Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1

Abstract

The mammalian hippocampus is critical for spatial information processing and episodic memory. Its primary output cells, CA1 pyramidal cells (CA1 PCs), vary in genetics, morphology, connectivity, and electrophysiological properties. It is therefore possible that distinct CA1 PC subpopulations encode different features of the environment and differentially contribute to learning. To test this hypothesis, we optically monitored activity in deep and superficial CA1 PCs segregated along the radial axis of the mouse hippocampus and assessed the relationship between sublayer dynamics and learning. Superficial place maps were more stable than deep during head-fixed exploration. Deep maps, however, were preferentially stabilized during goal-oriented learning, and representation of the reward zone by deep cells predicted task performance. These findings demonstrate that superficial CA1 PCs provide a more stable map of an environment, while their counterparts in the deep sublayer provide a more flexible representation that is shaped by learning about salient features in the environment. VIDEO ABSTRACT.

Figure 1. Optical dissection of the radial axis of hippocampal area CA1

(a) Left: Experimental schematic of simultaneous two-photon imaging of GCaMP6f in deep and superficial sublayers of dorsal CA1 using a piezoelectric crystal. Right: Time-averaged image sequences from a representative recording session. Planes were separated by 25 µm. Bottom: Timing of frame acquisition. (b) Schematic of the behavioral apparatus. Head-fixed mice ran on a cue-rich 2 m treadmill while tone, light, odor, and reward were presented by a microcircuit controller. The experimental timeline is shown below. (c) Left: Representative fluorescence traces from spatially-tuned deep and superficial CA1 PCs. Belt position is plotted at bottom. Detected Ca²⁺ transients (p<0.05) are indicated in color. Middle: Polar trajectory plots from the same cells. Position and time are represented as the angular and radial coordinates, respectively. Significant running-related transients are indicated with dots. Right: Spatial tuning plots. Each running-related transient is represented as a vector (direction = position at onset, magnitude = inverse of occupancy). The complex sum of these vectors forms the tuning vector (black). (d) Spatial tuning heatmaps from a single recording session performed at low zoom. Each row represents a cell, and the x-axis represents the treadmill position. Heatmap intensity reflects the occupancy-normalized transient rate, and each cell is normalized to peak. Identified place cells are shown at top sorted by peak. (e - f) Firing properties and spatial coding features of deep and superficial CA1 PCs. Within-session averages of paired deep and superficial recordings (either during RF or GOL) are indicated by small closed dots. Within-mouse averages are indicated by the large colored dots. Means across mice are shown in the insets (mean +/− s.e.m.). (e) Activity rate (area under curve of significant transients per unit time, AUC/min) is defined as the area under significant Ca²⁺ transients divided by recording duration. Deep cells were significantly more active than superficial (n=14 mice, p<0.001, paired T-Test). (f) Place cell fraction as determined by spatial information. A higher fraction of deep CA1 PCs are identified as spatially tuned (n=14 mice, p<0.001, paired T-Test). (g) Place cell fraction as determined by tuning specificity. A higher fraction of deep CA1 PCs are identified as spatially tuned using this definition as well (n=14 mice, p<0.001, paired T-Test). (h) Tuning specificity was modestly but consistently higher in superficial than deep CA1 PCs (n=14 mice, p<0.05, paired T-Test).

Figure 2. Remapping of superficial and deep place fields during random foraging (RF)

(a) Experimental schematic. Mice ran on the treadmill for randomly administered water rewards (3/lap) during three 12-minute sessions in contexts A and B. Sessions were separated by 60–90 min. Five mice underwent this protocol for 1–3 days. (b) Multisensory contexts A and B differed in auditory, visual, olfactory, and tactile cues. (c) Population vector analysis schematic. Rate maps for individual cells (rows) were combined to form population vectors (columns) as a function of position. Vectors corresponding to the same position were compared in the A-B and B-B conditions. (i) Scatterplots of the mean PV correlation across position bins in each FOV in the B-B vs. A-B conditions (n=15 FOVs). Statistical tests were performed by averaging across FOVs to obtain one measure per mouse (n=7). (ii-iv) Individual FOVs are represented as lines, and circles indicate means across mice (mean +/− s.e.m.). (ii) A-B: Deep and superficial cells remapped similarly (n=7 mice, p=0.31, paired T-Test). (iii) B-B: PV correlations were greater in superficial than in deep (n=7 mice, p=<0.001, paired T-Test). (iv) [(A-B) – (B-B)]: Both sublayers were more stable in the B-B than A-B conditions (n=7 mice; Deep: p<0.05; Superficial: p<0.001; 1-Sample T-Test vs. 0), though the magnitude of the Δstability, the difference in stability between conditions, was greater among superficial cells (n =7 mice, p<0.01, paired T-Test). (d) Spatial tuning curves shown for example deep and superficial cells. The tuning curve correlation is the 1D correlation between spatial tuning curves. (i) Scatterplots of tuning curve correlations in A-B and B-B for deep and superficial FOVs. (ii-iv) Data presented as in (c). (ii) A-B: tuning curve correlations were similar between deep and superficial (n=7 mice, p=0.19, paired T-Test). (iii) B-B: tuning curve correlations were higher in superficial than deep (n=7 mice, p<0.001, paired T-Test). (iv) [(B-B) – (A-B)]: Both sublayers were more stable in the B-B than A-B conditions (n=7 mice; Deep: p<0.05; Superficial: p<0.01; 1-Sample T-Test vs. 0); the magnitude of the difference was higher in superficial than deep CA1 PCs (n=7 mice, p<0.05, paired T-Test).

Figure 3. Goal-oriented learning task for head-fixed imaging

(a) Schematic of the goal-oriented learning (GOL) task. Mice (n=6) searched for an unmarked reward zone, and water rewards were administered only when the mice licked within the fixed 10 cm goal. At the end of condition I, the reward was moved to a new location of the belt, and the experiment was repeated. The same context (A) was maintained throughout. (b) Representative licking data from four individual experiments from one mouse performing the task. The fraction of total licks is plotted as a function of position on the belt (50 bins, 4 cm per bin). The reward zone is shaded gray. On the first day of the experiment, the mouse licked diffusely throughout the belt as it searched for the hidden reward zone. By the last day of condition I, the mouse licked selectively at the reward location. After the reward was moved, the mouse continued to lick at the original reward location. It eventually reverted to an exploratory licking state, and by the end of condition II the mouse selectively licked at the new reward location. (c) Peri-stimulus time-histogram (PSTH) of licking rate triggered on reward zone entry for the first (black) and last (cyan) days of each condition. PSTHs were calculated for each mouse and smoothed with a one-second Hamming filter. Shaded regions indicate mean +/− st. dev. across mice. Licking was initially diffuse. By the last day, licking outside the reward zone was largely suppressed and rose sharply prior to reward zone entry, reflecting expectation of reward. The effect was highly consistent across mice. (d) Anticipatory licking (fraction of non-reward zone licks in the 10 cm preceding the reward) increased significantly by the end of learning (n=6 mice, p<0.01, paired T-Test). Error bars indicate mean +/− s.e.m. across mice. (e) The fraction of licks occurring within the reward zone aggregated by recording session and plotted by day (mean +/− s.e.m. across mice). Over time, licking became more selective for the reward zone.

Figure 4. Sublayer-specific modulation of activity by the GOL task

(a) Stability in the B-B condition of RF compared to session-to-session stability in GOL (similar elapsed time of ~90 min.). Deep but not superficial CA1 PCs showed a significant increase in PV correlation in the GOL task as compared to RF (n=7 RF mice, 6 GOL mice; deep: p<0.05; superficial: p=0.36; Mann-Whitney U Test). Error bars indicate mean +/− s.e.m. across animals. A two-way ANOVA analysis is included in Supplemental Table 1. (b) The magnitude of the task modulation was compared across sublayers by performing two shuffling procedures: randomizing cell identity and randomizing experiment identity. Both comparisons suggested the magnitude of task modulation was greater for deep than for superficial cells (p<0.001, p<0.01). (c) The same analysis was performed with tuning curve correlation. Deep but not superficial CA1 PCs showed a significant increase in tuning curve correlation in the GOL task as compared to RF (n=7 RF mice, 6 GOL mice; deep: p<0.05; superficial: p=0.11; Mann-Whitney U Test). Error bars indicate mean +/− s.e.m. across animals. A two-way ANOVA analysis is included in Supplemental Table 1. (d) Both shuffles showed the magnitude of the task modulation was greater for deep than superficial (p<0.01, p<0.01).

Figure 5. Sublayer-specific modulation of activity in the reward zone

(a - c) For each sublayer (first and second columns) we compared place field properties near the reward (green, within π/8 radians, ~12 cm on either side of the center of the reward zone) versus away from the reward (purple, π/8 to π/2 radians, ~12–50 cm). We performed a paired comparison of the in vs. out differences to directly compare reward-related modulation between the sublayers (third column). Error bars indicate mean +/− s.e.m. across animals. (a) Day-to-day centroid shift. Deep place fields near the reward were significantly more stable at 24 hours than those away from the reward (n=6 mice, p<0.01, paired T-Test). Superficial CA1 PCs did not show this trend (n=6 mice, p=0.17, paired T-Test). The direct, paired comparison of the in versus out stability [(shift_out - shift_in)_D vs. (shift_out - shift_in)_S] showed that deep cells were stabilized by the reward to a greater degree than superficial cells (n=6 mice, p<0.05, paired T-Test). (b) Day-to-day tuning curve correlation. As in (a), deep place fields near the reward were more stable than those away from it (n=6 mice, p<0.05, paired T-Test); this relationship was absent in the superficial sublayer (n=6 mice, p=0.51, paired T-Test). The direct comparison of the sublayers showed that deep cells were stabilized by the reward to a greater degree than superficial (n=6 mice, p<0.05, paired T-Test). (c) Place field width. In both sublayers, place fields were narrower near the reward than they were away from it (n=6 mice, deep: p<0.001, superficial: p<0.05, paired T-Test). The magnitude of the effect was greater for deep than for superficial (n=6 mice, p<0.05, paired T-Test).

Figure 6. Reward zone representation versus performance on the GOL task

(a) Time-averaged images of the deep sublayer from two different recording sessions are shown in grayscale. Identified place cells from each session are overlaid and colored according to the distance of their centroid to the reward (green = near, purple = away). The mean distance to the reward zone is indicated (radians). (b) As in (4b), licking distributions from the two experiments corresponding to (a). (c) Mean distance of each place cell centroid to the reward on first and last days of the experiment. On the last day, the mean distance to reward was significantly different from the chance level of π/2 for both sublayers (dashed line; n=6 mice, one sample T-Test, deep: p<0.001, superficial: p<0.05), but was not significantly different between sublayers (n=6 mice, p=0.95, paired T-Test). Error bars indicate mean +/− s.e.m. across animals (d) We did not detect a relationship between distance to reward at the end of condition II with distance at the end of I, sublayer, or with the interaction [Type II ANOVA, n=172 deep, 336 superficial place cells, F(Distance end of I) = 0.10, p(Distance end of I) = 0.76; F(layer) = 0.54, p(layer) = 0.46; F(interaction) = 0.11, p = 0.74]. The dashed line represents the mean distance expected in the case of a uniform place field distribution, and error bars indicate mean +/− s.e.m. across cells. (e) Fraction of licks in reward zone (P) plotted against D_D (top) and D_S (bottom). Individual points represent single recording sessions. The dashed line indicates the linear fit with the 95% confidence interval shaded. We observed a significant relationship between D_D and P (n = 91 sessions, r= −0.40, p<0.001, Pearson’s r) but not between D_S and P (n=91 sessions, r= −0.15, p = 0.15, Pearson’s r). (f) In order to directly compare the sublayers’ relationships to performance on the GOL task, we compared the magnitude of the difference in correlation coefficients (0.25, dashed line) relative to a null distribution (see Methods). The true difference (dashed line) fell outside the shuffle distribution (p<0.001).