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Impaired spatial selectivity and intact phase precession in two-dimensional virtual reality

Abstract

During real-world (RW) exploration, rodent hippocampal activity shows robust spatial selectivity, which is hypothesized to be governed largely by distal visual cues, although other sensory-motor cues also contribute. Indeed, hippocampal spatial selectivity is weak in primate and human studies that use only visual cues. To determine the contribution of distal visual cues only, we measured hippocampal activity from body-fixed rodents exploring a two-dimensional virtual reality (VR). Compared to that in RW, spatial selectivity was markedly reduced during random foraging and goal-directed tasks in VR. Instead we found small but significant selectivity to distance traveled. Despite impaired spatial selectivity in VR, most spikes occurred within 2-s-long hippocampal motifs in both RW and VR that had similar structure, including phase precession within motif fields. Selectivity to space and distance traveled were greatly enhanced in VR tasks with stereotypical trajectories. Thus, distal visual cues alone are insufficient to generate a robust hippocampal rate code for space but are sufficient for a temporal code.

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Figure 1: Similar rat behavior but different neural rate maps in two-dimensional RW and VR.
Figure 2: Reduced activity, spatial selectivity and stability of rate maps in VR.
Figure 3: Dependence of spatial selectivity on task type and locomotion cues.
Figure 4: Selectivity to distance traveled in VR goal-directed tasks.
Figure 5: Similar hippocampal motifs and motif fields in RW and VR.
Figure 6: Intact but variable phase coding in VR.

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Acknowledgements

We thank F. Quezada and B. Popeney for help with behavioral training, F. Quezada for help with spike sorting, N. Agarwal for help with electrophysiology, B. Willers for help with the analyses, P. Ravassard and A. Kees for help with surgeries, technical support and manuscript comments, D. Aharoni for help with hardware and the participants of the Kavli Institute for Theoretical Physics workshop on 'Neurophysics of Space, Time and Learning' for discussions. This work was supported by grants to M.R.M. from the US National Institutes of Health (5R01MH092925-02) and the W.M. Keck foundation. Results presented in this manuscript were uploaded on a preprint server BioRxiv in December 2013 at http://dx.doi.org/10.1101/001636.

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Authors and Affiliations

Authors

Contributions

L.A., J.D.C., Z.M.A. and M.R.M. designed the experiments. L.A., C.V. and J.D.C. performed the experiments. Z.M.A. and J.J.M. performed analyses with input from M.R.M. Z.M.A., L.A., J.J.M. and M.R.M. wrote the manuscript with input from other authors.

Corresponding author

Correspondence to Mayank R Mehta.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Additional example cells in RW and VR showing lack of spatial selectivity in VR.

a, Rat trajectory and spike positions for different neurons and corresponding firing ratemaps in RW. b, Same as (a) but in VR, showing long streaks of spikes, or putative motifs. Numbers indicate firing rate range. Color conventions are the same as in Fig. 1.

Supplementary Figure 2 Reduced mean firing rates, rate map sparsity and coherence in VR.

a, Mean firing rates were 25% (p = 7.6×10−20) lower in VR (0.70±0.02Hz) than in RW (0.93±0.02Hz). b, Ratemap sparsity, a measure of spatial selectivity, was also greatly (42%, p=2.3x10-162) reduced in VR (0.42±0.01) compared to RW (0.72±0.01). c, Ratemap coherence computed using 10x10cm bins, was 40% (p = 2.3×10−157) reduced in VR (0.45±0.01) compared to RW (0.75±0.01). d, At all mean rates, spatial information content was negatively correlated with the mean firing rate of a cell in both worlds (RW r = −0.36, p = 1.6×10−27; VR r = −0.48, p = 3.2×10−33). e, Spatial stability was lower in VR compared to RW. Stability was not correlated with mean firing rate in RW (r = 0.02, p = 0.54) and weakly positively correlated in VR (r = 0.28, p = 1.1×10−11).

Supplementary Figure 3 Estimation of the significance levels of spatial selectivity showing VR results were near chance levels.

To quantify spatial information content, ratemap sparsity and stability that are uninfluenced by the mean firing rate of a cell, these were computed in Z-scored units for each cell (see Methods). a, Z-scored spatial information content was only slightly greater than zero in VR (0.92±0.08, p = 3.2×10−27) but the difference was far greater in RW (20.65±0.49, p = 7.7×10−140), and the two distributions were significantly different (difference = 19.73, p = 7.4×10−206). b, Similar to information content, Z-scored ratemap sparsity was only slightly greater than zero in VR (0.91±0.07, p = 3.4×10−32) but the difference was far greater in RW (10.26±0.20, p = 7.7×10−140). These two distributions were significantly different (difference = 9.35, p = 9.5×10−200). c, The Z-scored stability was close to zero in VR (0.13±0.06, p = 0.036) but significantly above chance in RW (3.99±0.09, p = 1.0×10−135; difference = 3.86, p = 1.2×10−155).

Supplementary Figure 4 Loss of spatial selectivity in dynamic rate maps and reduction in neuronal coactivation in VR.

a, Spatial ratemaps of two pairs of neurons in RW (left) and their dynamic ratemap (right) (see Methods) showing spatially localized activity. Numbers on top right indicate firing rate range. b, Same as (a) but for two pairs of neurons in VR showing no spatial selectivity. c, Dynamic ratemap information content in RW (0.63±0.01bits, n = 10831 pairs from 4 rats) was 65% greater (p<10−100) than in VR (0.22±0.00bits, n = 8202 pairs from 4 rats). d, Dynamic ratemap sparsity in RW (0.56±0.002) was also greater (36%, p<10−100) than in VR (0.36±0.002). The relative spiking of coactive neurons was spatially informative in RW but not in VR. e, In order to investigate coactivity of cell pairs (including sequential activity on intermediate time- and length scales) we computed cross-covariances between the firing rates of pairs of active cells in a session as a function of time elapsed or distance traveled (see methods). The fraction of coactive cells in RW (15.5(16.8)% in distance(time) domain) was far greater than that in VR (8.3(8.9)% in distance(time) domain).

Supplementary Figure 5 Comparison of activities of cells active in both RW and VR on the same day.

a, For cells recorded in both worlds on the same day mean firing rate was correlated regardless of minimum firing rate (grey, r = 0.32, p = 1.7×10−7, n = 258 from 3 rats). This was also true for the subset of cells active at high rates in both worlds (purple, r=0.21, p=0.03, n = 109 from 3 rats), used for all subsequent same-cell analyses. b, The peak firing rate of the same cell was reduced in VR compared to RW and the two were not significantly correlated (r = 0.12, p = 0.23), despite their correlated mean rates, due to lack of spatial selectivity in VR. c, Spatial ratemap sparsity of the same cell was also reduced in VR but correlated with RW (r = 0.36, p = 0.0001), which could be partially explained by correlated mean firing rates (Fig. 2e). d, Despite positive correlations in mean rate and sparsity, the distribution of correlation of ratemaps of the same cells between RW and VR is not significantly different from zero (p = 0.39) and not different from the ratemap correlations obtained by shuffling the cell identities (p = 0.97).

Supplementary Figure 6 Quantification of behavior and neural responses during goal-directed VR tasks.

a, Rats’ sample trajectories between two reward locations and the corresponding shortest path between them in the VR random-pillar task (left) and VR systematic-pillar tasks (center and right). b, We defined the excess path length as the difference between the shortest distance between two consecutive reward locations and the actual path length traveled by the rat. We then calculated the median value of this excess path length over an entire session. The rats’ behavior was more goal-directed during the VR random-pillar task because the median excess path length (56.3 ± 10.8 cm) was significantly smaller compared to random foraging task (178.2 ± 13.9 cm, p = 6.1×10−4). A similar effect was observed in VR systematic-pillar where the median excess path length (77.3 ± 12.2 cm) was significantly shorter compared to random foraging (178.2 ± 13.9 cm, p = 1.4×10−5). Further, VR random-pillar and VR systematic-pillar were equally goal-directed because the median excess path lengths were comparable in the two conditions (p = 0.44). c, Ratemap stability in the VR systematic-pillar task (0.34 ± 0.03, n = 282 cells with at least 100 spikes in each session half from 3 rats) is greater than VR random foraging (p = 2.4x10-3) and smaller than RW random foraging (p = 1.8×10−18).

Supplementary Figure 7 Additional example cells in VR in systematic-pillar tasks.

a, Rat trajectory and spike positions for different neurons (top row) and corresponding firing ratemaps (bottom row) for the two-pillar task. b, Same as (a) but for the three-pillar task. Numbers indicate firing rate range. Color conventions are the same as in Fig. 3. Note that examples show elevated firing along only one or multiple arms of the triangle.

Supplementary Figure 8 Selectivity to distance traveled in VR goal-directed tasks and presence of disto-code in the three-pillar task.

a, Left: Trajectory of the rat (light brown) and spike positions (dark brown) during the VR random-pillar task on the two-dimensional platform for the same cells shown in Fig. 4a. Note that the cells fire randomly in two-dimensions although one of them (bottom panel) does exhibit selectivity to distance along the linearized path. Right: Same as left but for VR systematic-pillar task (trajectory and spikes are depicted in light and dark green respectively). The black dots indicate the reward locations and the arrows correspond to running direction. b, Significance levels (p values) for population vector overlap in Fig. 4d. The significant diagonal is indicative of firing at the same distance along the two arms (disto-coding). c, Disto-coding index (see Methods) for the population of multi-arm selective arm pairs (n = 431 arm pairs from 3 rats) in the three-pillar task was also significantly positive (0.23±0.02,p = 1.5×10−31), further supportive of a disto-code.

Supplementary Figure 9 Comparable spatiotemporal properties of individual motifs and motif fields in RW and VR.

a, For each cell we computed the mean firing rate within individual motifs and calculated the mean of those values to obtain a single number for individual cells. Motif mean rates in VR (5.92±0.06Hz) were slightly smaller (10%, p = 7.7×10−10) than in RW (6.52±0.06Hz). b, Similarly, motif peak rates in VR (23.39±0.24Hz) were smaller (21%, p = 6.1×10−21) than in RW (28.32±0.69Hz). c, There was significant correlation between mean rate and the percentage of spikes that occurred within motifs in RW (r = 0.54, p = 4.1×10−65) and VR (r = 0.41, p = 1.2×10−28). This could explain the reduced motif duration and percentage of spikes contained in motifs in VR compared to RW (Fig. 5e). d, In both RW and VR, the percentage of spikes in motifs was significantly correlated with spatial information content of a neuron (RW r = 0.28, p = 4.2×10−17; VR r = 0.26, p = 6.5×10−12). e, Motif-field mean firing rates in VR (4.12±0.05Hz) were only slightly smaller (5%, p = 9.2×10−3) than in RW (4.34±0.05Hz). f, Motif-field durations in VR (1.33±0.01s) were similar but slightly reduced (10%, p = 1.1×10−12) compared to RW (1.48±0.01s). g, For cells active in both worlds on the same day, motif-field duration was correlated between RW and VR (r = 0.31, p = 1.2×10−3). h, Motif-field peak firing rate had a similar correlation (r = 0.54, p = 1.2×10−9). i, j,To estimate the percentage of spikes contained in motifs and motif durations, uninfluenced by the mean rate, we computed the Z-scored values for these two measures (see Methods). i, The Z-scored percentage of spikes in motifs was significantly above zero in VR (35.15±1.06, p=3.9x10-83) and RW (23.52±0.64, p = 1.0×10−26). In fact larger Z-scored values in VR indicate greater propensity for motif generation compared to RW. j, The Z-scored mean motif duration was indeed similar in both worlds (8.02±0.25 in RW and 7.33±0.27 in VR, p = 0.03) and greatly above zero (p = 2.1×10−96 and p = 1.4×10−83 in RW and VR respectively).

Supplementary Figure 10 Increased theta power but reduced theta frequency in VR.

To further examine the dynamics of LFP theta, we investigated the LFPs recorded from the same electrode on the same day in both worlds without any electrode movement between the two sessions. Analysis was further restricted only to data when rats ran at speeds greater than 5cm/s to eliminate contamination by variable periods of stopping when theta is reduced. In order to compare data from different sessions, the power spectrum from each electrode was normalized by the mean power on that electrode in RW and VR over the frequency range 1-100 Hz.

a, Normalized power between 5-15 Hz, averaged over all the LFP (n = 57 from 3 rats) in RW and VR shows a clear difference in theta power and frequency between the two environments. b, Peak theta power is significantly increased (p = 0.002, paired Wilcoxon signed rank test) in VR (56.95±3.75) compared to RW (46.61±2.51). c, Theta frequency in VR (7.21±0.07Hz) is significantly lower (p = 5.1×10−11) than in RW (8.32±0.06Hz).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 (PDF 2044 kb)

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RW Random Foraging: Spatial Selectivity.

Animation of experimental data when a rat was performing the RW random foraging task during a representative session with spikes from a place cell overlaid over the rat's trajectory. The rat's position is represented by the moving grey shape. The light blue trace corresponds to the trajectory of the rat and the dark blue dots indicate where the spikes occurred. Note that the activity of the neuron is composed of motifs that occur in a restricted region of space. (MOV 4920 kb)

VR Random-Pillar: No Spatial Selectivity.

Animation of experimental data when a rat was performing the VR random-pillar task during a representative session with spikes from a putative pyramidal neuron overlaid over the rat's trajectory. The rat's position in virtual space is represented by the moving grey shape. The light brown trace corresponds to the trajectory of the rat and the dark brown dots indicate where the spikes occurred. Note that the activity of the neuron is composed of motifs that are distributed nearly randomly in space. (MOV 5096 kb)

VR Systematic-Pillar: Place Code.

Animation of experimental data when a rat was performing the VR systematic-pillar task during a representative session with recorded spikes from a putative pyramidal neuron overlaid over the rat's trajectory. The rat's position is represented by the moving grey shape. The light green trace corresponds to the trajectory of the rat and the dark green dots indicate where the spikes occurred. Note that the activity of the neuron is composed of motifs which occur in a restricted region of space on only on one of the three arms of the triangular path followed by the rat. (MOV 3782 kb)

VR Systematic-Pillar: Disto-Code.

Animation of experimental data when a rat was performing the VR systematic-pillar task during a representative session with spikes from a recorded putative pyramidal neuron overlaid over the rat's trajectory. The rat's position is represented by the moving grey shape. The light green trace corresponds to the trajectory of the rat and the dark green dots indicate where the spikes occurred. Note that the activity of the neuron is composed of motifs which occur in restricted regions of space at the same distance along all the three arms of the triangular path of the rat. (MOV 3738 kb)

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Aghajan, Z., Acharya, L., Moore, J. et al. Impaired spatial selectivity and intact phase precession in two-dimensional virtual reality. Nat Neurosci 18, 121–128 (2015). https://doi.org/10.1038/nn.3884

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