(a) Some hippocampal CA1 cells in bats showed egocentric goal direction
selectivity. Left, Schematic for egocentric goal direction. Top right, an
example goal direction cell that has the highest firing rate when the goal
direction is 0° (the bat flies toward the goal location). Bottom right,
trajectories of goal-direction angles along the behavioral session (gray), with
spikes overlaid (red). Reproduced, with permission, from Sarel, Finkelstein
et al. [28].
(b) Activity of some place cells in mouse hippocampal CA1 can be
modulated by egocentric heading direction to a reference point in the
environment. Left, Schematic of the egocentric heading direction relative to a
specific reference point. Right, Heat map: spatial firing rate map; red circle:
center of mass for the rate map; blue arrows: heading direction tuning within
each spatial bin; black circle: the reference point obtained by a model based on
the real heading direction tuning; red arrows: heading direction tuning fitted
by the model in each spatial bin. Reproduced, with permission, from Jercog
et al. [29].
(c) An example cell in LEC showed selectivity for egocentric bearing of
the arena boundary/center. Left, trajectory (gray lines) and position and head
direction of the rat when the cell fired (colored dots). Middle, Color wheel
denotes the head direction. Right, Local head direction tuning in each spatial
bin. Arrow direction: preferred head direction; arrow size: firing rate; color
saturation: the mean vector length of the tuning curve (MVL); number on top:
maximum MVL.
(d) An example LEC cell tuned for egocentric bearing of goal location in
a goal-oriented task. In this task, a single food well (red circle) was shifted
from the standard goal location in session 1 (left) to a different location in
session 2 (middle), and then back to the original standard location in session 3
(right). Local head direction tuning is showed as in (c).
(e) An example cell in MEC showed selectivity for allocentric head
direction, as demonstrated by all arrows pointing in the same, allocentric
direction.
(f) LEC represents spatial information in an egocentric frame of
reference, whereas MEC utilizes an allocentric frame of reference. Bayesian
information criterion (BICBoundary) indicates the goodness of fit of a
cell’s activity to an egocentric bearing model or an allocentric bearing
model. ΔBICBoundary describes the difference of goodness of fit between
the two models, in which a negative value means the cell prefers the egocentric
frame of reference, whereas a positive value means the cell prefers the
allocentric frame of reference. (c-f) Reproduced, with permission, from Wang,
Chen et al. [33].’