throughout the portion of the room used by
the bats during foraging (Fig. 2E), with typ-
ically one (54%) or two (24%) fields per cell
(91% had three or fewer fields).
The results presented thus far suggest that
CA1 neurons encode nonlocal navigational in-
formation during flight but they do not in-
dicate what might determine the locations
of the spatiotemporal fields. We reasoned that
spatiotemporal field organization might be
linked to the self-organized behavioral struc-
ture that bats exhibit during goal-directed nav-
igation. Because we found that neurons were
tuned to flight paths headed in the same di-
rection (Fig. 2C), we posited that neurons might
also be tuned to other spatial commonalities
between paths, specifically the places where
paths intersect. To address this hypothesis,
we first determined whether there was any
overlap between path intersections and the
spatiotemporal field locations during the goal-
directed foraging task (Fig. 2). We identified
the intersections between all pairs of flight
paths (Fig. 3A and fig. S15; 10.8 mean ± 8.6 SD
path intersections per neuron, for a total of
1931 intersections). We only considered inter-
sections where paths were headed in the same
direction (enter and exit angles < |90°|) and
only intersected once (see the supplementary
materials and methods). Intersections based
on this criterion were found for 97% (178/183)
of neurons with spatiotemporal fields. We
found a high degree of overlap between the
nonlocal spatiotemporal fields and intersec-
tion locations (Fig. 3B), supporting the hy-
pothesis that temporally shifted firing fields
were centered at locations where flight paths
intersected. We computed the distance be-
tween intersection locations and spatiotem-
poral field locations and found that 72% (129/
178) of the neurons had an intersection within
≤2mfromtheneuron’s spatiotemporal field.
Moreover, 50% (973/1931) of all the intersec-
tions were <2 m from their respective neuron’s
spatiotemporal field (Fig. 3C; less dispersed
than would be expected by chance,P< 0.01,
randomization test; see the supplementary ma-
terials and methods). On the basis of the strong
directional orientation, it is apparent that neu-
rons are only active along a subset of paths, which
could explain why a portion of intersections are
far from spatiotemporal fields. Thus, there is a
strong correlation between the location of a large
subset of intersections and spatiotemporal fields.
Next, we sought to determine whether spatio-
temporal fields could result from the patterns
of neural activity along intersecting paths. If
theneuralactivityoneachpathweretempo-
rally advanced or delayed with respect to the
intersection, then this could result in a spatio-
temporal field at the intersection. For each
neuron, we identified pairs of paths with signif-
icant spatial information (see the supplemen-
tary materials and methods) and an intersection
<2 m from the spatiotemporal field of the neu-
ron. We identified 162 path pairs from 29% (53/
183) of the spatially informative neurons. We
then investigated whether the peak firing rates
are aligned to path intersection points when
the optimal lag is taken into consideration.
We first scaled all the flights from 0 to 100%
(flight phase) and then identified the phase
of intersections (Fig. 3D) and the peak firing
rate phases at lag zero and at the optimal lag
(optimal lag is the lag with maximal spatial
information for that neuron). Figure 3E shows
the intersection phases (red dots) overlaid on
the firing rate profiles for each pair of paths at
lag zero (top) and the optimal lag (bottom). The
peak firing rates for both paths, even though
most have little overlap in space, became aligned
with the intersection phase at the optimal lag.
Furthermore, there was a strong positive cor-
relation between the peak firing rate phases
for all pairs of intersecting paths, which increased
when accounting for the optimal lag (Fig. 3F).
Indeed, simply accounting for the optimal lag
substantially shifted the distribution of differ-
ences between the peak firing rate phase and
the intersection phase toward zero (Fig. 3G;P<
0.01 rank sum test). These results demonstrate
a precise alignment between self-organized
navigation patterns and neural dynamics, sug-
gesting that spatiotemporal firing fields may
form around intersections.
When we recorded from bats flying freely at
high speeds during either random exploration
or in a goal-directed manner, we found that
the neural activity in bat CA1 robustly encodes
nonlocal navigational information. Classical
place tuning appears to be simply part of a
larger continuum representing the bat’s past,
present, and future locations. Much of this
information would go undetected if past or
future positions were not taken into consider-
ation. These findings also complement reports
demonstrating that cells with no identified
firing fields, i.e., non–place cells, can contribute
to the neural code for space at the population
level ( 35 , 36 ), albeit in a different manner. Last,
these findings, although functionally similar,
are mechanistically distinct from theta-phase
and theta-sequence coding and reveal another
complementary mechanism by which positional
information that extends beyond the animal’s
present position can be represented in the hippo-
campal formation. The diversity of temporal and
rate-based coding schemes by which nonlocal
positional information can be represented raises
important questions for future studies to con-
sider about how simultaneous representations
of past, present, and future positions in the
hippocampus can be read out effectively by
downstream regions.
Ourresultsfurtherindicatethatnonlocal
representations are anchored near both future
and past intersections of self-selected flight
paths, which can occur at all phases of flight.This differs from a vectorial tuning to a spe-
cific endpoint goal location ( 32 ), yet the two
could serve as important complements to each
other during navigation. These results are also
consistent with studies showing evidence for
path-invariant representations of spatial posi-
tions ( 29 ) such as intersections and goal loca-
tions. Previous investigations into rate-shifted
coding mechanisms in rats showed that neu-
rons optimally encoded a position immediately
in front of the animal ( 28 , 29 ). This could have
been because of the slower speeds of movement
exhibited by rodents in small experimental en-
vironments compared with the high speeds of
bat flight, which necessitates planning and rap-
idly predicting positions far into the future.
Combined, these results reveal a positional rep-
resentation in flying bats that extends along a
continuum of space and time and could sup-
port a representation of remembered paths.
Spatial coding has been observed in the
hippocampus (or analogous structures) across
a wide variety of species that have evolved
to navigate in very different environments,
whether underwater, on the ground, or in the
sky ( 5 , 8 , 37 – 39 ). The way in which a given
species negotiates its environment may neces-
sitate weighing the relevance of past and fu-
ture positions differently. For example, monkeys
jumping between tree branches or humans
driving a car or skiing downhill at high speeds
may require a higher weight placed on future
locations where the positional information
ahead could be more important for survival
than the present position. This notion is con-
sistent with more recent theories highlighting
the function of the hippocampus as a flexible
predictive map ( 40 ). Furthermore, different
species may also weigh the importance of
specific locations in their environment differ-
ently. An Egyptian fruit bat, which naturally
forms highly reproducible paths ( 1 ) along
which it traverses repeatedly at high speeds,
may benefit from a nonlocal representation of
specific locations along flight paths (e.g., inter-
section points), yet this may not be the case for
an animal exploring the environment in a dif-
ferent manner. Our results further highlight the
importance of studying neural circuits during
spontaneously emerging behavioral patterns
across a diversity of species. Examining neural
activity from an ethological perspective that
carefully considers how a specific animal moves
and operates could help to better inform us of
the underlying neural computations that gen-
erate behavior ( 41 , 42 ).REFERENCESANDNOTES- A. Tsoaret al.,Proc. Natl. Acad. Sci. U.S.A. 108 , E718–E724
(2011). - M. Geva-Sagiv, L. Las, Y. Yovel, N. Ulanovsky,Nat. Rev. Neurosci.
16 , 94–108 (2015). - L. Harten, A. Katz, A. Goldshtein, M. Handel, Y. Yovel,Science
369 , 194–197 (2020). - S. Toledoet al.,Science 369 , 188–193 (2020).
246 9JULY2021•VOL 373 ISSUE 6551 sciencemag.org SCIENCE
RESEARCH | REPORTS