Lever et al., 2009; Sharp, 1997). Environmental
boundaries which can drive BVC firing in this way may
be walls, low ridges or vertical drops and the colour,
texture or odour of these does not seem to influence the
cell’s firing (Lever et al., 2009).
Within this group of spatially modulated
neurons there is a great diversity of different types.
Initial models predicted that different cells would
respond to different distances from boundaries.
However, border cells in the mEC seem to respond to
boundaries not more than 10 cm away (Bjerknes et al.,
2014; Solstad et al., 2008); the same seems to be true
of boundary cells in the claustrum (Jankowski and
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O’Mara, 2015). Subicular boundary vector cells, in
contrast, can be observed to have fields distant from
their preferred boundary (Lever et al., 2009) suggesting
that these two types of cell may form discrete
populations. Furthermore, Stewart et al. (2014) report
the existence of ‘boundary-off’ cells in the subiculum.
These cells fire in a way that resembles an inverted
boundary cell, with activity covering an environment
except for an area near one particular boundary. Stewart
et al. (2014) propose that these cells may play a distinct
role in navigation, suggesting that they may form
another separate class of boundary cells. So far, all of
these boundary and border cells do not direct their
activity (or lack of activity) to all boundaries in all
directions: they are selective only for those found at a
specific distance from the animal and at a particular
allocentric direction. However, so called ‘perimeter’ or
‘annulus’ cells in the rostral thalamus (nucleus reuniens
and anteromedial thalamus)(Jankowski et al., 2015),
anterior claustrum (Jankowski and O’Mara, 2015),
mEC (Solstad et al., 2008) and mouse anterior
cingulate cortex (Weible et al., 2012) break even this
rule and fire along all environment boundaries (Fig. 4).
These cells are also accompanied by corresponding
boundary off counterparts that fire only in the centre of
an apparatus (Weible et al., 2009). These cells may
mark yet another distinct population, perhaps forming
a precursor to boundary cells that is generally active
near all boundaries.
Object cells
Recognising if a stimulus is novel or familiar
is often crucial to an animal’s survival and this is true
as much of object recognition as it is of recognising an
immediate threat - confusing a slice of pizza for your
hat may result in a short period of embarrassment but
it's easy to see how faulty or non-existent object
recognition such as this could escalate quickly to be a
life or death matter, especially for animals.
Furthermore, the recognition and memory of objects
may underlie features of episodic and ‘episodic like’
memory in humans and animals: it would be difficult
to forget the aforementioned pizza incident but it would
Figure 4 Border/boundary cells. The plots show heatmaps, similar
to the ones in Figure 1, from various studies showing firing of cells
concentrated near boundaries in the environment, irrespective of
other contextual cues and of spatial location. Top row: boundary
cells recorded in the subiculum by Lever et al. (2009 adapted from
figure 2) and more recently by Stewart et al. (2014 adapted from
figure 2). It is clear that cells in this region respond to boundaries,
firing along them. This is best demonstrated using the classical
‘barrier’ test, as shown in the righthand plots. These cells can also
fire at a distance to boundaries, as can be seen in the second plot
from the left. Second row: border cells recorded in the medial
entorhinal cortex by Savelli et al. (2008 adapted from figure 4),
Solstad et al. (2008 adapted from figure 1) and more recently by
Bjerknes et al. (2014 adapted from figure 1). As in the subiculum,
these cells respond to boundaries, but few respond at a distance to
them. Third row: boundary cells recorded by Jankowski and
O’Mara (2015 adapted from figure 3) in the anterior claustrum.
Unlike the more classical boundary cells, these seem to respond to
all boundaries in the environment. Bottom row: Weible et al. (2012
adapted from figure 3) demonstrated that similar ‘all-boundary’
responses can be observed in the anterior cingulate cortex, they
termed these cells ‘annulus’ or ‘bulls-eye’ depending on whether
they fired along boundaries or away from them. Jankowski et al.
(2015 adapted from figure 4) also observed annulus cells in the
nucleus reuniens of the thalamus.
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likewise be impossible to remember without also
having access to a memory for the associated objects.
Indeed, a more recent view of the hippocampus is that
it is involved in integrating a variety of stimuli in order
to potentially lay the framework for the creation of
episodic or episodic like memory. In this view, memory
is comprised of spatial (‘where’) and nonspatial
(‘what’) information (Knierim et al., 2014) of which
objects comprise a small part. The hippocampus is
known to be involved in processes far beyond purely
spatial navigation (Eichenbaum et al., 1999) including
the discrimination of non-spatial cues such as objects
(Clark et al., 2000; Fortin et al., 2004). For instance
place cells in the hippocampus have been shown to
respond to the spatial location of objects (Lenck-
Santini, Rivard, Muller and Poucet, 2005) so this
information is certainly being utilised by the
hippocampus and thus possibly also in the formation of
episodic memory. It likely originates in structures
outside the hippocampus however; as nonspatial
information is thought to progress through the
perirhinal cortex and lateral entorhinal cortices before
entering the hippocampus (Deshmukh et al., 2012;
Witter et al., 2000; Witter and Amaral, 1991). We will
review some electrophysiological evidence of object
related activity outside the hippocampus, paying
particular attention to those representations that are still
spatial in nature.
It is important to remember that cells which are
more active around or near specific objects are not
necessarily spatially modulated, these neurons may be
encoding visual or tactile information (Burke et al.,
2012) or novelty (Wan et al., 1999; Zhu et al., 1995).
Upon analysis this phenomenon may appear spatial, but
this is merely because the objects occupy a specific
spatial location (see O’Keefe, 1999 for a similar
discussion) for an example see the dissociation between
representations in the perirhinal and lateral entorhinal
cortex (Deshmukh et al., 2012). However, a number of
studies have highlighted cells that also possess spatial
characteristics which we will discuss here. For
instance, about 5.5% of cells in anterior claustrum
show a sensitivity to objects: this firing begins
immediately upon exploring the object and dissipates
as soon as it is removed (Jankowski and O’Mara,
2015)(Fig. 5). Firing persists in darkness or if the object
is replaced with a different one, meaning that it does
not simply represent visual, texture or other sensory
features. Rather, these cells seem to encode the spatial
location of objects in the environment. In the absence
of objects some of these cells are also spatially
modulated and resemble hippocampal place cells
suggesting that they are encoding spatial information
Figure 5 Object cells. These plots show heatmaps, similar to the
ones in Figure 1, from various studies showing the firing of cells
related to objects in the environment. Top row: object sensitive
cells recorded in the lateral entorhinal cortex by Deshmukh and
Knierim (2011 adapted from figure 3) and Tsao et al. (2013 adapted
from figure 1). These cells fire near to objects placed in the
environment, regardless of their identity. As can be seen in the right
example, these cells do not typically show spatially selectivity in
the absence of objects. Middle row: two example cells recorded by
Jankowski and O’Mara (2015 adapted from figure 4) in the anterior
claustrum. In the absence of objects these cells are somewhat
spatially modulated, but when objects are added these cells show
very specific firing around them. Bottom row: two cells recorded
by Weible et al. (2012 adapted from figure 5) in the mouse anterior
cingulate cortex. Cells in this brain region are also sensitive to the
presence of objects, firing around them. However, some cells, as in
the left example, instead fire in an area marking the absence of a
previous object.
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about objects when they are present rather than their
identity.
Similarly, lateral entorhinal cortex (lEC) cells
are not well spatially modulated in a blank environment
(Deshmukh and Knierim, 2011; Hargreaves et al.,
2005) or even in an environment where many distal
cues are provided (Yoganarasimha et al., 2010):
however, when objects are placed inside the
environment many cells show activity resembling the
object-sensitive cells in the claustrum (Deshmukh and
Knierim, 2011). In contrast to those cells though, units
in the lEC often maintain their object-related firing
when the object is removed, providing evidence of a
spatial memory for the position of objects (Tsao et al.,
2013)(Fig. 5). In support of this spatial view, some of
these neurons are sensitive only to specific objects or
fire in the position of a moved or missing object
(Deshmukh and Knierim, 2011; Tsao et al., 2013):
these object memory ‘trace responses’ were not seen in
the claustrum cells. However, units which show the
same trace response activity have been reported in
lateral entorhinal cortex (Vandrey and Ainge, 2016)
and can also be found in the mouse anterior cingulate
cortex (ACC) where cells respond to the precise spatial
location of a missing object for up to 30 days after
initial exposure to the environment (Weible et al., 2012,
2009)(Fig. 5). One interpretation of these findings is
that, as discussed above, these brain regions are
involved in conveying object/place associations to the
hippocampus. Certainly, lesions of the lEC result in
object recognition impairments (Wilson et al., 2013).
Goal cells
Similar to object recognition, the
representation of spatial and nonspatial goals is a
fundamental requirement of survival. Rarely do we
navigate without a specific goal in mind, even if it is
only a subgoal on a longer journey or a simple place of
regular interest such as the supermarket. Without a
representation of the supermarket however you would
need to walk aimlessly until randomly finding it every
time you needed to buy milk. It is true that rats do not
need to buy milk very often, but they do have to find
food, potential mates and shelter. Thus, having a
representation of these spatial goals can allow them to
navigate quickly, efficiently, safely and flexibly
whenever necessary. However, finding a representation
of spatial goals in the brain has proven difficult and
initial reports suggested that even the highly integrative
spatial map provided by place cells does not encode this
information (Hölscher et al., 2003; Siegel et al., 2008;
Speakman and O’Keefe, 1990). Some studies
suggested that the firing of place cells at the beginning
of a complex maze may be related to the future goal of
the animal and that this reflected the animal’s
anticipation of this location (Ferbinteanu et al., 2003;
Frank et al., 2001; Wood et al., 2000), however, recent
evidence suggests that this firing is actually related to
the animal’s future trajectory, not the goal (Grieves et
al., 2016b). Other evidence suggests that place fields
may subtly shift towards spatial goals causing an
overrepresentation of that location (Hollup et al., 2001;
Kobayashi et al., 2003; Dupret et al., 2010) or that some
place cells may fire preferentially just before reward
attainment (Eichenbaum et al., 1987). However, these
goal representations are not as specific as one might
expect or hope for. Early models actually predicted the
existence of ‘goal cells’ which would modulate their
firing rate based on an animal's distance from its current
goal (Burgess et al., 1993; Burgess and O’Keefe, 1996;
Pfeifer, 1998). These cells, which were thought to
perhaps reside in the subiculum, could be used to
navigate efficiently towards a goal location if the
environment is not too complex. This seems like a
much more attractive way to represent spatial goals and
some cells in frontal-cortical regions may play a
comparable role.
Despite direct hippocampal input (Jay and
Witter, 1991), cells in the prelimbic area of the medial
prefrontal cortex (mPFC) do not typically encode
location (Gemmell et al., 2002; Ito et al., 2015; Jung et
al., 1998; Poucet, 1997) though they may encode
contextual information (Euston and McNaughton,
2006; Hyman et al., 2012; Ito et al., 2015; Jung et al.,
1998). However, using a novel open field task where
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rats activate the availability of a food reward from a
location separate to the one where they actually
consume the reward (Rossier et al., 2000), Hok et al.
(2005) found evidence of a quasi-spatial representation
in the prelimbic/infralimbic areas of the mPFC (Poucet
et al., 2004)(Fig. 6). Specifically, many units there
increased their activity in the location of a spatial goal,
despite this being spatially distinct from the location of
the reward. This representation perhaps informs
hippocampal activity - where similar goal related firing
can also be observed in place cell firing (Hok et al.,
2007; Poucet et al., 2004). Although inactivation of the
mPFC does not disrupt goal firing in place cells (Hok
et al., 2013), so the reverse flow of information may be
true. These cells have very large firing fields located on
or near the goal location, characteristics which are both
important features of the aforementioned goal cells
(Burgess and O’Keefe, 1996). However, further
research will be needed to clarify whether mPFC cells
do hold a spatial representation of the goal or if they are
merely representing task-relevant stimuli (Hagler and
Sereno, 2006) or goal-based action selection
(Matsumoto, 2003) in a spatial task, thus just giving the
appearance of a spatial response.
Looking at the nearby obitofrontal cortex
(OFC); initial evidence from primates suggested that
this structure may encode the economic significance of
different behavioral outcomes (Padoa-Schioppa et al.,
2006). Indeed, recent evidence in rats also suggests that
the OFC may encode expectations or realisations about
rewards (Steiner and Redish, 2012). In terms of a
spatial representation, cells seem to simply modulate
their firing in relation to spatial goals, perhaps giving
the appearance of spatial coding (Steiner and Redish,
2012; Stott and Redish, 2014). However, cells in the
OFC may be spatially responsive to odour-place
associations (Feierstein et al., 2006; Lipton et al., 1999)
and their firing may also be related to future goal
locations and response directions (Feierstein et al.,
2006).
Another possibility is that already discovered
cells actually fulfil this role. It has been proposed for
instance that the firing of grid cells in the medial
entorhinal cortex could be translated into a gradient
signalling the distance to a spatial goal (Stemmler et al.,
2015), but this signal may not be represented itself by
a specific cell. Alternatively, we may not need to stray
out into distal cortical regions in order to find a goal
representation at all; more recent fMRI data suggest
that there may be such a representation in the human
hippocampus (Howard et al., 2014) but direct
electrophysiological evidence in any animal of such a
signal has remained elusive. Preliminary evidence from
research on bats, however, may provide a concrete
example of such a representation; these animals appear
to have neurons that encode the precise spatial location
and allocentric direction of a goal even when it is
obscured (Sarel et al., 2015). How this representation is
formed, utilised or adapted is still to be determined;
likewise if a similar representation is to be found in the
human or rat brain is still unknown.
Figure 6 Goal cells. These plots show heatmaps, similar to the
ones in Figure 1 but with a slightly different colormap that
ranges from orange (no firing) to purple (maximum firing).
These four medial prefrontal cortex cells were recorded by Hok
et al. (2005 adapted from figure 3) and demonstrate a clear
field of activity surrounding the goal zone. This zone was where
rats had to wait to trigger the release of food and is thus
dissociated from the location of actual food consumption.
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Conjunctive cells
So far we have seen examples of cells with
specific representations of space, however, many cells
in the brain do not form a pure representation such as
this, especially those cells in structures associated with
integrative processes. Many cells encode a number of
environmental, spatial or behavioural features
simultaneously and are thus termed ‘conjunctive cells’.
Concentrating on those cells that conjunctively encode
spatial features, many of these are found in the medial
entorhinal cortex, but there are also examples in other
brain regions too. Theta-modulated place-by-direction
(TPD) cells, for example, have been observed in the rat
presubiculum and parasubiculum. These cells fire in
specific spatial locations but only when the animal is
facing in a particular direction (Cacucci et al.,
2004)(Fig 7). Similar cells have also been observed in
the parasubiculum (Taube, 1995b) and retrosplenial
cortex (Alexander and Nitz, 2015; Cho and Sharp,
2001; Vedder et al., 2016)(Fig 7), there they are
thought to integrate this information with self-motion
information. Many cells in the medial entorhinal cortex
(mEC) respond conjunctively to direction, location and
running speed; for instance some grid cells may also
exhibit head direction correlates (Sargolini et al., 2006)
Figure 7 Conjunctive cells. These plots show heatmaps, similar to the ones in Figure 1, from various studies showing the multi-modal
or conjunctive firing of different cells. Next to these are shown directional ‘polar’ plots as described in Figure 1. Top row: conjunctive
cells in the medial entorhinal cortex (mEC); Tang et al. (2015 adapted from figure S3) report that the activity of boundary cells may also
be directional, like that of head direction cells. Latuske et al. (2015 adapted from figure 5) similarly report that some head direction cells
can show a strong spatial modulation, much like place cells. Sargolini et al. (2006 adapted from figure 3 and S7) also demonstrated that
many grid cells also show directional modulation similarly to head direction cells, further strengthening the view that these cells may be
involved in processing self-motion information. Bottom row: spatially responsive cells can be observed in the retrosplenial cortex (RSC)
and these cells often also have directional correlates (Cho and Sharp, 2001; adapted from Jacob et al., 2016). Cacucci et al. (2004 adapted
from figure 2) observed cells in the pre- and parasubiculum that were sensitive to the heading direction of the animal but which also
showed strong spatial selectivity.
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Grieves and Jeffery | The representation of space in the brain: author accepted manuscript
or head direction cells may exhibit strong spatial
modulation (Tang et al., 2014)(Fig 7). Indeed, these
conjunctive cell types were being used in artificial
navigation modules before they were discovered and
may be necessary for efficiently computing current
location (Milford et al., 2006, 2010) suggesting that
these cells may fulfil an important role in spatial
navigation processes.
Movement- or action-sensitive cells
So far in this review we have focused on those
cells that encode an animal’s spatial location: however,
movement through space is a critical part of spatial
processing, and many cells in the brain encode
movement, or encode space conjunctively with
movement. Self-motion information can be used to
estimate spatial location through path integration
processes if a known reference point is provided
(Mittelstaedt and Mittelstaedt, 1980). As such, self-
motion information is generally concerned with cues
internally generated, which makes it a relatively
simpler form of information. However, given the vast
number of possible internal cues that these cells may
encode and the fact that, as we saw earlier, many
encode a conjunction of stimuli, these representations
can be difficult to recognise or examine. Nevertheless,
a number of brain structures and cells have been
identified that reliably encode such features.
As mentioned above, early studies found that
place cells conjunctively encode velocity as well as
place (McNaughton et al., 1983), and studies of head
direction cells have similarly found modulation by
movements including angular head velocity (Chen et
al., 1994; Sharp, 1996). Additionally, head direction
cells in some regions show anticipatory firing that was
originally suggested to reflect a motor efference signal
(Blair et al., 1997; Blair and Sharp, 1995); however, it
still occurs during passive movements (Bassett et al.,
2005), and may reflect vestibular information about
predicted head direction instead. Movement sensitivity
is found in many other regions however. One such
structure is the striatum, the activity of which is often
compared and contrasted to that of the hippocampus.
Whereas the hippocampus is thought to underlie the
encoding of flexible, perhaps rare or unlikely
experiences (Eichenbaum, 2004), the striatum appears
to be more closely involved with repetitive experiences
(Berke, 2009). In evidence of this, when animals are
trained on a task that allows only the use of body-
referenced (egocentric) or world-referenced
(allocentric) information, inactivation of the striatum
tends to increase the use of a hippocampal-dependent
‘place’ strategy, whereas inactivation of the
hippocampus tends to increase the use of a striatum-
dependent ‘response’ strategy (Packard and McGaugh,
1996). This makes the striatum an important structure
for processing task-relevant action information which
is often similar if not identical in repetitive tasks; it also
implicates the striatum in the formation of automated
responses or habits (Jog et al., 1999; for a review see
Yin and Knowlton, 2006). Consistent with this, many
studies reveal that the activity of striatal neurons is
related to specific body movements in both primates
(Alexander and DeLong, 1985; Schultz and Romo,
1988) and rats (Gardiner and Kitai, 1992; West et al.,
1990). In behavioural tasks, striatal neurons also seem
to encode task-relevant information such as rewards
(van der Meer et al., 2010; van der Meer and Redish,
2009), task phase (Barnes et al., 2005; Kermadi and
Joseph, 1995) or response sequences (Berke et al.,
2009; Jin and Costa, 2010).
Strikingly, many striatal cells fire
preferentially when the animal is making a particular
movement at a particular location in an environment,
such as turning left in a T-maze (Thorn et al., 2010).
This is a pattern that has been seen elsewhere too:
neurons which respond to specific response sequences
can also be found in the posterior parietal cortex (PPC)
(McNaughton et al., 1989; McNaughton et al., 1989;
Chen et al. 1994). Neurons here are often modulated by
head direction (Chen et al., 1994; Whitlock et al.,
2008), but cells are also modulated by other factors,
perhaps related to the time since the last response was
made, distance to a goal or the animal’s allocentric
spatial location (Nitz, 2006; Nitz, 2012). More recent
research suggests that these cells may represent a phase
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in the process required to transform egocentric
information into an allocentric reference frame. By this
view, they actually encode the egocentric position of a
cue (Wilber et al., 2014). In support of this, previous
studies identified that rats with posterior parietal cortex
lesions suffered a strange inability to orient themselves
towards a cue (Kolb et al., 1994) and rats are generally
impaired in spatial tasks after lesions to this area -
especially if those tasks require using proximal visual
cues (Kolb et al., 1994, 1987; Save et al., 1992). A
similar integrative process has been suggested to take
place in retrosplenial cortex (RSC), where neurons can
be found that encode both response sequence and
spatial location (Alexander and Nitz, 2015). These cells
were recorded in a stereotyped maze environment
meaning that the location specificity may be due to
similar visual scenes rather than location, however, in
open field experiments conjunctive representations of
spatial location and head direction have also been
observed (Jacob et al., 2016) making this less likely.
Recent reports also suggest that cells may reside in the
retrosplenial cortex (Jacob et al., 2016, 2014) and
nearby subiculum (Olson et al., 2016) that encode a
conjunction of visual and head direction information
(Fig 7), in the case of the subiculum these cells have
been suggested to represent an animal’s allocentric
direction of travel.
Conclusion
The study of the neural encoding of space
began with the discovery (albeit spread across 30 years)
of a triumvirate of spatially modulated neurons: the
place cells, head direction cells and grid cells. Studies
of the network in which these neurons are embedded
has revealed many more cells that have firing
properties relevant to spatial encoding (See Fig. 8 for a
summary “wiring diagram”). Some of the response
profiles are conjunctive, making activity sometimes
difficult to decipher, and it seems likely that the “big
three” cell types represent nodes of unusually
decipherable (by scientists at least) representatives of a
diverse array of cell types, many of which will only be
understood using complex analytical methods.
We have seen that spatial tuning outside of the
hippocampus and medial entorhinal cortex is not
particularly unusual. Why might these spatial
representations exist in structures outside the
hippocampus at all? Do they receive their spatial inputs
from place cells in the hippocampus? Or are these
representations independent of that signal? The
answers to these questions are probably complex. We
do know that the hippocampus represents a melting pot
of spatial and non-spatial inputs, which in themselves
must come from outside the structure. We know this
because where other spatial signals such as that carried
by the head direction system and grid cell system can
be easily disrupted by lesions, the spatial representation
of the hippocampus continues almost perpetually, even
in the absence of both of these signals. This suggests
that activity in the hippocampus is the result of multiple
inputs and can adapt to a loss of a substantial amount
before degrading. However, this fine-tuned spatial
signal is likely also exported to other structures that are
either associated with spatial navigation processes or
involved in integrating it with other information. To
compound the matter, many of these projections are
likely to be reciprocal, so that the output of these
structures will also be found in the hippocampus.
Untangling this dense web of interconnections and
shared spatial responses is a slow process that began
almost forty years ago and will require still a great deal
of sophisticated electrophysiology and concomitant
behavioural investigation.
Future research
We have covered many different cell types so
far discovered in a variety of different brain regions,
but the question of how spatial cognition is supported
is far from resolved. For instance, interneurons are
spread throughout the brain and as we have seen here,
may contribute significantly to the spatial modulation
of many other cell types (Hangya, Muller and Czurko,
2010). Neural network models, deep learning projects
and major collaborative research such as the Human
Brain Project all require data concerning the activity
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profiles of neurons in different brain regions. Yet,
electrophysiology research has yet to tackle this large
population of diverse neurons found throughout the
brain, in part because it is often hard to find real-world
correlates of neuronal activity, and we do not yet have
a full suite of analytic tools with which to describe and
interpret more complex firing patterns. Advanced brain
research will require a fuller understanding of these
neurons and the role they fulfil (Sik et al., 1997).
Figure 8 Schematic diagram concentrating on the brain regions and cell types discussed in this review. Place cells can be found in
the hippocampus, nucleus reuniens (NRe), parataenial nucleus (PT), anteromedial nucleus (AM), claustrum, medial entorhinal cortex and
subiculum. Place correlates (i.e. weak spatial activity) can be found in the orbitofrontal cortex (OFC), postrhinal cortex, lateral entorhinal
cortex and lateral septum. Grid cells can be found in the medial entorhinal cortex, pre- and parasubiculum. Head direction cells can be found
in the lateral mamillary nuclei (LMN), anterodorsal nuclei (ADN), laterodorsal nuclei (LDN), retrosplenial cortex (RSC), postsubiculum,
nucleus reuniens and anteromedial nucleus (AM). Boundary cells can be found in the parasubiculum, claustrum, subiculum, anterior cingulate
cortex, pre- and parasubiculum and medial entorhinal cortex. Object sensitive cells can be found in the lateral entorhinal cortex, postrhinal
cortex, orbitofrontal cortex (OFC) and the lateral septum. Goal cells can be found in the medial prefrontal cortex (mPFC) and prelimbic and
infralimbic regions of the prefrontal cortex. Self-motion or egocentric cells such as those encoding running speed or angular head velocity can
be found in the mEC, striatum, RSC, PPC, LMN and DTN. For a more in depth review of the connectivity of the anterior thalamic nuclei see
Jankowski et al. (2013) or Aggleton and Nelson (2015). For the mammillary bodies see Dillingham, Frizzati, Nelson and Vann (2015). For the
circuit between hippocampus, mPFC and NRe see Vertes, Hoover, Szigeti-Buck and Leranth (2007) or Griffin (2015). For the entorhinal cortex
see Canto, Wouterlood and Witter (2008). For hippocampal, subicular pre- and parasubicular connectivity see van Strien, Cappaert and Witter
(2009).
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Even for cells that do have discernible real-
world correlates, it is often unclear what inputs many
spatial neurons are sensitive to. For instance, what
constitutes an object, and why do object sensitive cells
fire at or around certain items in the environment but
not others? What makes these items different to walls
or textures on the floor? For a better understanding of
brain networks and how neurons process information
we must know in greater detail what specific features
these cells are attending to or if they represent a model
of a more complex representation.
It is similarly unclear what constitutes a
boundary. Are boundary sensitive neurons representing
a physical barrier? Research suggests that they respond
to vertical drops (Lever et al., 2009), which is an
absence of support rather than the presence of a barrier,
so perhaps boundary cells instead represent simple
linear features of an environment? However the cells
do not seem to be sensitive to patterns, textured
flooring or changes in ground colour, suggesting that
they instead represent something more meaningful to
the animal such as impediments to locomotion
(Stewart, 2014). Are boundary cells also sensitive to
virtual or otherwise non-physical barriers, such as a
purely acoustic boundary (e.g., Hayman et al 2008)?
Future research will be needed to clarify these
relationships. It is also unknown whether the boundary
cells found in the subiculum (Lever et al., 2009), mEC
(Bjerknes et al., 2014), anterior claustrum (Jankowski
and O’Mara, 2015), rostral thalamus (Jankowski et al.,
2015) and anterior cingulate cortex (ACC)(Weible et
al., 2012) are related or if they represent divergent
features. Perimeter cells in some of these regions,
which fire along all boundaries, and their ‘boundary
off’ counterpart which fire away from all boundaries,
certainly seem to satisfy a different function and
perhaps precede the formation of traditional boundary
cells, but this is unknown.
Another major, unsolved question concerns the
representation of spatial goals in the brain. Research
shows that in many cases animals plan what they want
to do in terms of trajectories (Grieves et al., 2016a) and
continuously recall entire trajectories through space
(Pfeiffer and Foster, 2013). However, there is evidence
of goal encoding in the prefrontal cortex (Hok et al.,
2005) and the circuit this forms with the hippocampus
(Ito et al., 2015). Recent data also suggest that some
animals may possess a constantly updating goal vector
that can be observed in the activity of single neurons
(Sarel et al., 2015) or in the activity of whole brain
regions (Howard et al., 2014). Finding out how goals
are stored and retrieved, and the steps that lead to action
planning, will be a major task for the coming period and
will need to include areas of the brain outside the
hippocampus, in addition to hippocampus itself.
Indeed, one conclusion that should be drawn from the
examples described in this review is that many
fascinating functions of the brain and of brain networks
reside outside the most widely researched brain
regions. The next generation of behaviourists and
neuroscientists will certainly work together to better
elucidate the form and function of different brain
structures and the diverse neural specificity that awaits
there.
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Acknowledgments
This work was supported by a grant from the Wellcome Trust (103896AIA) to KJ.
... Cognitive representation of space is sustained by the spiking activity of "spatially tuned" neurons, such as hippocampal place cells, head direction cells, parietal cells, border cells, and others [1,2]. A particularly curious pattern of activity is exhibited by the grid cells in the rats' Medial Entorhinal Cortex (MEC) that fire in compact domains centered at the vertexes of a triangular lattice, tiling the navigated environment [3] (Fig. 1A). ...
... Second, the "empirical" size of a field is defined by the lengths of the typical paths that run though it, rather than the field's diameter. A simple correction to (2) can hence be obtained by replacing the diameter D g with the length of an average chord cutting through the field,l g = πD g /4 [10,11], which yields ξ g 2/π (Fig. 1B). ...
... The results discussed above were obtained by modeling the rat's moves through the firing fields in the observed environment-an approach that helps visualizing the grid cells' spiking patterns, but may not directly capture the organization of the underlying network computations [17,18]. Understanding the latter requires placing the grid cells' activity into the context of the brain's own representation of the environment-the cognitive map, encoded, inter alia, by the place cell and the head direction cell networks [1,2]. The computational units enabling this representation are the functionally interconnected groups of hippocampal place cells, c i [19,20], ...
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Grid cells play a principal role in enabling mammalian cognitive representations of ambient environments. The key property of these cells -- the regular arrangement of their firing fields -- is commonly viewed as means for establishing spatial scales or encoding specific locations. However, using grid cells' spiking outputs for deducing spatial orderliness proves to be a strenuous task, due to fairly irregular activation patterns triggered by the animal's sporadic visits to the grid fields. The following discussion addresses statistical mechanisms enabling emergent regularity of grid cell firing activity, from the perspective of percolation theory. In particular, it is shown that the range of neurophysiological parameters required for spiking percolation phenomena matches experimental data, which points at biological viability of the percolation approach and casts a new light on the role of grid cells in organizing the hippocampal map.
... proposal is the existence of 'place cells' in the hippocampus that show spatially localised activity patterns linked to boundaries and landmarks in an environment (O'Keefe & Dostrovsky, 1971). This was followed by the discovery of a variety of other spatial coding cells supporting navigation (see Grieves and Jeffery, 2017 for review). Given the ubiquity of spatial representation in the hippocampus and neighbouring parahippocampal structures, several essential questions arise: 1) How is information used during flexible navigation, as suggested by the hypothesis of the cognitive map?; 2) What information does the hippocampus code for downstream regions during navigation?; and 3) What contributions might other regions of the brain's navigation systems, such as the dorsal striatum, have for flexible navigation? ...
... Even more recently, cells encoding a boundary vector were discovered in the subiculum of freely moving rats (Lever et al., 2009), and in the entorhinal cortex (Savelli et al., 2008;Solstad et al., 2008), amongst other regions (Grieves and Jeffery, 2017). These cells fire when the animal is positioned at a preferred distance from an environmental boundary. ...
... Information necessary for spatial navigation is encoded and processed in a wide variety of brain regions in humans and rodents (Epstein et al., 2017;Grieves and Jeffery, 2017). The specific (or algorithmic) ways in which this spatial information is processed, manipulated, and transmitted throughout cortical and subcortical brain regions and finally used in behaviour, remains undiscovered. ...
Thesis
Animals and humans are remarkable in their ability to flexibly adapt to changes in their surroundings. Navigational flexibility may take many forms and in this thesis we investigate its neural and behavioral underpinnings using a variety of methods and tasks tailored to each specific research aim. These methods include functional resonance magnetic imaging (fMRI), freely moving virtual reality, desktop virtual reality, large-scale online testing, and computational modelling. First, we reanalysed previously collected rodent data in the lab to better under- stand behavioural bias that may occur during goal-directed navigation tasks. Based on finding some biases we designed a new approach of simulating results on maze configurations prior to data collection to select the ideal mazes for our task. In a parallel line of methods development, we designed a freely moving navigation task using large-scale wireless virtual reality in a 10x10 space. We compared human behaviour to that of a select number of reinforcement learning agents to investigate the feasibility of computational modelling approaches to freely moving behaviour. Second, we further developed our new approach of simulating results on maze configuration to design a novel spatial navigation task used in a parallel experiment in both rats and humans. We report the human findings using desktop virtual reality and fMRI. We identified a network of regions including hippocampal, caudate nu- cleus, and lateral orbitofrontal cortex involvement in learning hidden goal locations. We also identified a positive correlation between Euclidean goal distance and brain activity in the caudate nucleus during ongoing navigation. Third, we developed a large online testing paradigm to investigate the role of home environment on wayfinding ability. We extended previous reports that street network complexity is beneficial in improving wayfinding ability as measured using a previously reported virtual navigation game, Sea Hero Quest, as well as in a novel virtual navigation game, City Hero Quest. We also report results of a navigational strategies questionnaire that highlights differences of growing up inside and outside cities in the United States and how this relates to wayfinding ability. Fourth, we investigate route planning in a group of expert navigators, licensed London taxi drivers. We designed a novel mental route planning task, probing 120 different routes throughout the extensive street network of London. We find hip- pocampal and retrosplenial involvement in route planning. We also identify the frontopolar cortex as one of several brain regions parametrically modulated by plan- ning demand. Lastly, I summarize the findings from these studies and how they all come to provide different insights into our remarkable ability to flexibly adapt to naviga- tional challenges in our environment.
... From there, neuroscience research on spatial memory and cognitive map formation in rodents has advanced using single-cell recordings. Through this technique, several types of neurons have been discovered that are active in relation to the environment an animal is currently in (Grieves and Jeffery, 2017;Moser et al., 2008), such as place cells, grid cells, boundary cells, and head direction cells. These cells form the neural basis of cognitive maps (Grieves and Jeffery, 2017). ...
... Through this technique, several types of neurons have been discovered that are active in relation to the environment an animal is currently in (Grieves and Jeffery, 2017;Moser et al., 2008), such as place cells, grid cells, boundary cells, and head direction cells. These cells form the neural basis of cognitive maps (Grieves and Jeffery, 2017). Place cells are located in the hippocampus and fire when the animal is in one specific location in an environment (Moser et al., 2008;O'Keefe, 1976). ...
... Grid cells in the entorhinal cortex are also active in specific locations, but these have multiple firing fields that form a hexadirectional grid covering the environment (Hafting et al., 2005;Moser et al., 2008). Head direction cells have been found in several (sub) cortical areas, and are active when the head of the animal points in a certain direction (Grieves and Jeffery, 2017). The combined signalling of these cells contribute to the animal's spatial orientation, and to the integration of spatial information into a cognitive map of the environment. ...
Article
For efficient navigation, the brain needs to adequately represent the environment in a cognitive map. In this review, we sought to give an overview of literature about cognitive map formation based on non-visual modalities in persons with blindness (PWBs) and sighted persons. The review is focused on the auditory and haptic modalities, including research that combines multiple modalities and real-world navigation. Furthermore, we addressed implications of route and survey representations. Taking together, PWBs as well as sighted persons can build up cognitive maps based on non-visual modalities, although the accuracy sometime somewhat differs between PWBs and sighted persons. We provide some speculations on how to deploy information from different modalities to support cognitive map formation. Furthermore, PWBs and sighted persons seem to be able to construct route as well as survey representations. PWBs can experience difficulties building up a survey representation, but this is not always the case, and research suggests that they can acquire this ability with sufficient spatial information or training. We discuss possible explanations of these inconsistencies.
... Such extrahippocampal spatial signals may be important for contextualizing sensory and motor signals experienced in different locations (Flossmann and Rochefort, 2021;Teyler and DiScenna, 1986). However, in some cases this activity does not unequivocally encode position but may represent location-specific sensory cues, motor signals or rewards (Grieves and Jeffery, 2016;Knierim, 2006;Peyrache and Duszkiewicz, 2021). For this reason, we performed experiments in total darkness, and we used the LNP model to test whether position-tuned RSC neurons and their afferent inputs respond to tactile cues or motor variables such as speed, acceleration, or lick rate (Hardcastle et al., 2017). ...
... However, hippocampal place cells also provide an allocentric (world-centered) code for position in freely moving animals exploring a twodimensional arena (Wang et al., 2020). Such an allocentric code has not been unequivocally established in circuits outside the hippocampus (Grieves and Jeffery, 2016;Knierim, 2006;Peyrache and Duszkiewicz, 2021). Therefore, it remains to be determined whether our observations also apply to an allocentric spatial code. ...
Preprint
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Neuronal signals encoding the animal's position, originally discovered in the hippocampus, widely modulate neocortical processing. While it is assumed that these signals depend on hippocampal output, their origin has not been investigated directly. Here, we asked which brain region sends position information to the retrosplenial cortex (RSC), a key circuit for navigation and memory. Using two-photon axonal imaging in head-fixed mice performing a spatial task, we performed a comprehensive functional characterization of long-range inputs to agranular RSC. Surprisingly, most long-range pathways convey position information, but with key differences. We found that axons from the secondary motor cortex transmit the most position information. By contrast, axons from the posterior parietal- anterior cingulate- and orbitofrontal cortex and thalamus convey substantially less position information. Axons from the primary- and secondary visual cortex make a negligible contribution. These data show that RSC is a node in a widely distributed ensemble of networks that share position information in a projection-specific manner.
... Hence, understanding the neural basis supporting spatial and social behavior is of great significance. A generally accepted mechanism is that the surrounding environment is represented by cognitive maps in the brains of animals [2][3][4] and humans 5,6 with the capacity to integrate social and contextual spatial information 7,8 . The neural basis of a cognitive map is collectively formed by multiple spatially tuned cells, such as place cells 9 in the hippocampus and grid cells 10,11 in the MEC. ...
Article
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Grid cells with stable hexagonal firing patterns in the medial entorhinal cortex (MEC) carry the vital function of serving as a metric for the surrounding environment. Whether this mechanism processes only spatial information or involves nonspatial information remains elusive. Here, we fabricated an MEC-shaped microelectrode array (MEA) to detect the variation in neural spikes and local field potentials of the MEC when rats forage in a square enclosure with a planar, three-dimensional object and social landmarks in sequence. The results showed that grid cells exhibited rate remapping under social conditions in which spike firing fields closer to the social landmark had a higher firing rate. Furthermore, global remapping showed that hexagonal firing patterns were rotated and scaled when the planar landmark was replaced with object and social landmarks. In addition, when grid cells were activated, the local field potentials were dominated by the theta band (5–8 Hz), and spike phase locking was observed at troughs of theta oscillations. Our results suggest the pattern separation mechanism of grid cells in which the spatial firing structure and firing rate respond to spatial and social information, respectively, which may provide new insights into how the brain creates a cognitive map.
... 50 A central goal in neuroscience is to explain how cognitive and behavioral phenomena arise from the collective activity of populations of neurons and the specific circuit and cellular mechanisms that shape that activity, down to the single cell level. The rodent hippocampus, and the systems it is a part of, has been a productive area of research in this respect, giving birth to compelling theories about, for instance, how the activity of place cells, head direction cells and grid cells may 55 support spatial memory (Epstein et al., 2017;Grieves and Jeffery, 2017;Moser et al., 2015), or how theta phase precession and "replay" in hippocampal neurons implements the rapid encoding and subsequent retrieval of episodic-like memories (Buzsáki, 1989;Foster and Knierim, 2012). ...
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The hippocampus is thought to enable the encoding and retrieval of ongoing experience, the organization of that experience into structured representations like contexts, maps, and schemas, and the use of these structures to plan for the future. A central goal is to understand what the core computations supporting these functions are, and how these computations are realized in the collective action of single neurons. A potential access point into this issue is provided by “splitter cells”, hippocampal neurons that fire differentially on the overlapping segment of trajectories that differ in their past and/or future. However, the literature on splitter cells has been fragmented and confusing, owing to differences in terminology, behavioral tasks, and analysis methods across studies. In this review, we synthesize consistent findings from this literature, establish a common set of terms, and translate between single-cell and ensemble perspectives. Most importantly, we examine the combined findings through the lens of two major theoretical ideas about hippocampal function: representation of temporal context and latent state inference. We find that unique signature properties of each of these models are necessary to account for the data, but neither theory, by itself, explains all of its features. Specifically, the temporal gradedness of the splitter signal is strong support for temporal context, but is hard to explain using state models, while its flexibility and task-dependence is naturally accounted for using state inference, but poses a challenge otherwise. These theories suggest a number of avenues for future work, and we believe their application to splitter cells is a timely and informative domain for testing and refining theoretical ideas about hippocampal function.
Article
Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unraveling the learning and neural representation of such a map has become a central focus of neuroscience. In recent years, many models have been developed to explain cellular responses in the hippocampus and other brain areas. Because it can be difficult to see how these models differ, how they relate and what each model can contribute, this Review aims to organize these models into a clear ontology. This ontology reveals parallels between existing empirical results, and implies new approaches to understand hippocampal–cortical interactions and beyond. This Review organizes models of cognitive maps into a clear ontology. This ontology reveals parallels between existing empirical results and implies new approaches to understand hippocampal–cortical interactions and beyond.
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You and I, feeling and thinking matter, age as time elapses and thus conceive time as an irreversible arrow. We also speak of time that passes and that lasts, of time that marks the beat, and reminisce the past and plan the future times. Humans equip themselves with a linear arrow of time (sometimes circular) on which they chronologically arrange events, facts of their personal life, or those of History; events, which they causally (re)arrange from the past to anticipate the future. The intelligible and consciously available temporal phenomenology we, humans, experience is a hallmark of exquisite evolutionary processes, seemingly remote from the time that physics describe. Yet the psychological reality that makes up our intangible awareness of what we call “time” is inseparable from the functioning of the most complex physical systems known in the universe: our brains. Can we infer from the dynamic properties of brain activity our experience of time? Is the timing of consciousness emerging from our brain also the consciousness of time we claim to have? Herein, I will discuss the viewpoint that (psychological) time results from a neural code and that time perception cannot be reduced to “serial nows” – if only from the external observer’s viewpoint. Operationalizing “time” in cognitive neuroscience is a necessary heuristics to posit clear computational goals, algorithmic rules and possible implementations of mental clocks in thinking matter.
Article
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Much of our long-term knowledge is organised in complex networks. Sleep is thought to be critical for abstracting knowledge and enhancing important item memory for long-term retention. Thus, sleep should aid the development of memory for networks and the abstraction of their structure for efficient storage. However, this remains unknown because past sleep studies have focused on discrete items. Here we explored the impact of sleep (night-sleep/day-wake within-subject paradigm with 25 male participants) on memory for graph-networks where some items were important due to dense local connections (degree centrality) or, independently, important due to greater global connections (closeness/betweenness centrality). A network of 27 planets (nodes) sparsely interconnected by 36 teleporters (edges) was learned via discrete associations without explicit indication of any network structure. Despite equivalent exposure to all connections in the network, we found that memory for the links between items with high local connectivity or high global connectivity were better retained after sleep. These results highlight that sleep has the capacity for strengthening both global and local structure from the world and abstracting over multiple experiences to efficiently form internal networks of knowledge.
Conference Paper
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We investigate the extent to which navigation may be performed using exosomatic cues directly viewed in the environment, as opposed to relying on memory of a map or mental representation. Using trajectory data from a virtual navigation game app, Sea Hero Quest, we analyse the moment to moment route choices of 200 participants, and compare these against the expected routes based on several spatial variables measured from current isovists. Observations suggest that there is substantial evidence that for most participants navigation in a novel environment is indeed largely based on direct exosomatic information, and is based specifically on the space actually viewed, as opposed to that inferred by the shape of occluding edges. We also find evidence that strategies differ between individuals, in that the better navigators will deviate more from the exosomatic method, and rely more on their own memory and internal knowledge of the environment.
Article
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Hippocampal place cells fire at different rates when a rodent runs through a given location on its way to different destinations. However, it is unclear whether such firing represents the animal's intended destination or the execution of a specific trajectory. To distinguish between these possibilities, Lister Hooded rats (n = 8) were trained to navigate from a start box to three goal locations via four partially overlapping routes. Two of these led to the same goal location. Of the cells that fired on these two routes, 95.8% showed route-dependent firing (firing on only one route), whereas only two cells (4.2%) showed goal-dependent firing (firing similarly on both routes). In addition, route-dependent place cells over-represented the less discriminable routes, and place cells in general over-represented the start location. These results indicate that place cell firing on overlapping routes reflects the animal's route, not its goals, and that this firing may aid spatial discrimination.
Research
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Spatial cognition is an important model system with which to investigate how sensory signals are transformed into cognitive representations. Head direction cells, found in several cortical and subcortical regions, fire when an animal faces a given direction and express a global directional signal which is anchored by visual landmarks and underlies the “sense of direction”. We investigated the interface between visual and spatial cortical brain regions and report the discovery that a population of neurons in the dysgranular retrosplenial cortex, which we co-recorded with classic head direction cells in a rotationally symmetrical two-compartment environment, were dominated by a local visually defined reference frame and could be decoupled from the main head direction signal. A second population showed rotationally symmetric activity within a single sub-compartment suggestive of an acquired interaction with the head direction cells. These observations reveal an unexpected incoherence within the head direction system, and suggest that dysgranular retrosplenial cortex may mediate between visual landmarks and the multimodal sense of direction. Importantly, it appears that this interface supports a bi-directional exchange of information, which could explain how it is that landmarks can inform the direction sense while at the same time, the direction sense can be used to interpret landmarks.
Preprint
Travel constrained to paths, a common navigational context, demands knowledge of spatial relationships between routes, their components, and their positioning in the larger environment. During traversal of an environment composed of multiple interconnected paths, a subpopulation of subiculum neurons robustly encoded the animal’s current axis of travel. The firing of these axis-tuned neurons peaked bimodally at head orientations approximately 180 degrees apart. Track rotation experiments revealed that axis encoding carried the spatial reference frame of the larger environment as opposed to the track itself. However, axis-tuned activity of the same subpopulation was largely absent during unconstrained movement about a circular arena. Thus, during navigation in a path-rich environment, subpopulations of subiculum neurons encode the animal’s current axis of travel relative to environmental boundaries - providing a powerful mechanism for mapping of specific relationships between routes, route components, and the larger environment.
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1. There are indications that the execution of behavioral sequences involves the basal ganglia. In this study we examined the role of the caudate nucleus in the construction, storage, and execution of spatial plans. 2. Two monkeys (Macaca mulatta) were trained to perform sequences of saccades and arm movements. The animals had to remember the order of illumination, variable from one sequence to another, of three fixed spatial targets. After a delay, they had to visually orient toward, and press each target in the same order. Six different sequences were executed on the basis of the order of illumination of the targets. Single cell activity was recorded from the four caudate nuclei of the two monkeys. 3. Neural activity was analyzed in each sequence during 10 different periods: the instruction period in which the targets were illuminated, the three orientation periods toward the different targets, the three postsaccadic periods, and the three periods of target pressing. Statistical comparisons were made to detect differences between the different sequences with respect to activity in each period (sequence specificity). 4. A total of 2,100 neurons were studied, of which 387 were task related. The task-related cells were found in both the head and the body of the caudate nucleus. 5. During central fixation, anticipatory activity (n = 81) preceded onset of specific events. Four groups were considered: 1) neurons (n = 46) anticipating offset of the central fixation point, 2) neurons (n = 7) anticipating the illumination of any target, regardless of its spatial position or order of presentation (rank), 3) neurons (n = 17) anticipating the illumination of the first target, regardless of its spatial position, and 4) neurons (n = 11) anticipating the illumination of a given target, regardless of its rank. 6. Phasic visual responses to target onset were observed in 48 cells. The cells responded primarily to the contralateral and upper targets. In a majority (n = 35), visual responses were modulated by the rank of the target(s). Many cells (n = 20) responded only if the corresponding target was first; other cells responded only if the target was second or if it had complex time relationships with the other targets. 7. The responses of the cells to the same instruction stimuli repeated twice in a row, and under the condition that the animal did not behaviorally use the first instruction in between, were tested. More than one-third of the tested cells (n = 14) did not respond, or responded very weakly, to the second instruction.(ABSTRACT TRUNCATED AT 400 WORDS)
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Humans and animals frequently learn through observing or interacting with others. The local enhancement theory proposes that presence of social subjects in an environment facilitates other subjects' understanding of the environment. To explore the neural basis of this theory, we examined hippocampal place cells, which represent spatial information, in rats as they stayed in a small box while a demonstrator rat running on a separate, nearby linear track, and as they ran on the same track themselves. We found that place cell firing sequences during self-running on the track also appeared in the box. This cross-environment activation occurred even prior to any self-running experience on the track and was absent without a demonstrator. Our data thus suggest that social observation can facilitate the observer's spatial representation of an environment without actual self-exploration. This finding may contribute to neural mechanisms of local enhancement.
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This paper examines the home ranges and seasonal movements of eight GPS-collared elephants (two females and six males) in the northwestern Kunene region of Namibia. Minimum convex polygon (MCP) and the fixed kernel density estimation (FKDE) methods were used to analyse home ranges. The collared elephants showed defined home and seasonal ranges. In the eastern section of the research area, the elephants generally had smaller home ranges that were at their least during the hot and cold dry seasons, expanding during the wet season. In the western areas, the elephants moved between the Hoanib and Hoarusib Rivers in response to available vegetation that did not necessarily correspond to rainfall. The length of movement of collared elephants varied from 54.5 to 473 km in the eastern section of the research area to between 251 to 625 km in the west, over periods of up to five months.
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To navigate, animals need to represent not only their own position and orientation, but also the location of their goal. Neural representations of an animal’s own position and orientation have been extensively studied. However, it is unknown how navigational goals are encoded in the brain. We recorded from hippocampal CA1 neurons of bats flying in complex trajectories toward a spatial goal. We discovered a subpopulation of neurons with angular tuning to the goal direction. Many of these neurons were tuned to an occluded goal, suggesting that goal-direction representation is memory-based. We also found cells that encoded the distance to the goal, often in conjunction with goal direction. The goal-direction and goal-distance signals make up a vectorial representation of spatial goals, suggesting a previously unrecognized neuronal mechanism for goal-directed navigation.
Article
Flexible navigation demands knowledge of boundaries, routes and their relationships. Within a multi-path environment, a subpopulation of subiculum neurons robustly encoded the axis of travel. The firing of axis-tuned neurons peaked bimodally, at head orientations 180° apart. Environmental manipulations showed these neurons to be anchored to environmental boundaries but to lack axis tuning in an open arena. Axis-tuned neurons thus provide a powerful mechanism for mapping relationships between routes and the larger environmental context. © 2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.
Article
The retrosplenial cortex (RSC) plays an important role in memory and spatial navigation. It shares functional similarities with the hippocampus, including the presence of place fields and lesion-induced impairments in spatial navigation, and the RSC is an important source of visual-spatial input to the hippocampus. Recently, the RSC has been the target of intense scrutiny among investigators of human memory and navigation. fMRI and lesion data suggest an RSC role in the ability to use landmarks to navigate to goal locations. However, no direct neurophysiological evidence of encoding navigational cues has been reported so the specific RSC contribution to spatial cognition has been uncertain. To examine this, we trained rats on a T-maze task in which the reward location was explicitly cued by a flashing light and we recorded RSC neurons as the rats learned. We found that RSC neurons rapidly encoded the light cue. Additionally, RSC neurons encoded the reward and its location, and they showed distinct firing patterns along the left and right trajectories to the goal. These responses may provide key information for goal-directed navigation, and the loss of these signals may underlie navigational impairments in subjects with RSC damage.