Abstract
We present a mouse virtual reality (VR) system which restrains head-movements to horizontal rotations, potentially compatible with multi-photon imaging. We show that this system allows expression of the spatial navigational behaviour and neuronal firing patterns characteristic of real open arenas (R). Place and grid, but not head-direction, cell firing had broader spatial tuning in VR than R. Theta frequency increased less with running speed in VR than in R, while firing rates increased similarly in both. Place, but not grid, cell firing was more directional in VR than R. These results suggest that the scale of grid and place cell firing patterns, and the frequency of theta, reflect translational motion inferred from both virtual (visual and proprioceptive) cues and uncontrolled static (vestibular translation and extra-maze) cues, while firing rates predominantly reflect visual and proprioceptive motion. They also suggest that omni-directional place cell firing in R reflects local-cues unavailable in VR.
Introduction
Virtual reality (VR) offers a powerful tool for investigating spatial cognition, allowing experimental control and environmental manipulations that are impossible in the real world. For example, uncontrolled real-world cues cannot contribute to determining location within the virtual environment, while the relative influences of motoric movement signals and visual environmental signals can be assessed by decoupling one from the other1,2. In addition, the ability to study (virtual) spatial navigation in head-fixed mice allows the use of intracellular recording and two photon microscopy3-12. However, the utility of these approaches depends on the extent to which the neural processes in question can be instantiated within the virtual reality (for a recent example of this debate see13).
The modulation of firing of place cells or grid cells along a single dimension, such as distance travelled along a specific trajectory or path, can be observed as virtual environments are explored by head-fixed mice2-4,6-9,12 or body-fixed rats14-16. However, the two-dimensional firing patterns of place, grid and head-direction cells in real world open arenas are not replicated in these systems, in which the animal cannot physically rotate through 360°.
By contrast, the two-dimensional spatial firing patterns of place, head direction, grid and border cells have been observed in VR systems in which rats can physically rotate through 360° 17,18. Minor differences with free exploration remain, e.g. the frequency of the movement-related theta rhythm is reduced17, perhaps due to the absence of translational vestibular acceleration signals14,30. However, the coding of 2-d space by neuronal firing can clearly be studied. These VR systems constrain a rat to run on top of an air-suspended Styrofoam ball, wearing a “jacket” attached to a jointed arm on a pivot. This allows the rat to run in any direction, its head is free to look around while its body is maintained over the centre of the ball.
However, these 2-d VR systems retain a disadvantage of the real-world freely moving paradigm in that the head movement precludes use with multi-photon microscopy or intracellular recording. In addition, some training is required for rodents to tolerate wearing a jacket. Here we present a VR system for mice in which a chronically implanted head-plate enables use of a holder that constrains head movements to rotations in the horizontal plane while the animal runs on a Styrofoam ball. Screens and projectors project a virtual environment in all horizontal directions around the mouse, and onto the floor below it, from a viewpoint that moves with the rotation of the ball, following17,18. See Figure 1 and Materials and Methods.
We demonstrate that this system allows navigation to an unmarked location within an open arena, showing that mice can perceive and remember locations defined by the virtual space. We also show that the system allows expression of the characteristic 2-dimensional firing patterns of place cells, head-direction cells and grid cells in electrophysiological recordings, making their underlying mechanisms accessible to investigation by manipulations of the VR.
Results
Navigation in VR
Eleven mice were trained in the virtual reality system (see Figure 1 and Materials and Methods). All training trials in the VR and the real square environments from the 11 mice were included in the behavioural analyses below. The mice displayed an initially lower running speed when first experiencing the real-world recording environment (a 60x60cm square), but reached a higher average speed after 20 or so training trials. The increase in running speed with experience was similar in the virtual environments (Figure 1F-H). Running speeds did not differ between the 60cm and 90cm virtual environments used for recording in 7 and 4 of the mice respectively (12.01±2.77 in 60cm VR, 14.33±4.19cm/s in 90cm VR, p = 0.29). Running directions in the VR environment showed a marginally greater unimodal bias compared to the real environment (R; Figure 1K). Mice displayed a greater tendency to run parallel to the four walls in VR, a tendency which reduced with experience (Figure S2). They also took straighter, less tortuous, paths in VR than in R, as would be expected from their head-fixation (Figure 1L-N).
In the fading beacon task, performance steadily improved across 2-3 weeks of training (Figure 2D, one trial per day). They learned to approach the fixed reward location and could do so even after it became completely unmarked (fully faded, see Figure 2 and the Supplementary video of a mouse performing the task, and Materials and Methods for details of the training regime).
Electrophysiology
We recorded a total of 231 CA1 cells from 7 mice: 175 cells were classified as place cells in the real environment, 186 cells in the virtual environment, and 154 cells were classified as place cells in both real and virtual environments (see Materials and Methods).
We recorded 141 cells in dorsomedial Entorhinal Cortex (dmEC) from 8 mice, 82 of them were classified as grid cells in the real environment, 65 of them were grid cells in the virtual environment, and 61 were classified as grid cells in both real and virtual environments. Among these 141 recorded cells, 16 cells were quantified as head-direction cells (HDCs) in R, 20 cells were HDCs in VR, with 12 cells classified as HDCs in both R and VR environments.
Place cells recorded from CA1 showed spatially localised firing in the virtual environment, with similar firing rates in the virtual and real square environments. Place cells had larger firing fields in VR than in R, by a factor 1.44 (field size in VR/ field size in R), which did not differ between those recorded in a 60×60cm versus a 90×90cm VR environment (1.44 in 90cm, 1.43 in 60cm, p=0.66). The spatial information content of firing fields in VR was lower than in R. In addition, the firing of place cells was more strongly directionally modulated in VR than in R. See Figure 3.
One possible contribution to apparent directionality in firing could be inhomogeneous sampling of direction within the (locational) firing field. This can be controlled for by explicitly estimating the parameters of a joint place and direction (‘pxd’) model from the firing rate distribution24. However, using this procedure did not ameliorate the directionality in firing (see Figure 3). Further analyses showed that firing directionality increased near to the boundaries in both virtual and real environments (where sampling of direction is particularly inhomogeneous), but that the additional directionality in VR compared to R was apparent also away from the boundaries. See Figure S3.
Grid cells recorded in dmEC, showed similar grid-like firing patterns in VR as in R, with similar firing rates and ‘gridness’ scores. The spatial scale of the grids was larger in VR than in R, with an average increase of 1.42 (grid scale in VR/ grid scale in R, n=6 mice), which did not differ between those recorded in a 60x60cm versus a 90x90cm VR environment (1.43 in 60cm VR, 1.36 in 90cm VR, p=0.78). The spatial information content of grid cell firing was lower in VR than R, as with the place cells. Unlike the place cells, the grid cells showed only a slight increase in directionality from R to VR, which, unlike for place cells, appears to reflect inhomogeneous sampling of directions within firing fields, as the effect was not seen when controlling for this in a joint ‘pxd’ model. See Figure 4. It is possible that low directional modulation of the firing of a grid cell could reflect directionally modulated firing fields with different directional tuning. Accordingly we checked the directional information in the firing of each field, without finding any difference between R and VR (Figure 4H).
We also recorded head-direction cells in the dmEC, as previously reported in rats21 and mice25. These cells showed similar firing rates in VR and R, with similar tuning widths. See Figure 5. The relative differences in the tuning directions of simultaneously recorded head-direction cells was maintained between R and VR, even though the absolute tuning direction was not (see Figure S4).
The translational movement defining location within the virtual environment purely reflects feedback (visual, motoric and proprioceptive) from the virtual reality system, as location within the real world does not change. However, the animal’s sense of orientation might reflect both virtual and real-world inputs, as the animal rotates in both the real and virtual world. To check for the primacy of the controlled virtual inputs versus potentially uncontrolled real-world inputs (e.g. auditory or olfactory), we performed a 180° rotation of the virtual environment between trials. Note that the geometry of the apparatus itself (square configuration of screens, overhead projectors on either side) would conflict with rotations other than 180°. In separate trials, we observed a corresponding rotation of the virtual firing patterns of place, grid and head-direction cells, indicating the primacy of the virtual environment over non-controlled real world cues. See Figure 6.
The animal’s running speed is known to correlate with the firing rates of cells, including place cells, grid cells and (by definition) speed cells21,22,26, and with the frequency of the local field potential theta rhythm27-29. So these experimental measures can give us an independent insight into perceived running speed. We found that the slope of the relationship between theta frequency and running speed was reduced within the VR compared to R, while this was not the case for the firing rates of place, grid and speed cells. See Figure 7. However, the changes in grid scale and the changes theta frequency in virtual versus real environments did not correlate with each other significantly across animals.
Discussion
We have demonstrated the ability of a novel mouse virtual reality (VR) system to allow expression of spatial learning and of the characteristic spatially modulated firing patterns of place, grid and head-direction cells in open arenas. Thus it passes the first pre-requisite as a tool for studying the mechanisms behind the two dimensional firing patterns of these spatial cells, following previous systems for rats that also allow physical rotation of the animal17,18. Head-fixed or body-fixed VR systems have been very successful for investigating the one-dimensional spatial firing patterns of place cells2-4,12,14-16 or grid cells6-9, e.g. modulation of firing rate by the distance along a linear trajectory. But the two-dimensional firing patterns of place, grid or head direction cells are not seen in these systems.
Although the characteristic firing patterns of spatial cells were expressed within our VR system, there were also some potentially instructive differences in their more detailed properties between VR and a similar real environment (R), which we discuss below.
The spatial scale of the firing patterns of both place cells and grid cells was approximately 1.4 times larger in VR compared to R (Figures 3 and 4; see also17). Along with the increased scale of place and grid cell responses in VR, there was a reduction in the dependence of theta frequency on running speed. The LFP theta frequency reflects a contribution from vestibular translational acceleration signals14,30 which will be absent in our VR system. However, there was no change in the increasing firing rates of place, grid and speed cells with running speed in VR (Figure 7), indicating an independence from vestibular translational acceleration cues. Thus it is possible that the absence of linear acceleration signals affects both LFP theta rhythmicity and the spatial scale of firing patterns, but there was no evidence that the two were directly related.
Finally, uncontrolled distal cues, such as sounds and smells, and the visual appearance of the apparatus aside from the screens (the edge of the ball, the edges of the screens) will conflict with virtual cues indicating self-motion. Thus increased firing field size could also reflect broader tuning or reduced precision due to absent or conflicting inputs, consistent with the reduced spatial information seen in place and grid cell firing patterns (Figures 3 and 4), and potentially a response to spatial uncertainty31.
The head direction cells do not show broader tuning in the VR (Figure 5), probably because there is no absence of vestibular rotation cues and no conflict with distal real-world cues, as the mice rotate similarly in the virtual and real world. We note however, that spatial firing patterns follow the virtual cues when the virtual cues and entry point are put into conflict with uncontrolled real-world cues (Figure 6).
Place cell firing in VR showed an increased directionality compared to the real environment. One possible explanation, that the apparent directionality reflected inhomogeneous sampling of directions in the firing field, was not supported by further analyses (Figure 3E). A potential benefit of VR is an absence of local sensory cues to location, as experimenters typically work hard to remove consistent uncontrolled cues from real-world experiments (e.g., cleaning and rotating the walls and floor between trials). However, but reliable within-trial local cues may contribute to localisation of firing nonetheless14. Thus it maybe that uncontrolled local cues in real experiments (even if unreliable from trial to trial) are useful for supporting a locational response that can be bound to the distinct visual scenes observed in different directions, see also15. In this case, by removing these local cues, the use of VR leaves the locational responses of place cells more prone to modulation by the remaining (directionally specific) visual cues. We note that grid cells did not show such an increase in directional modulation as the place cells. This may indicate that place cell firing is more influenced by environmental sensory inputs - and thus directional visual inputs given the absence of local cues, while grid cell firing might be more influenced by self-motion cues, and thus less dependent on local cues for orientation independence. However, this would need to be verified in future work.
In conclusion, by using VR, the system presented here offers advantages over traditional paradigms by enabling manipulations that are impossible in the real-world, allowing visual projection of an environment that need not directly reflect a physical reality or the animals’ movements. Differences between the firing patterns in VR and R suggest broader spatial tuning as a possible response to under-estimated translation or spatial uncertainty caused by missing or conflicting inputs, a role for local cues in supporting directionally independent place cell firing and potentially for self-motion cues in supporting directionally independent grid cell firing. Finally, the differential effects of moving from R to VR on the dependence on running speed of the LFP theta frequency compared to neuronal firing rates suggests distinct mechanisms for speed coding, potentially reflecting a differential dependence on vestibular translational acceleration cues.
Previous body-rotation VR systems for rats17,18 also allow expression of the two dimensional firing patterns of place, grid and head-direction cells. However, by working for mice and by constraining the head to rotation in the horizontal plane, our system has the potential for future use with multiphoton imaging using genetically encoded calcium indicators. The use of multiple screens and floor projectors is not as elegant as the single projector systems17,18 but allows the possible future inclusion of a two photon microscope above the head without interrupting the visual projection, while the effects of in-plane rotation on acquired images should in principle be correctable in software.
Materials and Methods
Virtual Reality
A circular head-plate made of plastic (Stratasys Endur photopolymer) is chronically attached to the skull, with a central opening allowing the implant of tetrodes for electrophysiological recording (see Surgery). The head-plate makes a self-centring joint with a holder mounted in a bearing (Kaydon reali-slim bearing KA020XP0) and is clipped into place by a slider. The bearing is held over the centre of an air-supported Styrofoam ball. Four LCD screens placed vertically around the ball and two projectors onto a horizontal floor provide the projection of a virtual environment. The ball is prevented from yaw rotation to give the mouse traction to turn and to prevent any rotation of the ball about its vertical axis, following17. See Figure 1A-E.
The virtual environment runs on a Dell Precision T7500 workstation PC running Windows 7 64-bit on a Xeon X5647 2.93GHz CPU, displayed using a combination of four Acer B236HL LCD monitors mounted vertically in a square array plus two LCD projectors (native resolution 480×320, 150 lumens) mounted above to project floor texture. The head-holder is at the centre of the square and 60mm from the bottom edge of the screens, and 9500mm below the projectors. The LCD panels are 514mm × 293mm, plus bezels of 15mm all around. These six video feeds are fed by an Asus AMD Radeon 6900 graphics card and combined into a single virtual display of size 5760×2160px using AMD Radeon Eyefinity software. The VR is programmed using Unity3d v5.0.2f1 which allows virtual cameras to draw on specific regions of the virtual display, with projection matrices adjusted (see Kooima, 2008 http://csc.lsu.edu/~kooima/articles/genperspective/index.html) to the physical dimensions and distances of the screens and to offset the vanishing point from the centre. For example, a virtual camera facing the X-positive direction renders its output to a portion of the virtual display which is known to correspond to the screen area of the physical monitor facing the X-negative direction.
Translation in the virtual space is controlled by two optical mice (Logitech G700s gaming mouse) mounted with orthogonal orientations at the front and side of a 200mm diameter hollow polystyrene sphere, which floats under positive air pressure in a hemispherical well. The optical mice drive X and Y inputs respectively by dint of their offset orientations, and gain can be controlled within the Unity software. Gain is adjusted such that real-world rotations of the sphere are calibrated so that a desired environmental size (e.g. 600mm across) corresponds to the appropriate movement of the surface of the sphere under the mouse (i.e. moving 600mm, or just under one rotation, on the sphere takes the mouse across the environment). Mouse pointer acceleration is disabled at operating system level to ensure movement of the sphere is detected in a linear fashion independent of running speed.
The mouse is able to freely rotate in the horizontal plane, which has no effect on the VR display (but brings different screens into view). Rotation is detected and recorded for later analysis using an Axona dacqUSB tracker which records the position of two LEDs mounted at ~25mm offset to left and right of the head stage amplifier (see Surgery). Rotation is sampled at 50Hz by detection of the LED locations using an overhead video camera, while virtual location is sampled and logged at 50Hz.
Behaviour is motivated by the delivery of milk rewards (SMA, Wysoy) controlled by a Labjack U3HD USB Data Acquisition device. A digital-to-analogue channel applies 5V DC to a control circuit driving a 12V Cole-Parmer 1/16” solenoid pinch valve, which is opened for 100ms for each reward, allowing for the formation of a single drop of milk (5uL) under gravity feed at the end of a 1/32” bore tube held within licking distance of the animal’s mouth.
Control of the Labjack and of reward locations in the VR is via UDP network packets between the VR PC and a second experimenter PC, to which the Labjack is connected by USB. Software written in Python 2.7 using the Labjack, tk (graphics) and twistd (networking) libraries provide a plan-view graphical interface in which the location of the animal and reward cues in the VE can be easily monitored and reward locations manipulated with mouse clicks. See Figure 1.
Animals
Subjects (11 C57Bl/6 mice) were aged 11-14 weeks and weighed 25-30 grams at the time of surgery. Mice were housed under 12:12 inverted light-dark cycle, with lights on at 10am. All work was carried out under the Animals (Scientific Procedures) Act 1986 and according to Home Office and institutional guidelines.
Surgery
Throughout surgery, mice were anesthetized with 2-3% isoflurane in O2. Analgesia was provided preoperatively with 0.1mg/20g Carprofen, and post-operatively with 0.1mg/20g Metacam. Custommade head plates were affixed to the skulls using dental cement (Kemdent Simplex Rapid). Mice were implanted with custom-made microdrives (Axona, UK), loaded with 17 μm platinum-iridium tetrodes, and providing buffer amplification. Two mice were implanted with 8 tetrodes in CA1 (ML:1.8mm, AP: 2.1mm posterior to bregma), three mice with 8 tetrodes in the dorsomedial entorhinal cortex (dmEC, ML = 3.1mm. AP = 0.2 mm anterior to the transverse sinus, angled 4° posteriorly), and six mice received a dual implant with one microdrive in right CA1 and one in left dmEC (each mircrodrive carried 4 tetrodes). After surgery, mice were placed in a heated chamber until fully recovered from the anaesthetic (normally about 1 h), and then returned to their home cages. Mice were given at least 1 week of post-operative recovery before cell screening and behavioural training started.
Behavioural Training
Behavioural training in the virtual reality setup started while tetrodes were approaching target brain areas (see Screening for spatial cells). Behavioural training involved four phases. Firstly, mice experienced an infinitely long 10cm-wide virtual linear track, with 5 uL milk drops delivered as rewards. Reward locations were indicated by virtual beacons (high striped cylinders with a black circular base, see Figure S1A), which were evenly placed along the track (see Figure S1C). When the mouse contacted the area of the base, milk was released and the beacon disappeared (reappearing in another location). The lateral movement of the mice was not registered in this phase. The aim of this training phase was to habituate the mice to being head restrained and train them to run smoothly on the air-cushioned ball. It took three days, on average, for mice to achieve this criterion and move to the next training phase.
During the second training phase mice experienced a similar virtual linear track (see Figure S1B), which was wider than the first one (30cm wide). During this phase, reward beacons were evenly spaced along the long axis of the track, as before, but placed pseudo-randomly in one of three predefined positions on the lateral axis (middle, left or right). The aim of this training phase was to strengthen the association between rewards and virtual beacons, and to train animals to navigate towards rewarded locations via appropriate rotations on top of the ball. This training phase also took three days, on average. During the third training phase mice were introduced into a virtual square arena placed in the middle of a larger virtual room (see Figures 1E and S1C). The virtual arena had size 60×60cm or 90cm×90cm for different mice. Reward beacons had a base of diameter that equalled to 10% of the arena width. Mice were trained on a ‘random foraging’ task, during which visible beacons were placed in the square box at random locations (at any given time only one beacon was visible).
The last training phase was the ‘fading beacon’ task. During this task, every fourth beacon occurred in a fixed location (the three intervening beacons being randomly placed within the square enclosure; see Figure S1D). At the beginning of this training phase the ‘fixed location beacon’ slowly faded from view over 10 contacts with decreasing opacity. The beacon would remain invisible as long as mice could find it, but would became visible again if mice could not locate it after 2 min of active searching. Once mice showed consistent navigation towards the fading fixed beacon, they were moved to the ‘faded beacon’ phase of the task where the ‘fixed location beacon’ was invisible from the start of the trial and remained invisible throughout the trial, with two drops of milk given as reward for contact. This trial phase therefore requires mice to navigate to an unmarked virtual location starting from different starting points (random locations where the 3rd visible beacon was placed). As such, the ‘fading beacon’ task serves like a continuous version of a Morris Water Maze task19, combining reference memory for an unmarked location with a foraging task designed to optimise environmental coverage for the assessment of spatial firing patterns. Mice typically experienced one trial per day.
Behavioural analyses
All training trials in the VR square and the real (R, see Screening for spatial cells) square environments from the 11 mice were included in the behavioural analyses. During electrophysiological recording in R, the mouse’s position and head orientation were tracked by an overhead camera (50Hz sampling rate) using two infra-red LEDs attached to the micro-drive at a fixed angle and spacing (5 cm apart). Brief losses of LED data due to cable obstruction were corrected with linear interpolation between known position values. Interpolation was carried out for each LED separately. The position values for each LED were then smoothed, separately, using a 400ms boxcar filter. During electrophysiological recording in VR, head orientation was tracked as in R, the path, running speed and running direction was inferred from the VR log at 50Hz (movements of VR location being driven by the computer mice tracking the rotation of the ball, see above).
Path excess ratio was defined as the ratio between the length of the actual path that an animal takes to run from one reward location to another, and the distance between the two reward locations.
Screening for spatial cells
Following recovery, mice were food restricted to 85% of their free-feeding body weight. They were then exposed to a recording arena every day (20 mins per day) and screening for neural activity took place. The recording arena was a 60×60cm square box placed on a black Trespa ‘Toplab’ surface (Trespa International B.V., Weert, Netherlands), and surrounded by a circular set of black curtains. A white cue-card (A0, 84 × 119 cm), illuminated by a 40 W lamp, was the only directionally polarising cue within the black curtains. Milk (SMA Wysoy) was delivered as drops on the floor from a syringe as rewards to encourage foraging behaviour. Tetrodes were lowered by 62.5 um each day, until grid or place cell activity was identified, in dmEC or CA1 respectively. Neural activity was recorded using DACQ (Axona Ltd., UK) while animals were foraging in the square environment. For further details see2.
Recording spatial cell activity
Each recording session consisted of at least one 40-min random-foraging trial in a virtual square environment (see above for behavioural training). For 7 mice the virtual environment had size 60×60cm and for 4 mice 90×90cm when recording took place. After one (or more) 40-min random foraging trials in the virtual square, mice were placed in a real-world square (60×60cm square, similar to the screening environment, see above) for a 20-min random-foraging trial in real world.
Additionally, 4 mice also underwent a virtual cue rotation experiment, which consisted of two 40-min random-foraging VR trial (one baseline VR trial and one rotated VR trial) and one 20 min R trial. Two mice navigating 60×60 cm VR squares and two 90×90cm squares participated in this experiment. In the rotated VR trials, all cues in the virtual reality environment rotated 180 degrees compared to the baseline trial, as was the entry point mice were carried into the VR rig from.
Firing rate map construction and spatial cell classification
Spike sorting was performed offline using an automated clustering algorithm (KlustaKwik20) followed by a manual review and editing step using an interactive graphical tool (waveform, Daniel Manson, http://d1manson.github.io/waveform/). After spike sorting, firing rate maps were constructed by binning animals’ positions into 1.5 × 1.5cm bins, assigning spikes to each bin, smoothing both position maps and spike maps separately using a 5×5 boxcar filter, and finally dividing the smoothed spike maps by the smoothed position maps.
Cells were classified as place cells if their spatial information in baseline trials exceeded the 99th percentile of a 1000 shuffled distribution of spatial information scores calculated from rate maps where spike times were randomly offset relative to position by at least 4 sec. Cells were classified as grid cells if their gridness scores in baseline trials exceeded the 99th percentile of a shuffled distribution of 1000 gridness scores21. Cells were classified as head direction cells if their Rayleigh vectors in baseline trials exceeded the threshold of the 99th percentile population shuffling.
Speed-modulated cells were classified from the general population of the recorded cells following22. Briefly, the degree of speed modulation for each cell was characterised by first defining the instantaneous firing rate of the cell as the number of spikes occurring in each position bin divided by the sampling duration (0.02s). Then a linear correlation was computed between the running speeds and firing rates across all position samples in a trial, and the resulting r-value was taken to characterise the degree of speed modulation for the cell. To be defined as speed-modulated, the r-value for a cell had to exceed the 99th percentile of a distribution of 1000 r-values obtained from spike shuffled data.
When assessing the directional modulation of place and grid cell firing (Figures 3 and 4), apparent directional modulation can arise in binned firing rate data from heterogenous sampling of directions within the spatial firing field23,24. Accordingly we fit a joint (‘pxd’) model of combined place and directional modulation to the data (maximising the likelihood of the data24) and perform analyses on the directional model in addition to the binned firing rate data.
Supplementary Figures
Supplementary Video – example of a mouse performing the ‘fading beacon’ task.
Acknowledgements
We acknowledge support from the Wellcome Trust, European Union’s Horizon 2020 research and innovation programme (grant agreement No. 720270), Biotechnology and Biological Sciences Research Council, European Research Council and China Scholarship Council, and technical help from Peter Bryan, Duncan Farquharson and Daniel Manson.