R ES E A RC H

NEUROSCIENCE

Spatial representations of self andother in the hippocampus
Teruko Danjo,1 Taro Toyoizumi,2 Shigeyoshi Fujisawa1*
An animal’s awareness of its location in space depends on the activity of place cells in
the hippocampus. How the brain encodes the spatial position of others has not yet been
identified. We investigated neuronal representations of other animals’ locations in thedorsal CA1 region of the hippocampus with an observational T-maze task in which one
rat was required to observe another rat’s trajectory to successfully retrieve a reward.
Information reflecting the spatial location of both the self and the other was jointly anddiscretely encoded by CA1 pyramidal cells in the observer rat. A subset of CA1 pyramidalcells exhibited spatial receptive fields that were identical for the self and the other. Thesefindings demonstrate that hippocampal spatial representations include dimensions forboth self and nonself.

1

Laboratory for Systems Neurophysiology, RIKEN Brain
Science Institute, 2-1 Hirosawa, Wako, Saitama, 351-0198,
Japan. 2Laboratory for Neural Computation and Adaptation,
RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama,351-0198, Japan.

*Corresponding author. Email: fujisawa@brain.riken.jp

Danjo et al., Science 359, 213–218 (2018)

side rules, respectively) (fig. S2) [see the supplementary materials (SM)].
We first examined how the location of theother rat was represented in the opposite-side
rule observational task. Firing-rate maps of theself’s positions (“self’s rate map”) and the other’s
positions (“other’s rate map,” which were obtainedby replacing the self’s positional data with theother’s) revealed that in addition to the expected
coding of the observer’s own position in space,
the majority of units also displayed obvious place
fields for the other (Fig. 1, C to F, top). To understand the positional relationship of the rat pair at
the time of firing, we constructed joint rate maps
by linearizing the rats’ trajectories and entering
them into two-dimensional x-y axes (fig. S3).
Here, the self’s and other’s place fields composedfrom identical spikes were combined into a single joint place field (Fig. 1, C to F, bottom, andfig. S4). We then examined whether these joint
place fields were truly modulated by the other’s
positions and not a mere consequence of theconstraints in the positional relationships ofthe two rats. The null hypothesis was that thefiring only depended on the self’s position andwas independent of the other’s. By computing
the surrogate firing rates that followed this null
hypothesis (see the SM) (20), we identified areas
with significantly higher firing rates for theother (P < 0.05) (Fig. 1, C to F, third from top, andfig. S5). A significant firing dependency on theother’s positions was detected for 85% of units(Fig. 1, G to I), indicating that the other’s spatial
information was generally encoded by the place
cells in this T-maze task. Importantly, the significant areas were not dependent on the self’s
specific behavior or the other’s positions (Fig. 1,
C to I). Moreover, the firing activities of mostunits were not dependent on the time periodsin the trial (fig. S6). Similar results were also
observed when we analyzed the same-side ruleversion of the task (fig. S7).
In the hippocampus, spatial information is encoded not only by the average firing rates of theneurons but also by the timing of the spikes withrespect to the phase of the underlying theta os-

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patial navigation requires the hippocampus(1, 2). The cognitive map theory states thatspatial recognition in the hippocampus isallocentric (3–5). Place cells in the hippocampus are the physiological correlate ofthis representation because they are highly sensitive to changes in landmarks and contexts (6–13).
The characterization of additional types of navigational representations, including head-direction
cells and grid cells, has promoted our understanding of the neural mechanisms of allocentric spatial
representations in the hippocampal-entorhinal
cortex network (14–19). The studies of these neuralmaps have focused on the animal’s own positionin space. It still remains unclear whether andhow spatial information of nonself, such as the
position of conspecifics, landmarks, and moving
objects, is represented in the hippocampus.
We designed an observational T-maze taskusing a pair of rats (hereafter termed “self” and“other”) and investigated how the other’s position
is represented in the self’s hippocampus. The self
was required to make a left/right choice to retrieve a reward based on the location of the other.
We used two versions of the task, an “oppositeside rule” version in which the self rat had tochoose the side opposite to the other’s location(Fig. 1A, fig. S1, and movie S1) and a “same-siderule” in which the self rat had to choose the sameside as the other rat (fig. S1 and movie S2). During
the T-maze task, neuronal activity in the self’s
hippocampus (dorsal CA1) was recorded extracellularly (n = 3 pairs of rats; 88 ± 8.1% and 84 ±11% correct performance with opposite-side andsame-side rules, respectively). All analyses were
performed on single units with pyramidal cellfeatures and place fields in the task area (n =1298 and 1205 units with opposite-side and same-cillations (7 to 11 Hz) (21–24). We next examined
this temporal coding in the other’s spatial representations. To control for confounds of the motion of the observer, we focused on units whose
rate peaks were located in the self’s starting positions in the opposite-side rule and analyzed
the relationship between the other’s positions
and the theta phases of the spikes (n = 78 units).
Although the self was not running during thetime course of this analysis, the local field potential was dominated by theta oscillations (fig.
S8 and SM). In contrast to the lack of thetaphase precession relative to the self’s location,
75% of the units displayed significant thetaphase precession as a function of the other’slocation (P < 0.01) (Fig. 1, J to M). The observed
theta-phase precession to the other’s location
was consistent with that to the self’s location,
as reported previously (21–24), demonstratingthat the other’s spatial information was also
temporally encoded in the hippocampal pyramidal cells.
We next sought to dissociate two types ofunits—those preferentially modulated by theother’s position (“other-preferred cells”) andthose preferentially modulated by the self’s goal[“goal-preferred cells,” also previously describedas “prospective cells” (25, 26)]. We took advantage of the two versions of our observationalT-maze task (Fig. 2A). Other-preferred cells fire
when the other is located on the same side of
the side arms (left or right), irrespective of therule during a given trial and, as a result, theseneurons fire when the self’s goal is on a different
side across the two conditions (Fig. 2B). This should
further support the allocentric representationof the other’s place. In contrast, goal-preferredcells increase their firing rate only according to
the future choice of the observer. By statisticallycomparing the other’s rate maps between thetwo versions, we defined other-preferred cells andgoal-preferred cells based on these criteria (seethe SM). We identified 58 other-preferred cells(Fig. 2, C to G, and fig. S9) and 252 goal-preferredcells (Fig. 2, H and I). Although the number ofother-preferred cells was smaller than that ofgoal-preferred cells, it represented 13% of theplace-responsive units examined in this analysis(Fig. 2F and SM), and the firing-rate peaks of both
unit groups distributed all along the side arms in
the other’s rate maps, constituting a full spatial
map based on the position of the other rat (Fig. 2,
G and I). Furthermore, the analysis of comparing
the correct and the error trials in the oppositeside rule resulted in similar percentages of otherpreferred and goal-preferred cells (fig. S10).
We next looked for another type of spatial
representation, “a common place field” for theself and other, for which a unit fires when either
the other or the self is in a specific spatial location. We found 45 units that had common place
fields in both the other’s and the self’s rate maps
under the opposite-side rule (Fig. 3, A to E, andSM). We further tested the trajectory specificity
of common place fields by separating left- andright-side targeting trials. Notably, we identified
51 of 1244 units in the opposite-side rule task

R ES E A RC H | R E PO R T

Other’s normalized rate map

Side arms
Left

Center arm

rewardspoutsLeft

Start arms

Other
Self

11.5

Right

5.4

Other

16.9 Self

Other

21.1

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Other

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rewardspouts

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d0215_u13012 January 2018

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p<0.0110
5
0

0 0.1 0.2 0.3 0.4 0.5
Correlation of
Theta Phase and Other’s Position

180
start
T-cross
Position of Rats

Fig. 1. Joint spatial representations of self and other in theobservational task. (A) Schematic of the opposite-side rule. See
fig. S1A. (B) Schematic of linearizing the trajectories on the T-maze.
See figs. S3 and S4. (C to F) Units whose firing rates significantly
depended on the other’s position. (Top) Other’s (left) and self’s (right)
rate maps. (Second from top) Trajectory plots of other (left) andself (right). Gray lines, rats’ trajectories; red dots, spike positions.
(Third from top) Linearized rate plots on other’s position (black lines).
Red lines indicate statistically significant bands (P < 0.05) (see fig. S5).
Blue thick line indicates the significant area. (Bottom) Joint rate
maps. Other’s and self’s linearized positions are coordinated in
Danjo et al., Science 359, 213–218 (2018)

Theta Phase (degree)

12.5 1001800Firing Rate (Hz)

Other

Center Arm

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Theta Phase (degree)

0
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Firing Rate (Hz)
10.6

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cell number

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0 startT-cross
Position of Ratshorizontal and vertical axes, respectively. The maximum firing rate isindicated at the top of each map. (G to I) Linearized and normalized rate
maps based on the other’s position (G), the self’s position (H), and thesignificant area on the other’s position (I), sorted by maximum rate
positions on the other’s map (n = 1298 units). (J to L) Units withtheta-phase precession based on the other’s position. (Top) Self’s rate
maps (left), linearized rate plots, and theta phases of the spikes as afunction of self’s position (right). (Bottom) Other’s rate maps (left),linearized rate plots, and theta phases of the spikes as a function of other’s
position (right). (M) Correlation between theta phase and the other’s
position. Color bars indicate significant correlation (P < 0.01; n = 59 units).

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cell number

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Self’s normalized rate map

R ES E A RC H | R E PO R T

that had trajectory-specific common place fields(Fig. 3, F and G; fig. S11; and SM), which wassignificantly higher than the number expectedby chance (P < 0.05) (see SM).
Because the allocentric representation of self’splace was also demonstrated by reliable reconstruction of self’s positions only from the spikesof place-cell ensembles (27, 28), we further tested
whether the spikes of joint-place-cell ensembles
could also reconstruct the other’s positions. First,
we performed Bayesian decoding analysis witha leave-one-out strategy in each rule (SM). The

Opposite-side rule

Same-side rule

Goal-preferred cells(Prospective Cells)

Other-preferred cells
Opposite-side
rule

Other
Other’splace field

Other

these spikes contained more information about
the other’s place than that obtained by distributions of time spent in the positional relationshipsof the self and the other. We computed a control
estimation of the other’s positions by reconstructing the self’s positions and then accordingly
referring to the probability distributions of thepositional relationships (fig. S12B and SM). For
the Bayesian reconstructions in this analysis,
the prior templates were computed from trials
including both rules, and results were examinedbased on error distances in a block-wise mannerSame-side
rule

Other

Opposite-side
ruleSame-side
rule

Other

Self
Self’s goal

Self

Self

Self

Other-preferred cells
Opposite-side rule

Opposite-side rule

Same-side rule

10.4

14.6

32.5

14.2

17.9
d0215_u35

39.2

Same-side rule

Opposite-side rule Same-side rule

Other

6.1

29.4

40.6

40.5

53.6
d0401_u77

Other-preferred cells
58 (13%)Goal-preferred cells
252 (58%)
The rest
124 (29%)58 cell number

1

Self

12.4
d0330_u70

Other-preferred cells
Same-side rule

Opposite-side rule

L

R

Start

L

Center Arm

R

Side Arms

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R

Start

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Center Arm

R

Side Arms

Goal-preferred cells
Opposite-side rule Same-side rule

Opposite-side rule1

Same-side rule

22.8

8.4

27.5

9.7

252

Self

cell number

Other

L

L

R

Start

Center Arm

R

Side Arms

L

L

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Start

Center Arm

R

Side Arms

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Fig. 2. Spatial representations of other-preferredand goal-preferred cells.
(A) Schematic of the relationship of the other’s position
and the self’s goal in oppositeside and same-side rules.
(B) Schematic explaining
other-preferred cells (left)
and goal-preferred cells(right). Other-preferredcells fire preferentially when
the other is on the same side,
regardless of the rule. Similarly, goal-preferredcells fire when the self’sgoal is on the same side.
(C to E) Rate maps ofother-preferred cells.
(Top) Other’s rate maps
in the opposite-side rule(left) and same-side rule
(right). (Bottom) Self’s rate
maps in the opposite-side
rule (left) and the same-side
rule (right). See fig. S9for statistical methods.
(F) Summary of otherpreferred cells and goalpreferred cells (n = total
434 units). (G) Linearized
and normalized rate
maps of other-referredcells, sorted by peak rate
positions. (H) Other’s (top)and self’s (bottom) rate mapsof goal-preferred cells in the
opposite-side rule (left)
and the same-side rule
(right). (I) Linearized andnormalized other’s rate
maps of goal-preferred cells
in the opposite-side task
(left) and same-side task(right), sorted by the
maximum rate positions.

reconstructed other’s trajectories revealed thatthe spikes of joint-place-cell ensembles contained
sufficient information regarding the other’s positions (Fig. 4A and fig. S12A). Although the error distances between the actual and decoded
positions were larger for the other’s decoding
than those for the self, the differences in theerror distances were as small as 5 cm per time
window (200 ms) in both rules (other’s versus
self’s error distances, 20.3 versus 16.1 cm in the
opposite-side rule, 20.1 versus 15.1 cm in the sameside rule) (Fig. 4B). We then examined whether

R ES E A RC H | R E PO R T

Other

27.2

Self

33.6

We propose an extended model of hippocampal spatial representations that can include dimensions for both self and other (fig. S13). Ourmodel, encompassing various types of spatial
representations, can categorize spatial representations into four types: own place fields, joint
place fields, other’s place fields, and common
place fields (fig. S13, A to D). In particular, thecommon place field could be hypothesized to bea mirror representation of place (29, 30). Combi-

Normalized firing rate

2D products
Other

16.3

Self

16.0

2D products

1

0.5

0

0

X
0

1

1

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0

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1

1

X
0

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start

0.5
0

T-cross

Other’s rate maps

start

0

T-crossstart

T-cross

1D products

1

Self’s rate maps

0.84
19.3

Self

23.4

2D products

45

Other

Cell number

d0925_u13

0.64d0213_u26

Right trials

Left trials

L

Center

R

L R

Side

Start

Whole

2D products

13.8

L

CenterRight trials

R

L R

Side

Start

Left trials

L

CenterR

Side

2D products

Other

17.2

Other

Whole

L R

Start

0.36

0.68
17.3

Self

Self

24.1

0.89

0.29
d0214_58

Fig. 3. Common place fields in self’s and other’s rate maps. (A to C)
Common place fields of representative units in the other’s (left) andthe self’s (middle) rate maps. (Right) The products (element-wise
multiplications) of normalized other’s and self’s firing rates in each pixel.
(D) Linearized other’s (left) and self’s (middle) firing rates and theirproducts (right). The products are the element-wise multiplications of thenormalized firing rates of the self and the other at each position, of theunits shown in (A) to (C). This value was used to identify units with
common place fields. (E) Linearized and normalized rate maps of units
Danjo et al., Science 359, 213–218 (2018)12 January 2018

d0923_32

with common place fields aligned by the position with the maximum
value of the products (n = 45 of 908 units). (F and G) Trajectory-specificcommon place fields of two representative units. (Left) Other’s (top) andself’s (bottom) rate maps of whole trials. (Middle) Rate maps with the left andright trials separated. (Right) The products of normalized other’s and self’s
firing rates of right (top) and left (bottom) targeting trials in each pixel.
Arrows indicate the centers of common place fields. The maximum color
value of the product map was set as 0.5, and the values in the product map
indicate the maximum of the product.

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Normalized firing rate (Hz)0.76

1D Products

Self

1

Other

natorial representations of spatial informationof self and nonself would open the door to examining whether this allocentric spatial representation extends more generally to other nonliving
moving objects (31–33) and how it is generated
in the hippocampal-entorhinal cortex network.
Our data indicate that the place cells in the hippocampus encode sufficient spatial informationfor organizing the recognition of other animals,
which is essential for social behavior (34, 35).

1D Product(Fig. 4, C and D, and SM). Reconstructing trajectories without rule information apparently madethe error distances of the other’s decoded positions larger, but they were significantly smaller
than the control estimate of the other’s positions(Fig. 4D). The overall averages of the error distances of the other’s decoding were less than
40 cm and also were significantly smaller thanthose of the control estimation in both rules(Fig. 4E).

R ES E A RC H | R E PO R T

Opposite-side rule
trial 41
Other’s
Position

trial 42

trial 43

trial 44

trial 45

trial 46

trial 42

trial 43

trial 44

trial 45

trial 46

trial 48

trial 49

trial 50

decodeactualtrial 41
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Self’sPosition

trial 47

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trial 49

trial 50

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25

20

20

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15

10

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5

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100

5

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6
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12 3

Error distance (cm)

1 2

80

60

Control estimation

60
40
20

4
0
6

1

2

3

4
Block

5

6

Fig. 4. Decoding other’s and self’s positions from spikes of hippocampal
cells. (A) Reconstructed other’s and self’s positions in the opposite-side
rule. Red and blue lines, reconstructed other’s and self’s positions,
respectively. Black lines, actual trajectories of the other or self. (B) Mean
error distances between actual and decoded positions (31 and 28 sessions
from the opposite-side and same-side rules, respectively). (C to E) The
reconstructions of other’s positions by using prior templates calculated fromtrials including both rules. (C) Schematic of the division of other’s trajectories

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ACKN OWLED GMEN TS

We thank A. Amarasingham for helpful suggestions on data
analysis and T. J. McHugh and C. Yokoyama for comments on the
manuscript. This work was supported by Japan Society for the
Promotion of Science Grants-in-Aid for Scientific Research(KAKENHI) nos. 16K14561 and 16H01290 to T.D. and 15H05876 and

16H01519 to S.F. All data necessary to support the paper’s
conclusions are present in the paper and the supplementary materials.

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Figs. S1 to S13
Movies S1 and S2References (36–41)
14 July 2017; accepted 7 December 201710.1126/science.aao3898

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Spatial representations of self and other in the hippocampus
Teruko Danjo, Taro Toyoizumi and Shigeyoshi Fujisawa

Science 359 (6372), 213-218.
DOI: 10.1126/science.aao3898

ARTICLE TOOLS

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The representation of others in space
Different sets of neurons encode the spatial position and orientation of an organism. However, social animals
need to know the position of other individuals for social interactions, observational learning, and group navigation.
Surprisingly, very little is known about how the position of other animals is represented in the brain. Danjo et al. and
Omer et al. now report the discovery of a subgroup of neurons in hippocampal area CA1 that encodes the presence ofconspecifics in rat and bat brains, respectively.
Science, this issue p. 213, p. 218