Internally Recurring Hippocampal Sequences as a Population Template of Spatiotemporal Information

Abstract

The hippocampus is essential for spatiotemporal cognition. Sequences of neuronal activation provide a substrate for this fundamental function. At the behavioral timescale, these sequences have been shown to occur either in the presence of successive external landmarks or through internal mechanisms within an episodic memory task. In both cases, activity is externally constrained by the organization of the task and by the size of the environment explored. Therefore, it remains unknown whether hippocampal activity can self-organize into a default mode in the absence of any external memory demand or spatiotemporal boundary. Here we show that, in the presence of self-motion cues, a population code integrating distance naturally emerges in the hippocampus in the form of recurring sequences. These internal dynamics clamp spontaneous travel since run distance distributes into integer multiples of the span of these sequences. These sequences may thus guide navigation when external landmarks are reduced.

Figure 1

CA1 Dynamics Display Recurring Sequences of Neuronal Activation during Run Epochs (A) (A1) Cartoon of the experimental setup with the mouse head-fixed below the objective (allowing for two-photon imaging, red box, A2) but free to run on a non-motorized treadmill. (A2) Schematic of the imaging conditions. (A3) Representative behavior on two imaging sessions shows alternation between run (light) and rest (dark) periods. Corresponding speed as a function of time is displayed below (black). (B) (B1) Two-photon in vivo GCAMP5 fluorescence image of the CA1 pyramidal layer; scale bar, 100 μm. (B2) Contour map of imaged cells with neurons recruited in a sequence (red), and other active neurons (green). (C) Individual calcium fluorescence signals as a function of time (top) of one representative cell displaying sustained firing (green) and three cells (red) recruited in four consecutive sequences visible on the rasterplot (middle) displaying a heatmap of the signal of all the neurons involved. Corresponding mouse behavior is indicated on the box below. (D) (Left) Rasterplots of neuronal activation as a function of time for two sequences occurring either during continuous run or including a short immobility period (dark green) and slope fit (red line); bottom plots indicate mouse speed as a function of time. Right rasterplot represents the pooled distribution of neuronal activation onsets (median and interquartile range) for all the sequences including a short immobility period, taking the start of the pause as a time reference (time 0, neuron #0, n = 91 pauses, 5 sessions, 3 mice). Temporal slopes before and after the pauses are indicated (red lines). Right plot shows the evolution of the normalized temporal slope (red); 95% confidence interval is indicated (CI, gray area, see Experimental Procedures).

Figure 2

Distance-Modulated Sequences (A) (A1) Rasterplots of neuronal activation as a function of run time (black) and robust fits (red) of three sequences occurring at different running speeds (bottom, raw data gray, median speed black). (A2) Superimposed fits for the three examples in (A1). (B) (B1) Graph of speed versus temporal slope for each sequence (dots) recorded in this representative imaging session; solid line indicates fit through origin while dashed line indicates ±10% interval (slope, 0.64 cell/cm). (B2) Graph plotting speed versus spatial slope for the same set of sequences (dots). Spearman correlation coefficient and corresponding p value are indicated. (B3) Graph plotting, for each imaging session (n = 28 sessions, 5 mice), the Spearman correlation coefficients for the spatial (ρt) and the temporal slope (ρd). Many sessions fell in the area where more information is carried by run distance (gray). Significant sessions for distance, time or nonsignificant representations are indicated by black, open or gray dots, respectively. (C) (C1) Representative rasterplot of neuronal activation as a function of run distance displaying successive sequences within an imaging session. Sequences were recurring but separated by gaps of non-encoded run distance (gray areas). (C2) Histogram plotting the distribution of 14 imaging sessions as a function of the fraction of the total run distance encoded within a distance sequence. (D) Polar representation of the firing field of representative neurons with 360° corresponding to one lap as schematized on the top left; firing probability is represented by the heatmap while black dots indicate activation onsets; three cells from an experiment on an empty treadmill (D1), three with tactile cues (D2). Heatmap represents the probability distribution of activation onsets. (E) p values obtained from the uniformity test (Kolmogorov-Smirnoff) applied to the distribution of firing onsets of individual cells along the track (1,048 cells, pooled data over all sessions displaying sequences); red line indicates the statistical threshold (0.05). (Inset) Representative distribution of the onsets of one cell displaying a uniform distribution (p = 0.43).

Figure 3

Distance-Modulated Sequences Can Repeat One after the Other within a Continuous Run (A) Representative rasterplots of neuronal activation as a function of time for three run epochs (light green) displaying an integer number of sequences during the same imaging session. Bottom graphs indicate corresponding speed as a function of time. (B) (B1 and B2) Perievent triggered distribution of the mouse speed (median: black line, inter-quartile range: gray area) aligned to the onset (B1) or to the offset (B2) of all first DS detected in each run epoch (n = 325 DS). (B3 and B4) Same as (B1) and (B2), but speed distribution now uses the onsets (B3) or the offsets (B4) of the DS that repeated after another in the middle of a run epoch (n = 64 DS). Gray area indicates the 95% confidence interval (CI). (C) Representative plot showing the uniform distribution on the absolute track position of repeating DS onsets for one imaging session (n = 25 DS, Kolmogorov-Smirnoff test, p > 0.1).

Figure 4

The Distance Unit Provides an Internal Template that Varies on a Daily Basis (A) Evolution of the “distance unit” over four daily imaging sessions for five mice (colored lines). (B) Representative example of the contour maps of imaged cells indicating neurons involved in distance sequences on the first day where sequences could be imaged (yellow, left) and the next (middle, red) or both (orange, right and black contours on all three maps). (C) (C1) Rasterplots showing the activation, as a function of distance, of the nine cells involved in distance sequences on both days. Neurons are ordered based on their average activation delay on the first day; day 1 (left), day 2 (right). (C2) Superimposed average rasterplot of the sequences on both days (day 1, orange; day 2, red); the median delay of onset (dot) and the corresponding interquartile range (rectangle) are indicated for each cell and calculated taking cell #5 as reference. Note that the “distance unit” (DU) calculated for each day (same color code) varied significantly. (D) Probability distribution histograms of the normalized (1) and absolute (2) run distance (n = 235 run epochs, 10 sessions with a median run distance smaller than 2 DU). The normalized run distance displays a bimodal distribution (r² = 0.98) with a first peak at 1.1. DU and a significant second one at 2.1 DU (Figures S4D–S4H, ^∗∗∗p < 0.001). The absolute distance histogram distribution is log-normal (r² = 0.91) with a peak at 77 cm. (E) Graph indicating the correlation between the average size of single-cell distance fields (median values and interquartile ranges, see Experimental Procedures) and the “distance unit”; the fit (red, Pearson, R = 0,94, p < 0.001) indicates that the average distance field represents one-fifth of the distance unit.