Abstract
Neural activity in the hippocampus is known to reflect how animals move through an environment1,2. Although navigational behaviour may show considerable stability3,4,5,6, the tuning stability of individual hippocampal neurons remains unclear7,8,9,10,11,12. Here we used wireless calcium imaging to longitudinally monitor the activity of dorsal CA1 hippocampal neurons in freely flying bats performing highly reproducible flights in a familiar environment. We find that both the participation and the spatial selectivity of most neurons remain stable over days and weeks. We also find that apparent changes in tuning can be largely attributed to variations in the flight behaviour of the bats. Finally, we show that bats navigating in the same environment under different room lighting conditions (lights on versus lights off) exhibit substantial changes in flight behaviour that can give the illusion of neuronal instability. However, when similar flight paths are compared across conditions, the stability of the hippocampal code persists. Taken together, we show that the underlying hippocampal code is highly stable over days and across contexts if behaviour is taken into account.
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Data availability
The dataset from this study is available from the corresponding author on reasonable request. Source data are provided with this paper.
Code availability
All analyses were conducted using custom code in MATLAB (MathWorks), which can be found at GitHub (https://github.com/WALIII/ImBat; https://github.com/gardner-lab/FinchScope).
Change history
30 May 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41586-022-04885-w
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Acknowledgements
We thank the members of the Yartsev laboratory and D. Foster laboratory for discussion and comments; E. Azim, B. Styr, J. Widloski and K. Qi for reading and comments on the manuscript; P. Golshani; T. Shuman, D. Cai and G. Blair for initial training on calcium imaging surgical techniques; N. Doston and D. Genzel for assistance with the experimental set-up; V. Shvareva and A. Rakuljic for help with histology; H. Aaron, F. Ives and staff at the CRL Molecular Imaging Center for assistance with the use of core imaging facilities; Y. Minton and L. Loomis for experimental room maintenance and animal care; G. Lawson, K. Jensen, J. Frohlich and the staff of the Office of Laboratory Animal Care for support with animal husbandry and care. This research was supported by the New York Stem Cell Foundation (NYSCF-R-NI40), Air Force Office of Scientific Research (FA9550-17-1-0412), the Packard Fellowship (2017–66825), National Institute of Neurological Disorders and Stroke (R01NS118422-01), the Valle Foundation (VS-2020-34), The Office of Naval Research (N00014-21-1-2063), the Searle Scholars Program (SSP-2016-1412) (to M.M.Y.) and the National Science Foundation Graduate Research Fellowship (to T.A.S).
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T.A.S., W.A.L. and M.M.Y. designed the research. W.A.L., T.A.S. and M.M.Y. analysed the data. T.A.S. performed experiments with help from W.A.L, A.F and M.S. Illustrations were made by M.S. The manuscript was written by W.A.L., T.A.S. and M.M.Y. with input from A.F. and M.S.
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Extended data figures and tables
Extended Data Fig. 1 The automated, human-free, flight room.
a, Panoramic perspective of the automated flight room showing the feeders, adjustable ceiling lights, foam walls and motion tracking cameras. b, View towards one of the interior walls of the flight room. Motion tracking camera is indicated. c, View of the feeders. Note that the ceiling lights (white squares) and tracking system are visible on the ceiling for all images. The ceiling lights were set to specific illumination levels during the flight experiments (Methods) and are shown here at maximum brightness level for visualization purposes.
Extended Data Fig. 2 Imaged hippocampal neurons are spatially selective and collectively span the available environment.
a, 14 simultaneously recorded ROIs during a single free-foraging session from one bat. Red dots represent estimated spiking events superimposed on the flight trajectories (grey; Methods). The size of each dot indicates the relative size of the deconvolved spiking event per imaging frame (per 33 ms bin; Methods). b, c, Firing density is not increased around the virtual boundary and the spatial activity recorded across neurons spans the available environment. b, Example spatial firing density for one animal. The virtual boundary that bats had to cross in order to rebait reward feeders is indicted by a dashed line. c, Spatial firing density of deconvolved neural activity (that is, ‘spikes’) for all animals (n = 7). Reward locations are indicated. Images in b, c are displayed from a top-down (XY) perspective. There was no significant difference in firing density within 1m of this boundary versus other locations in the room (P > 0.5 for all bats, two-tailed Wilcoxon Rank Sum).
Extended Data Fig. 3 Spatial behaviour and hippocampal neural tuning along structured flight paths.
a, b, Flights predominantly occur along a few highly repeated paths. a, After an initial period of exploration, a few common flight paths dominate the distribution of trajectories during the free-foraging session (n = 63 sessions from 7 bats). Blue and black lines are the mean proportion of flights over time for the three most common flight paths and the unique flight paths, respectively. Shading is 95% confidence interval around the mean. 73% of all recorded flights (3,472/4,731 flights from 7 bats) occur along repeated flight paths, 54% are flown across the three most common routes (1,877/3,472 flights from 7 bats) and 70% are in the top five most common routes (2,429/3,472 flights from 7 bats). b, Percentage of the top three flight paths and all other flights in each session across all animals, with the mean and SEM indicated. Each point is the prevalence of the top three flight paths on each session in which they occur. Points are horizontally jittered to ease visualization. c, Percentage of significantly tuned ROIs for all bats (n = 7; Methods). Each dot represents a single animal, the red line represents the mean for all seven bats, boxes are first and third quartiles and bars represent maximum and minimum across all bats. d, Percentage of ROIs with one or more fields per flight path (Methods).
Extended Data Fig. 4 Neural tuning is distinct for different trajectories but is highly similar in overlapping portions.
a, 2D projection (top-down view) of the mean flight path for the three most common flight paths for one bat. Note that the green and red trajectories are highly overlapping compared to the blue trajectory. b, Mean velocity profile for the same top three flight paths, with colours corresponding to those in a. c, Distribution of correlation scores (shown here as 1-r for ease of comparison to Fig. 4) for all ROIs, relative to the red flight path. The red distribution is calculated by comparing even/odd flights of the red flight path shown in a. d, Same as c but only for the timeframe in which red and green flights overlap in space. e, Additional examples of overlapping flight paths. Overlapping paths are coloured black and non-overlapping paths are grey. Overlapping regions are coloured red. f, The black distribution is calculated by comparing neural tuning relative to even/odd flights that comprise a flight path; the blue distribution is the comparison of neural tuning across distinct flight paths that partially overlap in some segments. The grey distribution compares neural tuning across flight paths that are similar in duration but do not overlap in space. g, Same as f but only comparing neural activity for the timeframe in which similar flight paths overlap in space. The red distribution is the comparison of neural tuning during the overlapping portions of distinct flight paths and the grey distribution is a time matched control for trajectories that do not overlap in space. For f, g, the black distribution is calculated by comparing neural tuning relative to even/odd flights that comprise a flight path; and red indicates a comparison of overlapping portions of distinct flight paths. h, i, Different flight paths can be accurately decoded using neural activity. h, Prediction accuracy for two bats (left and right confusion matrixes, respectively) where each class is a flight cluster. The percent accuracy and the number of classified flights is listed within each block (Methods). i, Decoder prediction accuracy for all bats and all flight paths with n > 10 flights. Points are horizontally jittered for ease of visualization.
Extended Data Fig. 5 Neural tuning is better explained by spatial rather than goal-vector tuning.
a, Example of the analysis approach. Shown is a 2D projection (top-down view) of the mean flight path for three flights of one bat. In this example, ROI distance and angle tuning for the grey flight path can be directly compared to a dissimilar flight path that terminates at the same goal location (in red, where shared angle/distance tuning is expected in the case of goal-vector tuning28) and to a similar flight path that ends in a different goal location (in blue, where goal-vector tuning is not expected, but shared angle/distance tuning might artefactually result from an overlap in spatial position). b, Distribution of correlation scores (shown here as 1-r for ease of comparison to Fig. 4) for all ROIs binned by distance to goal (Methods) between pairs of similar flights (in blue) or pairs of dissimilar, goal terminating flight paths (in red). c. Same as b but binned by angle to the goal location. For b, c, neural tuning is significantly more similar across pairs of overlapping flight paths (distance: P = 1.4 x 10−5 two-tailed Wilcoxon Rank Sum; angle: P = 1.6 x 10−5 two-tailed Wilcoxon rank sum); for dissimilar paths towards the same goal location, both goal and distance tuning distributions are not significantly different from what would be expected by chance (performing the same comparison after shuffling ROI identity, P > 0.05 two-tailed Wilcoxon rank sum). The percent of ROIs that have the same significant tuning across two non-spatially overlapping flights to the same goal location is 4.1% (14/335 ROIs) for distance and 3.9% (13/335 ROIs) for angle (Methods). Therefore, we did not observe a pronounced goal-vector tuning independent of spatial position. This differs from a previous report in CA1 of flying bats28 and could be due to increased false positive rates related to uncontrolled behavioural covariates, in agreement with a recent report58.
Extended Data Fig. 6 Tuning stability for neurons tracked over days.
a, The fraction of ROIs that can be confidently tracked, under ideal conditions (no experimenter refocusing or noticeably large change in field of view) as a function of days. Each dot represents the fraction of ROIs tracked for all bats. Tracking loss occurred at a rate of about 2% of ROIs per day (linear fit, R2 = 0.67, P = 3 x 10−4). Dotted lines indicate 95% coefficient confidence intervals of the linear regression line in red. b, The cumulative distribution function (CDF) of unique ROIs that were included in the analysis, considering that some bats were tracked over longer intervals than others. c, Tracking contiguity showing the number of session gaps that exist between confidently tracked and aligned unique ROIs across days. d. Replotting the data in main text Fig. 2b, showing all underlying data points as a scatterplot. Error bars are 99% confidence intervals of the mean. Points are horizontally jittered for ease of visualization. Note that the number of ROI/flight-pair comparisons over days is decreasing (n = 914, 635, 490, 367, 208, 167, 117, 92, 45, 11). e, Histogram of correlation values for all points in panel ‘d’ (n = 3,046 possible ROI/flight path pairs). Note the lack of a pronounced second peak where tuning stability equals 0, as would be expected if a prominent subset of neurons were to lose or gain tuning. f, Scatterplot distribution of ROI correlations compared to day ‘1’ for the subset of consecutively tracked, flight path aligned ROIs (n = 360 ROIs). Green box indicates the distribution median. g, Histogram of correlation values for all points in panel f. Note the lack of a pronounced second peak where tuning stability equals 0, as would be expected if a prominent subset of neurons were to lose or gain tuning. Points are horizontally jittered for ease of visualization. h, Distributions of the mean ROI peak times relative to the first day for all points in panel f, separated by day (colours). i, j, CA1 tuning persists despite a multi-day gap in flight path behaviour. i, The dominant three flight paths are coloured in red, green and blue. Note the five-day interval between sessions with green flight paths. j, Six example ROI timeseries aligned to the green flight path on day 1 (dark green) and day 6 (light green). Shading is the standard deviation of the mean fluorescent time series. Note the high similarity in the ROIs’ timeseries that persists after a multi-day gap during which this flight path was not flown.
Extended Data Fig. 7 Examples of abrupt, possibly biologically-driven, changes in neural participation over days.
Maximum intensity projections of adjacent flight sessions that show several compelling examples of sharp in focus ROIs that could be unambiguously identified on one day but are clearly absent in the proceeding or following imaging session. Several of these examples are less likely to be a result of a slow change in imaging plane across days, which explains the majority of our tracking loss (that is, a slow change of ROI sharpness and intensity over consecutive sessions, as may be seen in some panels). Examples of abrupt changes in neural participation are sparse; panels a–d show different examples with panel a being the most obvious example found in our data set. Blue markers identify ROIs that are easily tracked on adjacent sessions. Red circles indicate an ROI that putatively ‘drops-out’ and green circles indicate an ROI that putatively ‘drops-in.’ Note that this phenomenon is uncommon and shown here to demonstrate the conservative approach: only 2.6% of clearly isolated ROIs identified on any given day show this effect (Methods).
Extended Data Fig. 8 ROI tuning stability and flight consistency.
a, Flight-to-flight consistency varies along the phase of flight. Displayed are all ROIs tuned to the flight path shown in Fig. 3a, b. Each point represents a significantly tuned ROI’s stability (that is, one minus the normalized coefficient of variation (CV)) at the flight phase it is tuned to (determined by the peak of the mean flight-aligned deconvolved activity trace). Red dotted lines indicate takeoff and landing times. The solid blue line is the running average of all points. b, Flight variability as a function of flight phase, sampled on the same interval as in a. Flight variability was highest in the middle of the flight and lowest during the takeoff and landing. The left y-axis units are the mean Euclidean distance of all individual flights from the centre of the mean flight path. The blue dotted line is the same as the blue solid line from a, normalized from 0-1 (right y-axis). c, More structured behaviour in freely flying bats results in the estimation of a more stable CA1 hippocampal responses over days. Longitudinal stability of hippocampal neural responses when considering either the most common structured flight path (that is, the flight path comprised of the largest number of flights, in red) and all non-repeated (that is, unstructured) flights in the same session (in blue) for n = 7 bats. Correlation is computed for 2D rate-maps relative to the first day of the experiment. Thin lines are different bats/flight paths and thick lines are the average across animals. Error bars represent 95% confidence intervals of the mean.
Extended Data Fig. 9 Perceived sensory-based remapping when considering all flight trajectories before controlling for flight behaviour variability across lights-on and lights-off conditions.
Shown is the distribution of 2D rate map correlation scores for all ROIs when considering all flight paths between either the two lights-on sessions (teal) or lights-on versus lights-off conditions (grey). These distributions are significantly different (n = 178 ROIs, P = 7.04 x 10−55, two-tailed Wilcoxon rank sum test). *** P< 0.0001.
Extended Data Fig. 10 Similarities and differences in flight behaviour between the lights-on vs. lights-off cue conditions.
a, Aggregated flight paths that occurred across 10 consecutive days of experiments for one example bat, divided into successive lights-on, lights-off and lights-on′ periods (ordered from left to right). Coloured are the four most common flight paths. The red and blue flight paths occurred predominantly in lights-on and lights-on′ sessions. The green and magenta paths occurred predominantly in the lights-off condition. b, Distribution of the occurrence of the four most prevalent flight paths, coloured according to a, during different phases of the experiment across lights-on and lights-off sessions. Flight preference changed dramatically during each period. c, Aggregated flight paths across ten consecutive days for one representative bat divided into chronological thirds of each session where light levels were held constant. Shown are the three most common flight paths in red, blue and green. d, Distribution of the occurrence of the four most prevalent flight paths (colours) during different phases of the experiment across each third of ten sessions. e, Proportion of unique (that is, unstructured) flight paths increase on average in the lights-off cue condition (dark) compared to when the lights are kept on for the duration of the session. The blue line indicates the mean relative proportion of unique flights flown in the lights-on/off experiment. The dark grey line is the mean proportion of unstructured flights for bats where the light is kept on for the duration of the experiment. Shading for both plots represent 95% confidence intervals of the mean. f, The mean number of flights per minute is not significantly different in the light or dark conditions (P > 0.05 two-tailed Wilcoxon rank sum test, n = 80 and 40 binned minutes in the lights-on and lights-off periods respectively). Points are horizontally jittered for ease of visualization. g, Flight duration as function of cue condition. Flight duration is significantly longer in the lights-off condition, (P = 0.0038, two-tailed Wilcoxon rank sum test) although the effect size is small (that is, the distributions are highly overlapping; n = 1,567 and 746 flights in the lights-on and lights-off periods respectively). Points are horizontally jittered for ease of visualization. h, i, Across-day and across-condition behavioural stability for the lights-on versus lights-off experiments. h, Average correlation of repeated paths relative to day one for each bat (n = 5 flight paths). Each colour indicates a different flight path. Bars indicate 95% confidence intervals of the mean correlation on each day. i, Correlation values of shared flights to the mean of their flight path in the first lights-on period. Flights along the same path were not significantly different between the light and dark cue condition (P = 0.21 two-tailed Wilcoxon rank sum test, n = 268, 150, 267 flights for lights-on, lights-off and lights-on conditions, respectively). Points are horizontally jittered for ease of visualization. * P< 0.01.
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Liberti, W.A., Schmid, T.A., Forli, A. et al. A stable hippocampal code in freely flying bats. Nature 604, 98–103 (2022). https://doi.org/10.1038/s41586-022-04560-0
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DOI: https://doi.org/10.1038/s41586-022-04560-0
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