error 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 same-side rule) (Fig. 4B). We then examined whether
these spikes contained more information about
the other’s place than that obtained by distributions of time spent in the positional relationships
of 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 the
positional 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 examined
based on error distances in a block-wise manner
(Fig. 4, C and D, and SM). Reconstructing trajectories without rule information apparently made
the 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 than
those of the control estimation in both rules
We propose an extended model of hippocampal spatial representations that can include dimensions for both self and other (fig. S13). Our
model, 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, the
common place field could be hypothesized to be
a mirror representation of place (29, 30). Combinatorial representations of spatial information
of 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 information
for organizing the recognition of other animals,
which is essential for social behavior (34, 35).
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Materials and Methods
Figs. S1 to S13
Movies S1 and S2
14 July 2017; accepted 7 December 2017
Social place-cells in the
David B. Omer, Shir R. Maimon, Liora Las,*† Nachum Ulanovsky*†
Social animals have to know the spatial positions of conspecifics. However, it is unknown
how the position of others is represented in the brain. We designed a spatial observational-learning task, in which an observer bat mimicked a demonstrator bat while we recorded
hippocampal dorsal-CA1 neurons from the observer bat. A neuronal subpopulation represented
the position of the other bat, in allocentric coordinates. About half of these “social place-cells” represented also the observer’s own position—that is, were place cells. The representation
of the demonstrator bat did not reflect self-movement or trajectory planning by the observer.
Some neurons represented also the position of inanimate moving objects; however, their
representation differed from the representation of the demonstrator bat. This suggests a role
for hippocampal CA1 neurons in social-spatial cognition.
It is important for social animals to know the spatial position of conspecifics, for purposes of social interactions, observational learning, and group navigation. Decades of research on the mammalian hippocampal formation
has revealed a set of spatial neurons that represent self-position and orientation, including
place cells (1–3), grid cells (4–6), head-direction
cells (7–9), and border/boundary cells (10–12).
However, it remains unknown how the location
of other animals is represented in the brain.
We designed an observational-learning task for
Egyptian fruit bats (Rousettus aegyptiacus), which
are highly social mammals that live in colonies
with complex social structures (13). Bats were
trained in pairs: In each trial, one bat (“observer”)
had to remain stationary on a “start ball” and to
observe and remember the flight trajectory of
the other bat (“demonstrator”), which was flying
roughly randomly to one of two landing balls
(Fig. 1A,“demonstrator flying” in trials i and j).
After a delay, the observer bat had to imitate the
demonstrator bat and fly to the same landing
ball to receive a reward (Fig. 1A, “observer flying,”
and movies S1 and S2). This task had two key
features: First, it required the observer to pay
close attention to the demonstrator’s position and
to hold this position in memory during the delay
period (the average delay between the demon-
strator’s return to the start ball and the observ-
er’s takeoff was rather long: 12.7 ± 8.6 s; mean ±
SD). Second, because the observer was stationary
during the demonstrator’s flight, it allowed tem-
poral dissociation between the effects of self-
flights versus the flights of the other bat.
While the bats performed the task, we recorded
the activity of 378 single neurons in the dorsal
hippocampal area CA1 of four observer bats, using
a wireless electrophysiology system (Fig. 1B) (14).
For each neuron, we computed two firing-rate
maps: a “classical” map, based on the self-movement
Department of Neurobiology, Weizmann Institute of Science,
Rehovot 76100, Israel.
*These authors contributed equally to this work.
†Corresponding author. Email: email@example.com
(N.U.); firstname.lastname@example.org (L.L.)