Hippocampal place cell activity during treadmill locomotion

To study spatial coding in the RSC, we combined a head-fixed locomotion assay28 with chronic cellular imaging (Fig. 1a; see also Methods). C57Bl/6j mice (n = 14 wild-type (WT); n = 4 Thy1 GP4.3 transgenic mice29) were trained to run on a treadmill with tactile stimulus patches on its surface and a fixed reward site (Supplementary Fig. 1a). The animals alternated between high-speed movement and pauses, which were mostly but not exclusively near the reward location (Fig. 1b). Movement speed was approximately constant in between reward events (18.5 ± 5.5 cm s−1, mean ± s.d.; n = 18 mice, 40 runs). Experimental sessions lasted 11.3 ± 2.8 min (mean ± s.d.), yielding 33 ± 17 (mean ± s.d.) complete laps (trials) per session.

Fig. 1 Sparse orthogonal population representation of spatial location in the retrosplenial cortex. a Head-fixed locomotion assay. Mice moved a 150-cm linear treadmill (top) with tactile cues on its surface (bottom). A drop of sucrose water (blue) was delivered at a fixed location for every completed lap. b Lap running behaviour. Movement speed as a function of location for 53 consecutive laps from one experimental session. The animal moved robustly and slowed down or paused most frequently near the reward (as shown by dark colours on the left). c Cellular imaging of neural activity in the retrosplenial cortex (RSC) during head-fixed treadmill running. (top) Illustration of superficial and deep RSC neurons labelled with calcium indicator GCaMP6m (green dots). Calcium imaging was performed with a two-photon microscope through a glass window. (bottom) Tangential view of the labelled superficial RSC neurons with an example imaging field of view (black square). Red lines indicate superior sagittal sinus and transverse sinuses. Scale bar, 1 mm. A: anterior; L: lateral. d Calcium fluorescence signals (top, red) and inferred neural activity (top, black) of six example superficial agranular RSC neurons showing place cell activity; speed and treadmill position are at the bottom. Neural activity was inferred using a fast non-negative deconvolution algorithm 64. e Normalized activity of the six RSC place cells in d as a function of location for multiple laps. The y axis in each colour map corresponds to trial number. Note how neurons were activated as the animal crossed specific locations. Activity was normalized to the time spent at individual locations. f Raster plot showing activation time points for 31 simultaneously imaged RSC place cells, for the same session as in e, together with position (top). Activation time points defined as time points of peak response in each lap for each neuron. Cells ordered by the location that evoked largest responses. Note the repeated sequences of activation during movement and lack of activation when the animal was not moving. g Average normalized activity as a function of location for the 31 RSC place cells shown in f. h Correlation matrix (Pearson correlation coefficient) of population vectors as a function of position for data shown in g. i Correlation matrix (Pearson correlation coefficient) of population vectors as a function of position for data from four mice. (Data from WT mice with AAV1-hSyn-GCaMP6m injections.) Full size image

We expected that treadmill running would entrain neural activity in the hippocampus28 as observed in freely moving animals. To confirm this, we measured cellular activity in dorsal CA1 using chronic cellular imaging30, 31 (n = 1758 neurons, n = 2 WT mice; n = 1 Thy1 transgenic mice) and multisite silicon probe recordings (n = 337 neurons, n = 8 mice) (Supplementary Fig. 1b, e). Consistent with studies in freely moving animals4, CA1 neuronal activity was sparse (calcium transient rate = 0.8, 2.6, 11.3 min−1; 10, 50 and 90% percentiles) and the distribution of firing rates of isolated single units was skewed (10, 50 and 90% percentiles of firing rate = 0.6, 1.8 and 4.7 spikes s−1).

To assess spatial modulation of neuronal activity, calcium time courses were deconvolved32 (Fig. 1d) and expressed as a function of the animal’s location on the treadmill, in 1.5 cm intervals, and normalized by the time spent at each location. We used the resulting position-related activity profiles as measures of spatial tuning. Similar to cellular imaging measurements made in real4 and virtual28, 30 environments, 47% (159/337) of electrically recorded and 26% (452/1758) of optically recorded hippocampal CA1 neurons met established criteria for place cell activity (see Methods) (Supplementary Fig. 1c, f)30, 33. The active neurons showed sequential firing during movement (Supplementary Fig. 1g) and discrete, minimally-overlapping place fields that continuously, but sparsely, covered the treadmill belt (Supplementary Fig. 1d, h)1, 28. In the electrical recordings, place-cell activity was phase locked to low frequency local-field potential oscillations (5–12 Hz; Supplementary Fig. 1c), as observed in freely moving animals34, 35. Thus, running on the treadmill entrains hippocampal CA1 activity in a way that resembles that observed during free running in linear environments.

Spatial activity in the retrosplenial cortex

Next, we used the head-fixed treadmill assay to investigate neural correlates of spatial location in the RSC. We first examined the calcium time courses of 2256 (n = 4 WT mice, 14 sessions) neurons in the superficial agranular region of RSC. The neurons were labelled via AAV1-hSyn-GCaMP6m viral vector injections and imaged with a two-photon microscope through a cranial glass window implanted over the midline31 (Fig. 1c). To study spatial tuning, we expressed neuronal activity as a function of the animal’s location on the treadmill and averaged the result across laps, using the same procedure used for the CA1 recordings (see Methods).

About 13% (297/2256) of RSC neurons labelled with AAV1 viral vector injections had discrete spatially-localized firing fields that resembled the place fields of CA1 neurons. Accordingly, we refer to these cells as RSC place cells. Similar to CA1 place cells (Supplementary Fig. 1d, g, h), the RSC place cells showed spatially localized activity with distinct neurons firing as the animal crossed distinct treadmill locations (Fig. 1e). Within simultaneously imaged neuronal populations, the firing of RSC place neurons sparsely but continuously covered the full length of the treadmill (Fig. 1e–g). The RSC neurons fired in continuous, reproducible sequences during movement but not during stillness (Figs 1f and 2a). Similar results were obtained in Thy1 GP4.3 transgenic mice (n = 3)29 (Fig. 2), which specifically express GCaMP6 in a subset of cortical excitatory neurons. In these mice, the fraction of place cells was larger (56%, 1416/2544, n = 3 mice, eight sessions), possibly due to a bias in favour of a specific group of excitatory neurons. Importantly, there were many active cells in the RSC whose activity did not show place-cell characteristics. The characteristics of these ‘non-place’ cells will be reported elsewhere.

Fig. 2 Tactile stimuli enhance stability of RSC place cell activity. a (left) Normalized calcium activity of 176 simultaneously imaged RSC place cells on a belt with tactile cues. Cells were ordered by the positions of their peak average activity. Position and speed traces are shown below. Dashed lines and blue drops represent reward delivery. (right) trial-averaged position activity for the 176 RSC place cells shown on the left. Belt diagram (top) and speed traces as a function of position (bottom) are shown. Grey lines, speed traces for individual trials; black line, average speed trace. b Same, for 135 neurons imaged during movement on a belt devoid of salient tactile cues. Note the increased positional jitter of RSC place-cell activity in absence of salient tactile stimuli. c Correlation matrices (Pearson correlation coefficient) of population vectors as a function of position for RSC place cells on the cue belt (left) and on the blank belt (right). d Cumulative distributions of spatial information for all RSC place cells on the cue belt (red) and on the blank belt (blue). (Data from Thy1 GP4.3 transgenic mice.) Full size image

Similarity of the RSC and CA1 place codes

The spatial tuning properties of RSC and CA1 place cells were strikingly similar. RSC and CA1 neurons did not differ in the number (Fig. 3c; P = 0.21, χ2-test) and width of place fields (RSC: 39.6 ± 1.2 cm; CA1: 37.0 ± 0.8 cm; both mean ± s.e.m.; P = 0.57, two-sample KS test) (Fig. 3b). In both areas, place fields were distributed over the length of the treadmill; and there was a tendency of higher density of place fields around the reward location (Fig. 3a). This is sometimes observed in CA1 of freely moving animals34.

Fig. 3 Similar spatial response properties of RSC and CA1 place cells. a Place-field fraction as a function of place field location on the track. (top) RSC place cells (n = 297); bottom: CA1 place cells (n = 611, electrophysiology and imaging). Error bars: s.e.m. b Cumulative probability distributions of place-field widths for RSC place cells (black, n = 297) and CA1 place cells (red, n = 452, imaging). c Distribution of place field count per cell for RSC (black bars, n = 297) and CA1 (red bars, n = 611, electrophysiology and imaging) place cells. d Population vector correlation (Pearson correlation coefficient) as a function of distance for RSC (black) and CA1 (red) place cells. Shaded areas represent s.d. Note that the periodicity occurred because of the periodicity of the track. (Data from WT mice with AAV1-hSyn-GCaMP6m injections.) Full size image

To quantify spatial tuning at the level of cell populations, we computed the correlation matrix of the neurons’ position-related activity vectors sorted by their peak location, which quantifies response similarity between cells tuned to distinct locations. Consistent with a sparse orthogonal code, RSC population vectors showed high correlations between nearby locations (i.e., near the main diagonal) and weak correlations between more distant locations (Fig. 1h, i). Averaging the data across positions, the neuronal population vectors showed a steep drop in correlation with distance (Fig. 3d). The correlation structure of RSC activity closely resembled that of CA1 neurons during the same behaviour (Fig. 3d and Supplementary Fig. 1i) and during free movement on real linear tracks35.

Thus, RSC neurons during linear treadmill running show highly tuned spatial activity that resemble the sparse, orthogonal code of location generally observed in the hippocampus and is characteristic of CA1 neurons’ activity measured during the same behaviour.

Place-cell activity across subregions of the RSC

Distinct subregions of the RSC receive distinct inputs and make synaptic connections to distinct targets11, 36, 37, which could indicate functionally specialized domains. To compare spatial activity across subregions of the RSC, we imaged in each animal (n = 3 WT mice) neurons at two distinct depths (150 and 400 µm) and used the lack of GCaMP6 expression in layer 4 to determine the boundaries of RSC subregions in the in vivo images (Fig. 4a and Supplementary Fig. 2a). While hippocampus-like place cells were observed across all subregions of the RSC (Supplementary Fig. 2b), they were substantially more prevalent in superficial regions (agranular and granular). Place cells were found in the superficial agranular (n = 123/861), superficial granular (n = 31/198) and deep layers (n = 60/1237) of the RSC. A similar steep drop in population activity correlation with distance was observed in the three RSC subregions (Fig. 4b). Place cells across RSC subregions had similar spatial response properties (Figs. 4c and d; P = 0.58 for place-field width comparison, One-way analysis of variance; P = 0.27 for place field number comparison, χ2-test); however, there were about 2.5 times more place cells in the superficial agranular and superficial granular layers than the deep layers (superficial agranular: 15 ± 3%; superficial granular: 15 ± 1%; deep layers: 6 ± 2 %; mean ± s.e.m.; superficial vs. deep, both P < 0.05, paired t-test) (Fig. 4e). These results are consistent with the anatomical connections between dorsal hippocampus CA1 and superficial but not deep RSC12.

Fig. 4 RSC place cells are more prevalent in superficial layers. a Diagram of three RSC sub-regions. Sup. agr.: superficial agranular; Deep: deep agranular and granular; Sup. gr.: superficial granular. Green dots represent GCaMP6m labelled neurons. Imaging at different depths revealed neurons in different sub-regions. b Mean population vector correlation as a function of distance for the three sub-regions. Shaded areas represent s.d. c Cumulative probability distributions of place-field widths for superficial agranular (black, n = 123), deep (purple, n = 60), and superficial granular (blue, n = 30) RSC place cells. d Distribution of place field count per cell for superficial agranular (black bars), deep (purple bars) and superficial granular (blue bars) RSC place cells. e Place cell fraction in superficial agranular (black bar), deep (purple bar), and superficial granular (blue bar) RSC. Error bars: s.e.m. (Data from WT mice with AAV1-hSyn-GCaMP6m injections.) Full size image

Tactile stimuli enhance stability of RSC place-cell activity

In CA1, place fields in absence of ‘allocentric’ cues from external stimuli reflect path integration between reward events38, 39. Place-cell activity in the RSC could depend on the integration of self-motion cues (that is, from belt and limb movement), but, if so, would require anchoring to external stimuli, of which only the reward delivery and the tactile cues on the belt were available. To assess the specific contribution of tactile inputs, we compared activity on treadmill belts with and without tactile stimuli. Mice were well-trained in both conditions. We compared the impact of tactile stimuli on CA1 (n = 1 Thy1 transgenic mouse) and RSC (n = 3 Thy1 transgenic mice) activity.

Place-cell activity in the hippocampus was maintained in the reference frame of the reward stimulus in absence of tactile stimuli (Supplementary Fig. 3). CA1 neurons showed seamless sequential firing during movement on belts with and without tactile stimuli (Supplementary Fig. 3a, b). The tactile stimuli had little impact on population spatial tuning, showing the same rapid decrease in correlation with distance in presence and absence of tactile stimuli (Supplementary Fig. 3c). Spatial information (SI) encoded in the neurons’ activities was also similar, differing by <10% between the two conditions (Supplementary Fig. 3d; SI: cue belt, 0.93 ± 0.02; blank belt, 0.86 ± 0.03, both mean ± s.e.m.; P = 0.03, two-sample t-test).

Place-cell activity in absence of tactile cues was also maintained in RSC (Fig. 2). On the treadmill devoid of tactile stimuli, RSC neural population showed sequential firing (Fig. 2b) and similar spatial tuning (Fig. 2c). However, RSC neurons showed more pronounced trial-to-trial position jitter in the absence of tactile stimuli (Fig. 2b). This resulted in reduced tuning of the population (Fig. 2c) and lower SI in the neurons’ activity (Fig. 2d; SI: cue belt, 0.86 ± 0.03, mean ± s.e.m., n = 493; blank belt, 0.61 ± 0.04, mean ± s.e.m., n = 224; P = 2.4e-07, two-sample t-test).

Thus, while tactile stimuli were not required for place cell activity in RSC, tactile stimuli enhanced the reliability of the RSC spatial representation somewhat more than in the hippocampus. Although the place-cell activities observed in CA1 and RSC during treadmill running are similar, the more pronounced impact of manipulating tactile stimuli suggests that RSC responses may depend more on external sensory inputs; however, since there were few mice, we cannot rule out a contribution from between-animal differences.

RSC population encodes ‘allocentric’ treadmill position

Do RSC neurons encode location on the treadmill independent of task context? In the hippocampus, place fields rearrange seemingly unpredictably upon changes of environment and/or task context40,41,42, a process referred to as place-field remapping. Within a single environment, in the hippocampus, neurons rarely remap their place fields to different locations in response to external cue manipulations; most maintain their place fields at the same location but may adjust their in-field firing rates. In some cases this ‘rate-remapping’ is strong enough that, without adjustment of plotting scale, a few fields may seem to appear or disappear41. These firing rate changes allow the hippocampus to encode uniquely the conjunction of location and the behaviours and experiences that occur at a given location.

To probe remapping in the RSC, we examined, in separate experiments, the impact of changes of room illumination and changes of the reward site location on activity in RSC and CA1. The RSC receives strong inputs from the visual cortex43 and even shows visual responses44. The reward site location impacts running trajectories (Figs 1b and 2a, b) on the treadmill and serves as a salient and reliable event that anchors path integration in the hippocampus. These two factors could influence RSC activity. We compared place-cell activity under photopic illumination and in complete darkness, as well as activity with reward delivered at either of two locations. In all experiments, the belt bore tactile stimuli.

RSC place-cell activity was robust to changes in illumination (Fig. 5a and Supplementary Fig. 4a). To compare place-cell activity we computed the neurons preferred locations as well as the correlation of the position-related population activity profiles. RSC place-cell activity was highly correlated across light and dark conditions (Fig. 5b and Supplementary Fig. 4a). However, a small fraction of the neurons showed rearrangement of their place fields (Supplementary Fig. 4a). Similar results were obtained in CA1 (Supplementary Fig. 4b).

Fig. 5 Correlated RSC population representations of position upon environmental illumination change and reward relocation. a Spatial tuning curves of three example RSC place cells showing preserved place fields under light (black) and dark (red) conditions. b Population vector correlation (Pearson correlation coefficient) matrix for all RSC place cells under the light and dark conditions. Blue drops indicate reward locations. c (top) Diagram of the reward shift experiment. (bottom) Spatial tuning curves of two example RSC place cells showing preserved place fields when the reward site was shifted. Black lines, spatial tuning curves before reward shift; red lines, spatial tuning curves after reward shift. Blue drops indicate reward locations. d Population vector correlation (Pearson correlation coefficient) matrix for all RSC place cells under original and reward shifted conditions. Blue drops indicate reward locations. (Data from Thy1 GP4.3 transgenic mice a, b and WT mice with AAV1-hSyn-GCaMP6m injections c, d.) Full size image

Likewise, RSC place-cell activity was also robust to changes in reward site location (Fig. 5c). RSC place-cell activity may encode location in the reference frame defined by the tactile cues, or it may encode position relative to the reward site45, 46, possibly by path integration, as in hippocampus38, 39. However, RSC place fields were mostly preserved when the reward was shifted (Fig. 5c and Supplementary Fig. 5a). The population representations of position before and after change in reward location were highly correlated (Fig. 5d). However, a number of place cells moved their place fields to near the location of the reward site (Supplementary Fig. 5a). Similar results were obtained in CA1 (Supplementary Fig. 5b).

We conclude that, in presence of spatially-informative tactile stimuli, most RSC neurons encode location in the spatial frame defined by the tactile stimuli. This activity is similar to CA1 place-cell activity measured during the same behavioural assay. It is not driven by eye inputs and is only weakly affected by contextual cues such as the reward site location. However, a fraction of the cells respond to contextual changes by moving their place fields. This could allow the RSC population to distinguish between distinct contexts.