Shortcutting from self-motion signals reveals a cognitive map in mice
- Department of Cellular and Molecular Medicine, University of Ottawa, Canada
- Department of Physics, University of Ottawa, Canada
- Brain and Mind Institute, University of Ottawa, Canada
- Center for Neural Dynamics and Artificial Intelligence, University of Ottawa, Canada
Figures
Figure 1 with 1 supplement
Hidden Food Maze and experimental setup.
(A) The floor of the arena is 120 cm in diameter, and the walls are 45 cm tall. Note 20 cm scale bar in this panel. The home cage has a 10.5x6.5 cm2 floor area. The door slides upward (mice enter …
Figure 1—figure supplement 1
Pre-training trajectories and active sensing from the mouse perspective in random entrance experiment.
(A) Example trajectories of a mouse during pretraining (see Materials and methods). The mice are habituated to their home cage (Day 1) and the maze (Day 2, no food). On Day 3, mice start exploring …
Figure 2 with 1 supplement
Mouse spatial learning with random entrances.
(A) Two examples of mouse search trajectories during early learning (trial 3) when the entrance changes from trial to trial. They are irregular and vary unpredictably across trials. (A, B) Star = …
Figure 2—figure supplement 1
Hole check distribution in Random Entrance protocol for the four target holes from the mouse’s perspective.
(A) Red bar: hole checks in the target hole (labeled ‘0’). Green shaded bars: hole checks in the previous positions of the target in the mouse’s perspective (see Figure 1—figure supplement 1B; ‘–1’ …
Figure 3 with 1 supplement
Mouse spatial learning with static entrances.
(A) Two examples of mouse search trajectories during early learning (trial 3). They are irregular and variable similarly to those in the random entrance experiments. (A, B) Star = target location. …
Figure 3—figure supplement 1
Location of hole checks in the last 3 s before finding the target in Static Entrance protocol.
(A) Pink spots: hole checks in the last 3 s before finding the target; gray spots: earlier hole checks. Dashed ellipsis (x=mean): dispersion (covariance) of the spatial distribution of hole checks. …
Figure 4 with 5 supplements
Trajectory directionality and active sensing for random and static experiments.
Arenas on the top row (mean displacement vector – see color scale between panels B and E) correspond to the ones immediately below them (hole checking spatial distribution); the red ‘A’ label marks …
Figure 4—figure supplement 1
Definition of a hole-checking event as active sensing and target estimation vector (TEV).
(A) Observed trajectory with hole-checking events identified by the two criteria sets of our method, applied subsequently to avoid missed checks. (B) The velocity profile with dots representing the …
Figure 4—figure supplement 2
Estimation of significance for the mean displacement direction calculation.
The jackknife sampling procedure is obtained by the ‘leave-one-out’ rule, yielding N unique jackknife samples of N-1 points from an original sample of N points (see Materials and methods). (A) …
Figure 4—figure supplement 3
Kinetic and geometric features and correlation with active sensing.
(A) Definition of the geometric features that measure performance across trials. Food line: the straight line that connects the food hole (target) to the entrance; this line defines the optimal …
Figure 4—figure supplement 4
Covariance between geometric and kinetic features, and active sensing.
The definition of each of these parameters is given in Figure 4—figure supplement 3. Small filled symbols = average over trajectory for each mouse; large empty symbols = average over mice (N=8) of …
Figure 4—figure supplement 5
Control experiments for path-integration.
(A) Comparison in learning rate between mice with 2D cues on the wall (blue) and mice with no cues (red). N=4 mice per group, Error = SE. No significant difference in performance. (B) Example …
Figure 5
Changing start position after training in static protocol.
Mice are trained in the static entrance protocol to find food at the target labeled ‘A’ (blue circle), and a probe trial is executed with mice entering from a rotated entrance after 18 trials. (A, …
Figure 6 with 2 supplements
Two food location experiment.
(A-D) Trajectory exemplars of four sequential stages of the experiment (all trials done with static entrance, N=8): (A) training target A (trials 1 A and 16 A for early and late learning, …
Figure 6—figure supplement 1
Target estimation vector (TEV) in detail for the random, static and two-target experiments.
Black arrows point to the mean displacement direction starting from any given site in the arena. Panels (A-D) and (H-K) green arrows are the target (food) vectors (point from start to target; or …
Figure 6—figure supplement 2
Minimum distance to alternative target in 2-target condition, and short cut trajectories.
(A and B) Black squares are the minimum distances expected by chance for each trial; they are calculated with respect to a random point, and then averaged over ten such random points. Shaded area …
Figure 7
Trajectory directionality and active sensing in two food location experiment.
Arenas on the top row (mean displacement vector) correspond to the ones immediately below them (hole checking spatial distribution); the red ‘A’ and blue ‘B’ labels mark the targets (food sites), …
Figure 8
Two food location training with 180° rotated probe.
Mice (N=8) are trained to find food in the target labeled ‘A’ (red circle, panel A), and in target labeled ‘B’ (blue circle, panel B) afterwards; then a 180o rotated probe trial (no food; panel C) …
Videos
Video 1
The Shortcut video illustrates a two-food site experiment including hole checks; there were no landmarks.
The mouse was first trained with food at Site A (6 days, 18 trials) and, after training was complete, trained with food at Site B (3 days, 9 trials). The video was taken on a probe trial (Day 10) …
