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.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
O'Keefe, J. & Dostrovsky, J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175 (1971).
O'Keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Clarendon Press, 1978).
Muller, R.U. & Kubie, J.L. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. J. Neurosci. 7, 1951–1968 (1987).
Shapiro, M.L., Tanila, H. & Eichenbaum, H. Cues that hippocampal place cells encode: dynamic and hierarchical representation of local and distal stimuli. Hippocampus 7, 624–642 (1997).
Knierim, J.J. Dynamic interactions between local surface cues, distal landmarks, and intrinsic circuitry in hippocampal place cells. J. Neurosci. 22, 6254–6264 (2002).
O'Keefe, J. & Conway, D.H. Hippocampal place units in the freely moving rat: why they fire where they fire. Exp. Brain Res. 31, 573–590 (1978).
Save, E., Nerad, L. & Poucet, B. Contribution of multiple sensory information to place field stability in hippocampal place cells. Hippocampus 10, 64–76 (2000).
Battaglia, F.P., Sutherland, G.R. & McNaughton, B.L. Local sensory cues and place cell directionality: additional evidence of prospective coding in the hippocampus. J. Neurosci. 24, 4541–4550 (2004).
Anderson, M.I. & Jeffery, K.J. Heterogeneous modulation of place cell firing by changes in context. J. Neurosci. 23, 8827–8835 (2003).
Aikath, D., Weible, A.P., Rowland, D.C. & Kentros, C.G. Role of self-generated odor cues in contextual representation. Hippocampus 24, 1039–1051 (2014).
Gothard, K.M., Skaggs, W.E. & McNaughton, B.L. Dynamics of mismatch correction in the hippocampal ensemble code for space: interaction between path integration and environmental cues. J. Neurosci. 16, 8027–8040 (1996).
Markus, E.J. et al. Interactions between location and task affect the spatial and directional firing of hippocampal neurons. J. Neurosci. 15, 7079–7094 (1995).
Terrazas, A. et al. Self-motion and the hippocampal spatial metric. J. Neurosci. 25, 8085–8096 (2005).
Stackman, R.W. & Taube, J.S. Firing properties of head direction cells in the rat anterior thalamic nucleus: dependence on vestibular input. J. Neurosci. 17, 4349–4358 (1997).
Stackman, R.W., Clark, A.S. & Taube, J.S. Hippocampal spatial representations require vestibular input. Hippocampus 12, 291–303 (2002).
Calton, J.L. et al. Hippocampal place cell instability after lesions of the head direction cell network. J. Neurosci. 23, 9719–9731 (2003).
Angelaki, D.E. & Cullen, K.E. Vestibular system: the many facets of a multimodal sense. Annu. Rev. Neurosci. 31, 125–150 (2008).
O'Keefe, J. & Recce, M.L. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317–330 (1993).
Mehta, M.R., Lee, A.K. & Wilson, M.A. Role of experience and oscillations in transforming a rate code into a temporal code. Nature 417, 741–746 (2002).
Huxter, J.R., Senior, T.J., Allen, K. & Csicsvari, J. Theta phase–specific codes for two-dimensional position, trajectory and heading in the hippocampus. Nat. Neurosci. 11, 587–594 (2008).
Ravassard, P. et al. Multisensory control of hippocampal spatiotemporal selectivity. Science 340, 1342–1346 (2013).
Pastalkova, E., Itskov, V., Amarasingham, A., Buzsaki, G. & Buzsáki, G. Internally generated cell assembly sequences in the rat hippocampus. Science 321, 1322–1327 (2008).
MacDonald, C.J., Lepage, K.Q., Eden, U.T. & Eichenbaum, H. Hippocampal “time cells” bridge the gap in memory for discontiguous events. Neuron 71, 737–749 (2011).
Jezek, K., Henriksen, E.J., Treves, A., Moser, E.I. & Moser, M.-B. Theta-paced flickering between place-cell maps in the hippocampus. Nature 478, 246–249 (2011).
Hahn, T.T.G., McFarland, J.M., Berberich, S., Sakmann, B. & Mehta, M.R. Spontaneous persistent activity in entorhinal cortex modulates cortico-hippocampal interaction in vivo. Nat. Neurosci. 15, 1531–1538 (2012).
Miller, J.F. et al. Neural activity in human hippocampal formation reveals the spatial context of retrieved memories. Science 342, 1111–1114 (2013).
Ekstrom, A.D. et al. Cellular networks underlying human spatial navigation. Nature 425, 184–188 (2003).
Jacobs, J., Kahana, M.J., Ekstrom, A.D., Mollison, M.V. & Fried, I. A sense of direction in human entorhinal cortex. Proc. Natl. Acad. Sci. USA 107, 6487–6492 (2010).
Rolls, E.T. Spatial view cells and the representation of place in the primate hippocampus. Hippocampus 9, 467–480 (1999).
Dombeck, D.A., Harvey, C.D., Tian, L., Looger, L.L. & Tank, D.W. Functional imaging of hippocampal place cells at cellular resolution during virtual navigation. Nat. Neurosci. 13, 1433–1440 (2010).
Harvey, C.D., Collman, F., Dombeck, D.A. & Tank, D.W. Intracellular dynamics of hippocampal place cells during virtual navigation. Nature 461, 941–946 (2009).
Schmidt-Hieber, C. & Häusser, M. Cellular mechanisms of spatial navigation in the medial entorhinal cortex. Nat. Neurosci. 16, 325–331 (2013).
Chen, G., King, J.A., Burgess, N. & O'Keefe, J. How vision and movement combine in the hippocampal place code. Proc. Natl. Acad. Sci. USA 110, 378–383 (2013).
Cei, A., Girardeau, G., Drieu, C., El Kanbi, K. & Zugaro, M. Reversed theta sequences of hippocampal cell assemblies during backward travel. Nat. Neurosci. 17, 719–724 (2014).
Hölscher, C., Schnee, A., Dahmen, H., Setia, L. & Mallot, H.A. Rats are able to navigate in virtual environments. J. Exp. Biol. 208, 561–569 (2005).
Cushman, J.D. et al. Multisensory control of multimodal behavior: do the legs know what the tongue is doing? PLoS ONE 8, e80465 (2013).
Aronov, D. & Tank, D.W. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system. Neuron 84, 442–456 (2014).
Hafting, T., Fyhn, M., Bonnevie, T., Moser, M.B. & Moser, E.I. Hippocampus-independent phase precession in entorhinal grid cells. Nature 453, 1248–1252 (2008).
Mehta, M.R. Neuronal dynamics of predictive coding. Neuroscientist 7, 490–495 (2001).
Cheng, J. & Ji, D. Rigid firing sequences undermine spatial memory codes in a neurodegenerative mouse model. Elife 2, e00647 (2013).
Louie, K. & Wilson, M.A. Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep. Neuron 29, 145–156 (2001).
Gelbard-Sagiv, H., Mukamel, R., Harel, M., Malach, R. & Fried, I. Internally generated reactivation of single neurons in human hippocampus during free recall. Science 322, 96–101 (2008).
Hahn, T.T.G., McFarland, J.M., Berberich, S., Sakmann, B. & Mehta, M.R. Spontaneous persistent activity in entorhinal cortex modulates cortico-hippocampal interaction in vivo. Nat. Neurosci. 15, 1531–1538 (2012).
Resnik, E., McFarland, J.M., Sprengel, R., Sakmann, B. & Mehta, M.R. The effects of GluA1 deletion on the hippocampal population code for position. J. Neurosci. 32, 8952–8968 (2012).
Mehta, M.R. Contribution of Ih to LTP, place cells, and grid cells. Cell 147, 968–970 (2011).
Russell, N.A., Horii, A., Smith, P.F., Darlington, C.L. & Bilkey, D.K. Lesions of the vestibular system disrupt hippocampal theta rhythm in the rat. J. Neurophysiol. 96, 4–14 (2006).
Nitz, D.A. Path shape impacts the extent of CA1 pattern recurrence both within and across environments. J. Neurophysiol. 105, 1815–1824 (2011).
Mehta, M.R., Quirk, M.C. & Wilson, M.A. Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron 25, 707–715 (2000).
Mehta, M.R., Barnes, C.A. & McNaughton, B.L. Experience-dependent, asymmetric expansion of hippocampal place fields. Proc. Natl. Acad. Sci. USA 94, 8918–8921 (1997).
Mehta, M.R. & McNaughton, B.L. Expansion and shift of hippocampal place fields: evidence for synaptic potentiation during behavior. in Computational Neuroscience Trends (ed. Bower, J.) 741–745 (Plenum Press, New York, 1996).
Vanderwolf, C.H. Hippocampal electrical activity and voluntary movement in the rat. Electroencephalogr. Clin. Neurophysiol. 26, 407–418 (1969).
Berens, P. CircStat: a MATLAB toolbox for circular statistics. J. Stat. Softw. 31, 1–21 (2009).
Bonnevie, T. et al. Grid cells require excitatory drive from the hippocampus. Nat. Neurosci. 16, 309–317 (2013).
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.
Author information
Authors and Affiliations
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
Ethics declarations
Competing interests
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)
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)
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nn.3884
This article is cited by
-
An automated, low-latency environment for studying the neural basis of behavior in freely moving rats
BMC Biology (2023)
-
An optical design enabling lightweight and large field-of-view head-mounted microscopes
Nature Methods (2023)
-
Mobile cognition: imaging the human brain in the ‘real world’
Nature Reviews Neuroscience (2023)
-
Optogenetic frequency scrambling of hippocampal theta oscillations dissociates working memory retrieval from hippocampal spatiotemporal codes
Nature Communications (2023)
-
Interactions between rodent visual and spatial systems during navigation
Nature Reviews Neuroscience (2023)
