Updating an allocentric goal from lateralised egocentric visual memories

Domain: chemistry
Published: 2026-03-06
Editorial outcome: major_revision

Abstract
Animals navigate by combining egocentric (viewpoint-dependent) and allocentric (world-referenced) spatial representations, yet how their brains achieve this integration remains unclear. Here we show how the brains of insect expert navigators, such as ants, accomplish this task. Field experiments reveal that ants recognise long-term egocentric visual memories – assumed to be encoded in the Mushroom Bodies – via a lateralized mechanism: instead of memorising views while facing their goal, ants store these memories by looking to the sides. Recognition signals inform whether to turn left or right, but do not directly drive motor responses. Instead, they are processed separately in the two brain hemispheres and integrated to update a goal heading in an ancestral, central brain region –the central complex. This goal heading —now anchored in an allocentric frame—is then used with celestial compass cues for robust steering. Computational models based on insect neural circuits validate this two-stage process, demonstrating how noisy, viewpoint-dependent lateralized inputs are transformed into stable allocentric directional control. These findings reveal how compact brains leverage bilateral processing to combine spatial representations for visual navigation.

Introduction
It is long known that insect expert navigators such as ants, wasps and bees store long-term egocentric visual memories of their surroundings1,2, which are encoded in a brain area called the Mushroom Bodies (MB) 3–6. These visual memories are stored in an egocentric way, requiring the insects –despite their nearly full-panoramic visual field7–9 –to align their gaze direction with what they experienced previously in order to recognise a view 5,10–12. For instance, ants can no longer recognise the natural scene along a familiar one-way route if their gaze is oriented away from the usual direction, such as when dragging a heavy food item backwards13. This calls into question their ability to perform mental rotations of the perceived scene and explains why ants, wasps, and bees need to physically ‘scan’ multiple directions to recognise memorised views and guide their path accordingly 13–17.

The observation that these insects regularly ‘turn back and look’ while departing from their nest or a feeder led to the dominant idea they memorise egocentric views while facing the goal 13,17–23 (or anti-goal 24–26), and must consequently align their body in these same directions to recognise a learnt view as familiar when subsequently returning to the goal 12,20,25,27–33. This is parsimonious as view familiarity – because it is rotation dependant – indicates that the goal is ahead and can simply trigger a ‘go forward’ command30,34. However, it implies that recognition bears no indication as to whether to turn left or right12,20,25,27–33.

It should be noted that ants and flies, when subjected to a sudden rotation of the surrounding visual scene – in the absence of celestial compass cues – are able to correct their course accordingly35,36. This ability can be attributed to a short-term mapping between local visual features and their heading representation in another, ancestral brain area: the central complex (CX) 35,37–39. This mapping does not involve the MB long-term visual memories, which in contrast to the CX, enables – and are necessary for3,4 – the insect to recognise its familiar route after a large displacement.

But how are these long-term, egocentric memories used for steering? Are they learned only when the animal is facing the goal or anti-goal direction? Does their recognition convey information about whether the goal lies to the left or right? Are these mushroom body (MB) visual recognition signals transferred into an allocentric representation in the central complex (CX)? And if so, how is this transformation achieved?

We investigated these questions by combining neural modelling with field experiments in Cataglyphis velox and Myrmecia croslandi ants — two ecologically and phylogenetically distant species that rely on long-term visual memories for route navigation40–43.

Results
The recognition of familiar views triggers compensatory left or right turns.

We first tested whether ants could recognise views misaligned with their goal direction using an open-loop trackball system enabling the experimenter to choose both the position and body orientation of tethered ants directly in their natural environment 44. We captured homing individuals of Cataglyphis velox and Myrmecia croslandi species at the end of their two-way familiar route, just before they entered their nest. This ensured that these so-called zero-vector ants (ZV) can no longer rely on their path integration homing vector and must therefore rely on their terrestrial cue memories to recover their route direction 45. We recorded the motor response of these ants while mounted on the trackball system, placed in the middle of their familiar route, far from the catchment area of the nest, when fixed in eight different body orientations (Fig. 1a, b). Results show that, irrespective of their body orientation, ants steered towards the correct route direction (Fig. 1, Supplementary Figure 1). This was not the case when tested in unfamiliar surroundings (Fig. 1c, d), showing that the lateralised responses observed on the familiar route were triggered by the recognition of the terrestrial visual scene. This demonstrates that ants can recognise familiar views of their route in multiple gaze orientations, and can derive whether the route direction is towards their left or their right. Interestingly, when the body was oriented towards (0°) or away (180°) from the nest/homing route direction, individuals still showed a strong preference for turning on one side (to the left or to the right, depending on individuals) (Fig. 1d). Perhaps it is possible to identify, for each individual, a precise orientation that triggers purely forward movement. However, we note that even when ants are free to recapitulate their familiar route on the ground, they continuously alternate between left and right turns rather than proceed straight40,46–49. Thus, even when facing the route or anti-route direction, recognition of familiar views appears to trigger a ‘left vs. right’ decision rather than a ‘go forward vs. turn’ decision.

Guidance based on memorised views involves the celestial compass.

We showed that the recognition of long-term visual memories along their route – known to be achieved in the MB 3–6 – indicates whether the goal direction is towards the left or right. In principle, guidance could thus be achieved by having these left/right signals directly trigger the left or right motor command. An alternative would be that the visual recognition signals from the MB are used to update a ‘desired heading directions’ in the Central Complex (CX), which in turn uses its own compass information to control steering50,51. This, combined with what we have shown previously, makes a counterintuitive prediction: if the recognition of familiar views triggers a turn towards the correct goal-side (Fig. 1), reversing the direction of the compass representation in the CX should reverse the motor decision. We tested this prediction by mirroring the apparent position of the sun in the sky by 180° to ants tethered to our trackball system. The sun is an important source of celestial compass information, which feeds into the insects’ CX38,52,53, and previous studies have shown that this mirroring manipulation was sufficient to shift compass heading representation in desert ants54,55, including the species used here13.

We first tethered well-trained ZV ants (i.e., captured just before entering the nest) on our trackball system with their body orientation fixed perpendicularly to their familiar route direction. As expected, ants in this situation turned towards the correct route direction (Fig. 2, left panels, natural sun), indicating that they correctly recognised familiar visual terrestrial cues. When mirroring the apparent sun’s position by 180°, these ants responded by turning in the opposite direction within one second (Fig. 2, left panel, mirrored sun). We repeated the experiment by placing such ZV ants in the same compass direction but in an unfamiliar location. In this situation, the ants turned in random directions (Fig. 2, middle panels), showing that the direction initially chosen by the ants on their familiar route (Fig. 2, left panels) was based on the recognition of terrestrial rather than celestial cues. It however remains unclear whether the sun rotation had an impact on ants in unfamiliar terrain, as ants in this situation regularly alternate between left and right turns26. Finally, to ensure that the observed effect on route was not due to an innate bias at this particular location, we repeated this experiment with ants tethered at the exact same route location and body orientation, but trained to an alternative route, which was straight and aligned with the tethered direction of the trackball (Fig. 2, right panel). As expected, these ants facing their route heading showed no preference in turning direction at the group level, although most individuals still strongly favoured one side rather than walking straight (Fig. 2 right panels). Furthermore, mirroring the sun significantly reversed the individual’s chosen direction, even though they were aligned with their goal direction (Fig. 2c right panels).

Taken together, these results demonstrate that guidance based on learnt views is a two-stage process: the recognition of visual memories – assumed to be through the MBs – does not directly drive motor commands, but it instead signals a desired heading – likely in the CX –, which in turn is used to control guidance using celestial compass information.

The CX circuitry is suited to receive lateralised input to produce goal-oriented paths.

We next investigated how information about such a lateralised visual recognition of scenes – presumably computed in the MB – could plausibly be transmitted to the CX for guidance given the known circuitry of insect brains. The CX’s neural architecture is sufficiently mapped and conserved across species to allow biologically constrained neural modelling to address this question 39,56–58. Research shows that the CX performs three key functions: 1) tracking the current heading in substructures such as the Ellipsoid Body (EB) and Protocerebral Bridge (PB) 35,38,59,60; 2) retaining a goal heading representation for tens of seconds or more in the Fan-shaped Body (FB) 51,61,62; and 3) comparing current and goal headings to produce compensatory left/right steering commands 39,51,60–64. The goal heading can be updated via bilateral signals to the FB from external regions51. Such signal may originate from the MB, directly or indirectly through the many relays observed in the dorsal Protocerebrum, notably the Superior Intermediate Protocerebrum (SIP) 5,29,50,51.

Our previous results indicate that the recognition of visual route memories – presumably in the MB – send lateralised information to a centre processing compass information – presumably the CX –, signalling whether the goal is to the left or to the right. Interestingly, self-motion velocity signals sent to the CX, necessary for path integration, are also lateralised, with the left and right hemispheres transmitting velocities peaking at +45° and -45° offsets from the forward heading, respectively 39,65,66. We thus built a model of the CX – as achieved previously for integrating velocity signals 39,57,65,66 – to test how this circuitry instead accommodates lateralised left and right signals from visual recognition in the MB (Fig. 3). The model was implemented in a simulated agent, creating a closed-loop system: the CX steers the agent heading exposing it to recognise left or right visual memories in the MB, which, in turn, updates the CX goal heading and influences subsequent steering decisions. Egocentric visual scene recognition in the MB is inherently noisy and sensitive to gaze orientation 6,12,67,68. To account for this, we modelled visual recognition as requiring, at each time step, a precise alignment of the agent with a memorised ‘offset’ direction (i.e., the ‘directional bias’ in Fig. 3) to generate a strong familiarity signal. Departures from this gaze orientation lead to a rapid decay of the familiarity signal (Fig. 3b), as observed in Mushroom Body models 6,12,67,68. As a result, views are often not recognised—or only recognised with a low familiarity signal—resulting in sporadic, lateralised MB signals being transmitted to the CX during navigation.

The model produced stable route headings, showing the robustness of the CX in processing intermittent inputs to maintain navigational stability (Fig. 3). Impressively, varying parameters – such as the time during which FB neurons sustain their activity, motor or neural noise, the heading angle for which visual recognition is strongest, or large random variation around this angle – hardly had any effect on performance: straight routes emerged as long as left and right hemispheric inputs roughly correlated with a current right and left heading bias, respectively (Fig. 3, Supplementary Figure 2). Conversely, reversing these correlations led to stable route-following in the opposite direction (Fig. 3). Having the MB signal correlating with moments where the agent faces the goal direction (directional bias 0° in Fig.3c,d) corresponds to a labile zone of transition between two stable regimes of route-following in opposite directions. A thorough search through the parameter space revealed that this configuration produces a mediocre directionality at best, and is very sensitive to parameter change (Supplementary Figure 2).

The robustness of the model with lateralised input was evident regardless of how the CX inner circuitry was modelled. Stable route-following emerged whether the CX was designed as in models of path integration in bees39,57 (Supplementary Figure 3) or allocentric travel tracking in flies65,66 (Supplementary Figure 4). This adaptability underscores the CX’s capacity to form a stable goal heading when distinct lateralised input signals are present—one hemisphere signalling a rightward bias and the other a leftward bias. Note, however, that this does not imply that the MBs in each hemisphere exclusively compute left or right visual memories. Instead, both MBs likely process both left and right visual memories in different MB output neurons with opposing valences69, the later being a characteristic feature of such neurons70. Lateralisation likely arises through opponent integration at downstream relay points25,69,71, before being transmitted to the CX.

A closed-loop interaction between egocentric terrestrial and allocentric celestial guidance

To further validate the proposed guidance system, we tested our model by simulating how agents navigating their familiar route would respond to a sudden 135° shift in the CX’s compass estimate of the current heading (Fig. 4). We then compared these simulated behaviours to those of real homing ZV C. velox ants in an analogous scenario, where the sun’s position was artificially shifted by 135° using a mirror as the ants recapitulated their familiar route.

The results highlight a complex closed-loop interaction between the use of long-term terrestrial memory cues and heading estimates derived from compass information. Notably, the sudden shift in the sun’s position triggered the ants to veer immediately, confirming that—even while on a familiar route—their instantaneous guidance is at least partly influenced by their internal compass representation. After walking a few centimetres in the wrong direction, the ants reoriented to realign with the correct route, indicating that terrestrial cues provided corrective directional information (Fig. 4a, green).

Interestingly, this was followed by a period of meandering, suggesting transient uncertainty in directional control (Fig. 4b, green). Remarkably, despite the nonlinear dynamics at play, the simulated responses closely matched those observed in the ants under sun manipulation (Fig. 4a, b, blue). The period of directional uncertainty in the model emerges from a superimposition in the FB of the CX: the previous goal direction (now misaligned with the route given the updated compass representation) overlaps with the newly updated goal direction based on the novel compass frame. The agent resumes a straight path as the old memory trace in the FB decays (Fig. 4c). The fact that ants resume a straight path within seconds suggests a relatively fast decay of this memory trace.

Overall, these results bolster the model’s credibility and offer valuable insights into the mechanisms underlying insect navigation.

Methods
The trackball setup:

For experiments shown in Figs. 1 and 2, we used the air-suspended trackball setup as described in Dahmen et al., 2017 44; and chose the configuration where the ants are fixed in a given direction and cannot physically rotate (if the ant tries to turn, the ball counter-rotates under its legs). To fix ants on the ball, we used a micro-magnet and metallic paint applied directly on the ant’s thorax. The trackball air pump, battery and computer were connected to the trackball through 10 m long cables and hidden in a remote part of the panorama. The trackball movements were recorded using custom software in C++, data was analysed with Matlab R2016b.

Route setups and ant training in Cataglyphis velox:

For all experiments (Figs.1, 2 and 4), Cataglyphis velox ants were constrained to forage within a route using half-sunken wood planks that prevented their escape, while leaving the surrounding panoramic view of the scenery intact (as described in Wystrach et al., 2012107). Nest to feeder distances were 10 m (2 m wide corridor) in experiment 1 (Fig. 1), 4 m (1 m wide corridor for the straight part) in experiment 2 (Fig.2), and 10 m (2 m wide corridor) in experiment 3 (Fig. 4). Cookie crumbs were provided ad libitum in the feeder positions for at least two days before any tests.

Some barriers dug into the ground created baffles, enabling us to control whether ants were experienced with the route. Ants were considered trained when able to home along the route without bumping into any such obstacle. These ants were captured just before they entered their nest to ensure that they could not rely on path integration (so-called ZV ants), marked with a metallic paint on the thorax and a colour code for individual identification, and subjected to tests (see next sections).

Route setups and ant training in Myrmecia croslandi:

For the experiment with Myremcia croslandi ants (Fig. 1), we used each individual’s natural route, for which these long-lived ants have extensive experience 41. Individuals were captured on their foraging trees, marked with both metallic paint and a colour code for individual identification, given a sucrose solution or a prey and released where they had been captured (on their foraging tree). Upon release, these ants immediately started to return home. We followed them while marking their route using flag pins every 50 cm (so that their exact route was known). We captured the ants just before they entered their nests and subjected them to the test, with the trackball (see next section) placed exactly on their route (thanks to the flags) at a distance of at least 8 metres away from their nest. Total route lengths varied from 10 to 27 metres and were overall straight.

Experimental protocol for the left/right trackball experiment (Fig. 1):

1- An experienced ant was captured just before entering its nest, and marked with a drop of metallic paint on the thorax.

2- A large opaque ring (30 cm diameter, 30 cm high) was set around the trackball setup.

3- The ant was fixed on the trackball within the opaque ring, which prevented her to see the surroundings. Only a portion of sky above was accessible to the ant.

4- The trackball system (together with the opaque ring and the fixed ant within) was moved to the desired position and rotated so that the ant was facing the desired direction.

5- One experimenter started recording the trackball movements (from the remote computer), when another lifted the ring (so the ant could see the scenery) before leaving the scene, letting the ant behave for at least 15 seconds post ring lifting.

6- The experimenter came back, replaced the ring around the trackball system, and rotated the trackball system (following a pre-established pseudo random sequence) for the ant to face in a novel direction.

7- We repeated steps 5 and 6 until the 8 possible orientations were achieved (the sequence of orientations were chosen in a pseudo-random order so as to counter-balance orientation and direction of rotation).

The data shown in Fig. 1 for each orientation is averaged across 12 seconds of recording (from 3 to 15 seconds post- ring lifting). We decided to let 3 seconds after ring lifting, because the experimenter’s movements before leaving the area might disturb the ants.

In all experiments, Cataglyphis ants were tested only once. In Experiment 1, Myrmecia ants were tested successively in familiar and unfamiliar terrain, in randomised order.

Experimental protocol for the mirror trackball experiments (Fig. 2):

1- An experienced ant was captured just before entering its nest, and marked with a drop of metallic paint on the thorax.

2- A large opaque ring (30 cm diameter, 30 cm high) was set around the trackball setup.

3- The ant was fixed on the trackball within the opaque ring, which prevented her to see the surroundings. Only a portion of sky above was accessible to the ant.

4- The trackball system (together with the opaque ring and the fixed ant within) was moved to the desired position and rotated so that the ant was facing the desired direction.

5- One experimenter started recording the trackball movements, when another lifted the ring (so the ant could see the scenery) before leaving the scene, letting the ant behave for at least 10 seconds post ring lifting.

5- Two experimenters simultaneously hid the real sun using a 50 x 50 cm wood board, and projected the reflected sun using a mirror, so that the sun appeared in the opposite half of the sky relative to the ant (as in 13), for at least 8 seconds.

Ants were tested only once, in one of the conditions. For the two-legged route, individual attribution to the conditions ‘On route’ and ‘Unfamiliar’ were interleaved. The straight route condition was achieved subsequently as it require to train a different cohort of individual. Note that the present and following experiment could not be achieved with Myrmecia ants, whose compass system, probably adapted to the forest environments where the sun is often occluded, is rather insensitive to the sun’s position. The compass system of the desert ant Cataglyphis velox, on the other hand, relies heavily on the sun’s position13.

Experimental design and protocol for the mirror experiment with ants on the floor (Fig. 4):

Cataglyphis velox ants were trained to a 10 meters-long route (route corridor 2m wide) for at least two consecutive days. A 240 × 120 cm thin wood board was placed on the floor in the middle of the route, ensuring that the navigating ants walked smoothly without encountering small clutter over this portion of the route. Homing ants were captured just before entering their nest and released at the feeder as ZV ants. Upon release, these ZV ants typically resume their route homing behaviour; at mid-parkour (halfway along the board section) the real sun was hidden by one experimenter and reflected by another, using a mirror, for the sun to appear to the ant 135° away from its original position in the sky. To ensure that each individual was tested only once, tested ants were marked with a drop of paint after the procedure.

The ZV ants walking on the board were recorded using a Panasonic Lumix DMC-FZ200 camera on a tripod, and their paths were digitised frame by frame at 10 fps using image J. We used four marks on the board to correct for the distortion due to the tilted perspective of the camera’s visual field. Analysis of the paths were achieved with Matlab R2016b 2016.

The central complex neural models.

The central complex models circuitries and input signals are described in Supplementary Figures 3 and 4, and the different parameters used to obtain the output (motor command) are described in Supplementary Figure 2. All the modelling has been achieved with Matlab R2016b 2016.

Statistics & Reproducibility

No statistical method was used to predetermine sample size. We expected strong, dichotomous effects that would clearly verify our a priori predictions (left vs. right turns on the trackball; Figs. 1–2; left turns accompanied by a sharp increase in sinuosity upon mirroring the sun; Fig. 4). Therefore, we used a relatively small sample size and non-parametric statistical tests, aiming to detect robust effects while minimizing the likelihood of false positives.

No individuals were excluded from the analyses. Experimental conditions were randomized as described in the respective protocols (see “Experimental protocol for the left/right trackball experiment,” Fig. 1; and “Experimental protocol for the mirror trackball experiments,” Fig. 2). Data consisted of video tracking (Fig. 4) and sensor recordings of trackball movements (Figs. 1–2), which were automatically captured and thus not subject to investigator bias during recording.

Reporting on sex

Because ant foragers are only females, our study is only using female subjects.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this work the authors use a clever combination of controlled behavioural experiments in the ants’ natural environment and computational modelling. They demonstrate that ants, suspended on a trackball to prevent their ability to explore in translation or rotation, show a lateralized steering response towards a familiar route when placed at a range of orientations on the route. Next they show that rotating the allocentric cue (i.e. the sun), leads to a corresponding rotation in the initial trajectory of these ants. Hence, the allocentric celestial compass is involved in egocentric guidance based on memorised visual cues. The authors go on to demonstrate how noisy, lateral inputs corresponding to visual familiarity and associated with a turning command, together with global cues, allow for a central complex circuitry based model resulting in directed control. This is robust to a range of parameters and also accurately reflects the shift experienced in the trajectory of real ants exposed to a rotation in the celestial cue. This is exciting and significant research, however, the modeling requires more explanation and it is not clear how a fundamental change in behaviour (directed backward facing routes) is achieved between the main work and the supplementary materials (see comments below). Overall, this work puts forward a novel, biologically plausible and parsimonious strategy for visual route following. We only have sparse knowledge how brains achieve the remarkable navigational behaviour observed in insects and this work significantly improves our understanding how the brains of insects integrate information from egocentric and allocentric cues for navigation. Please see the specific comments below.\nMain text:\nL33-35: This assumes that ants are unable to perform mental rotation, as being able to do so would provide an indication as to whether to turn left or right. Ardin et al (Frontiers 2016) don’t rule out mental rotation when considering the ability of ants to home when travelling backward. Either this needs to be considered as a possibility for generating lateral commands, or its discounting should be appropriately justified.\nL34: Requires unpacking of what recognition means and what the steps of this process are, explaining visual recognition signals (as later appearing in L60).\nL50-54 & L86-88: It seems that this response at 0 degrees could be because the route is not perfectly straight. Especially if distinct left/ right responses are triggered for individual ants but are not apparent on a global scale. The routes of ants are tortuous and idiosyncratic (e.g. Kohler & Wehner 2005, Mangan & Webb 2012). Are these ants placed at orientations towards the ‘average’ overall direction of the route, but might they, on a smaller scale, actually be oriented with a rotational offset to locally learned views? Particularly given the statements in lines L191-193 (and also to some degree L14-15 in the abstract) on naive ants. These ants exhibit exploratory routes and, as stated, ‘expose their gaze in all directions’, perhaps as a result of this exploratory route structure.\nL52: Should probably be figure 1d?\nL107: Explain what ‘SIP’ is.\nL119: how did you model visual recognition, what was the structure of the proxy like?\nL118-121: How are ‘sporadic signals transmitted to the CX’, if there is a strong familiarity signal at ‘each time step’? This sentence requires clarification. Also, it is also not entirely clear what ‘fortunate alignment’ means.\nL108-121: Additionally to the above, the modelling could use more explanation. For example, figure 3b needs to be briefly explained in the text. Additionally, some of the explanations in the supplementary materials are better suited to the main body to aid in explanation of the modelling, for example, some of the parameter explanation (L33-63 in supplementary materials) and some of the information in extended figure 2 could be briefly highlighted here.\nL147-157: Very little comment on the results, needs more description. What are the nonlinear dynamics at play? Why is it remarkable? How is the model in agreement with the experimental results?\nL191-193: For clarity here it would be useful to reiterate that left/ right visual memories are tied to signaling which way to turn, rather than a ‘step forward’ (e.g., as in L176-177). Perhaps when leading into line L194, as this feels like a jump.\nL216: There is more information about the data analyses required in the method sections. For example: (1) For figure 1: How did you calculate (right-left)/(right+left)? Did you define full turns and then calculated if they were left or right turns or did you add up the angular changes between frames? Also, say how you defined ‘preferred side’. (2) For figure 2: Over what time period did you calculate angular velocity in b and c? (3) For figure 4b: Were turns and bearings calculated over a segment? You have to briefly say how you have calculated these.\nL228-237: Please provide the nest-to-feeder distance for each experiment. How wide were the routes?\nL240: Should probably be figure 1 instead of 2.\nL242: How far away were these trees?\nL245-246: State more clearly why you had to know their exact route. Did you track the routes as well or did you simply do some measurements while the flags were there?\nL249: Please say in this section how far away from the nest you placed the trackball.\nL267: Should be figure 1.\nL270: Ants were tested either at unfamiliar or familiar sites but tested in all directions. Hence, ants were tested multiple times. I would thus slightly rephrase this to make it clearer.\nL284: What did you use to hide the sun?\nL284-286: I suggest citing here a reference where this was done before.\nL287: See comment for L270.\nFigures\nFigure 1\na/b: A panoramic image representing the ants’ view would aid this figure.\nc/d: Examples of the path from -90, -45, 0, 45 etc. would assist this figure, however, this could also go in the supplementary information. Also, would there be a way to add some statistics here that backs up your conclusion?\nFigure 2\nL523: Clarification on 5s is required, is this instantaneous or over a period?\nL524-527: The wording ‘turn towards the right...’ is confusing (e.g. in the middle or right panel they don’t have a preference to turn right, isn’t?). Did you test if data is different from 0? It needs to be much clearer what exactly you have tested and about which panel you are talking about. As this is paired data, it seems like an opportunity for a pairwise comparison between each of the pairs of ‘natural’ and ‘mirrored’ sun. Only one statement of ‘power’ in the stats, rather than throughout stats in 2b.\nc: Arrows are not in the panels described (middle and left as opposed to middle and right), not specifically pointing at the reversed trajectory velocities.\nFigure 3\na: DNs needs to be explained, was the intention MNs?\nc: need to say that the yellow/ orange arrows represent the directional bias (if this is the case!).\nBetween b/c: To aid in a more step by step explanation, a subfigure perhaps like that of the top right panel of extended 2.\nFigure 4\nb: you need to say if this is the mean. What do the error bars represent? Also, would there be a way to add some statistics here that backs up your conclusion?\nc: Black dot in ant heading representation is confusing, perhaps a different marker and a title in the key saying ‘route direction according to celestial compass’?\nc: Colours behind arrows are confusing, as the yellow is very similar to the background colour when the sun has been rotated. Remove or modify.\nc: You need to explain the width and height of the yellow and orange bars around the ant heading representation diagrams (circles).\nL570: for consistency, counter clockwise or left?\nL573: wrong figure reference (Figure 3, not 1)\nL577-578: more details required on segment analysis, what does a segment consist of?\nSupplementary Materials\nExtended figure 1:\nThe colour bar covers the same colour ranges as in figure 3, but now indicates ‘directed’ (blue) to ‘random’ (red). For consistency with figure 3, use the range ‘directed’ (blue) to ‘random’ (green).\nImportantly, what is the difference between extended figure 1 and figure 3 from the main paper? What is the difference in implementation or parameter which means that the opposite paths don’t occur anymore? This is not clear, the explanation in L10-18 requires more detail.\nExplain the difference between ‘visual recognition directional basis (average)’ in extended figure 1a and ‘visual recognition directional basis’ in main figure 3d.\nExtended figure 2:\nTop right panel labelling is confusing, use subheadings to denote the steps and keep a, b, c etc for main panels of this figure.\nSee also the comments for figure 4 in main text.\nBe a little bit more specific how the yellow and orange histograms relate to the arced bars in the circular representation in (e).\nL72: as observed in other insects, please specify.\nL73: ‘spaced’ 45 degrees apart, or similar phrasing, for clarification.\nL99-100: ‘Numbers on the left indicate neuron numbers’, please clarify (e.g. ‘where brain areas are preceded by a number, this indicates the number of neurons’)\nExtended figure 3:\n3f is not in agreement with 3g. If 3g is correct, why doesn't the backward behaviour happen here but in the paths shown in 3f?\nAs above, top right panel labelling is confusing, use subheadings to denote the steps.\nWhy is ‘average’ missing from the x axis title in 3g?\nL139: HΔB, PFNs etc. are not defined, make sure any new regions or abbreviations are introduced.\nGeneral\nLines 4, 10, 12 13, 44, 79, 131. Referring to figure 1 in main text, when this should be figure 3.\nSome typos for main and extended:\nL40: ensure → ensured\nL42: cues → cue\nL47: was → were\nL49: orientation → orientations\nL68: feed → feeds\nL96: produces → produce\nL113: signal → signals\nL113/114: accommodates instead → instead accommodates\nL119: as → at\nL159: brackets instead of these hyphens\nL166: enables to steer the direction … → enables steering in the direction…\nL184: eyes → eye\nL198: feedbacks → feedback\nL229: them to → their\nL267/268: sec → seconds\nFigure 3, L546: bracket\nFigure 4, L583: situation → situations\nSupplementary:\nL151: …neurons (DN), which difference… → …neurons (DN), for which difference…\nWe hope these comments will help improve your paper. Nice work!\nAmany Amin & Cornelia Buehlmann", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The manuscript by Wystrach et al is a very interesting combination of behavioral experiments and computational modeling that provides surprising novel insight into the possible neural mechanisms underlying ant navigation. It addresses how view based memories are integrated with vectorial representations of traveling directions to enable robust route following. The experiments are elegant and combine track-ball setups used outdoors in the natural environment of desert ants to investigate how these insects respond to familiar and unfamiliar scenes presented at specific angles relative to their body axis. The main result is that ants appear to recognize familiar views irrespective of their own orientation, but respond with distinct steering movements when the familiar scene is towards the right or left compared to their goal heading along the familiar route. This elegantly combines view recognition and celestial compass navigation, gives a first idea of why two memory centers (mushroom bodies) are needed in the brain, but only one central complex (i.e. an explanation for lateralization), and provides a clear concept of how insects can use the same steering circuits for highly different navigational strategies.\nThe predictions from the presented computational models are nicely checked against real data and make this model highly convincing. The emerging concepts are also relevant beyond ants and illuminate how neural systems can evolve new navigation strategies despite constraints imposed by ancient circuit components (or in fact by taking advantage of them). The presented circuit level hypotheses are testable by experiments at the neural level and thus have the potential to move the field forwards significantly. Overall, the work is of high quality, clear writing and nicely illustrated. I have two main points below that need to be addressed, as well as several minor points, but generally think that this paper would be a suitable contribution for Nature Communications.\nMajor comments\n1. The experiments are directly interpreted in a neural context and the text leaves the impression that the neural conclusions directly follow from the data. This is however not the case, as no neural manipulations are presented that allow the confirmation of the involvement of the CX and the MB in the processes described. While I agree that the explanations are perfectly in line with the current understanding of these brain regions, and likely are not incorrect, these points are still only predictions that match the data, and no direct conclusions from the data. The authors should thus change the language of the text to adjust accordingly. E.g. it states: “…results indicate that MB recognition of route memories send lateralized information to the CX.” The results do not show that information is sent to the CX. While likely, other possibilities also exist in the brain and cannot be ruled out without direct experimental manipulations of the involved neural pathways. This would of course be far beyond the scope of the paper, and the issue can be fixed with careful rewording. Similar statements can be found throughout the paper.\n2. It is currently not fully clear what the second ant species adds to the paper, especially as only experiment 1 was carried out with both, and the other figures only show Cataglyphis data. It should be clarified why the second species was used for experiment 1 and why this was not needed for the remaining experiments. Showing that the identified mechanisms are generally important across distantly related ants is important in my view, but this idea should be highlighted more if that point should come across as a main conclusion.\nMinor comments\n1. Minor grammar edits needed throughout the paper.\n2. Extended data figure 3, schematic network diagram: EPG neurons are only described as excitatory in the literature, but here are both excitatory and inhibitory. Maybe replace with delta7 (also indicating heading), as those have been shown to be mostly inhibitory, but also excitatory towards one PFN neuron type in flies (so that would fit the model very nicely).\n3. Extended data figure 2: Using fly neuron names in one figure and the old bee neuron names in another is confusing. I recommend using the fly names consistently, as these will be more widely known in the field.\n4. Modelling figures: Yellow lines and fonts are nearly impossible to read on a print out version of the paper.\n5. line 205: remove ’the’ before Prof Hansjürgen Dahmen", "reviewer_4": "Decades of remarkable field studies in different species of foraging ants have revealed their use of path integration and route following in navigating away from and then back to their nests. Meanwhile, anatomical and physiological insight from studies, mainly in other insects, have highlighted the how specific highly conserved insect brain regions, particularly the central complex (CX) and mushroom body (MB), might play complementary roles in implementing such algorithms. Models inspired by behavioral, physiological, and anatomical studies have suggested specific computations for circuits within these brain regions, but these models have yet to be fully tested. Specifically, the CX is known to carry out head-direction or compass-based computations while the MB provides the substrate for associative memory that could include some form of visual-template learning. This manuscript uses a fascinating and clever open-loop track-ball setup in the field to record the behavioral responses of ants to views at familiar locations along their foraging route. The authors show that views trigger directional turning responses which seek to align the animal to the route direction. Further, the directionality of the responses is dependent on the location of an allocentric (global) sun-compass cue in the ant’s visual field. This, along with known anatomy and prior experimental work, enabled the authors to build a computational model that utilizes a feedforward signal from the MB to the CX. The experimental data and the modeling work in this manuscript have the potential to be of considerable interest to researchers working in animal behavior and studying the neuroscience of navigation, although there are some issues that deserve further attention before publication.\nThe authors argue that the left and right MBs (L and R MBs) store views that the ant experiences to the left and the right of the learnt route direction at a given location (“left view” and “right view”). To determine which direction to face at a given location along the ant’s return journey, the current view is independently compared with the left and right views which generate two independent familiarity signals that are then passed on to the central complex (CX), specifically the fan shaped body (FB) neurons (also known as PFNs). These independent left and right familiarity signals can then be used by extensively modeled CX circuitry to compute a turning direction. Overall, the behavioral data is novel in its approach and findings and certainly indicates the presence of a feedforward signal from the MB to the CX. However, the data seem insufficient to suggest that the MB stores left and right views, which are independently compared with the current view presented to the ant. The manuscript might benefit by modifying the model to explicitly account for these gaps.\nMajor Concerns\nIn the experiment, the ant is “clamped” to one of 8 directions at a single familiar location along its route and its turning response is recorded for a 20 second duration. The authors find that the presentation of a view not along the route direction directs turns that seek to align the animal with the route. The claim that the MBs store left and right views at every point along the route is not uniquely (or strongly enough) supported by the behavioral data. It is possible that the ant stores a single view in the direction of the route across synapses in both the L and R MB, and that the comparison of the current view with this single stored view contains directional information. This might still lead to distinct familiarity signals in the L and R MBs which could drive the L and R PFNs differently as proposed in the model. The manuscript might benefit from explicit descriptions, if possible, of the scenes viewed by the ant at the given experimental location to clarify whether directional information can be extracted by a comparison of a side view with the view in the route direction. Further, explicitly modeling how the MB signal is computed from the scene (as opposed to proposing a form for the signal relative to the route direction) might provide useful insight into the possible ways in which views are stored and compared in the MB, and exactly how these different ways either account for the observed data or fall short.\nMinor Concerns\nFigure 1: “Cumulative turn ratio” instead of “Cumulated turn ratio”?\nMethods don’t contain a description of the turn ratio. Why was 12 seconds chosen for averaging?\nHow is goal direction experimentally determined? Is it the direction towards the nest? Is it the direction tangential to the route? Are these the same for the chosen route? From previous work one would imagine it’s tangential to the route, but the methods section should be more explicit about this as well.\nHere it might be useful to see the shape of the route.\n33: Lent 2010, 2013 suggest ants can compute turn direction based on the presented view. This should be cited and discussed.\n48: The sinusoidal curve in Fig. 1c can be explained without the need for lateralized signals from the MB to the PFNs. The existence of some goal direction vector in the FB aligned with the route direction should produce the curve shown. It would be useful to plot the turn ratio for each individual ant in the unfamiliar location to see if it has a similar curve which is not centered at the route direction. In general, individual data points might be more informative than averaged curves.\nIt might also be useful to analyze behavior conditioned on the order in which the ant was exposed to each of the 8 directions. Does the turn size (mean angular velocity) change as the ant samples each orientation? Is the change in turn size predicted based on which view the ant was previously exposed to?\n49: orientations instead of orientation\n65: Cartwright and Collett (1983) suggest that landmark navigation might depend on the allocentric reference frame. This should be cited.\n82: remove extra \"ants\"\n141: Doesn’t it still requires two separate plasticity events at the same location when the ant is facing left and when it is facing right of the route? How would this happen?\n184-185: Unclear if individual KCs pool over multiple VPNs and what the proportion of ipsilateral dominant vs contralateral dominant KCs is.\n198: Motor feedback to the MB from the CX in general might not be sufficient, it should be specific to the DANs. Is there evidence in the connectivity for this?", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Dear editor,\nWe are very grateful to the four reviewers for their time, enthusiasm, and constructive feedback.\nWe have incorporated their suggestions into the revised manuscript, added an additional figure\n(Extended Data Fig. 1), and included new statistical analyses. All changes in the text are highlighted\nin yellow. Below, you will find our detailed responses (in bold) to each comment.\nWith best wishes,\nAntoine\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this work the authors use a clever combination of controlled behavioural experiments in the ants’\nnatural environment and computational modelling. They demonstrate that ants, suspended on a\ntrackball to prevent their ability to explore in translation or rotation, show a lateralized steering\nresponse towards a familiar route when placed at a range of orientations on the route. Next they\nshow that rotating the allocentric cue (i.e. the sun), leads to a corresponding rotation in the initial\ntrajectory of these ants. Hence, the allocentric celestial compass is involved in egocentric guidance\nbased on memorised visual cues. The authors go on to demonstrate how noisy, lateral inputs\ncorresponding to visual familiarity and associated with a turning command, together with global\ncues, allow for a central complex circuitry based model resulting in directed control. This is robust to\na range of parameters and also accurately reflects the shift experienced in the trajectory of real ants\nexposed to a rotation in the celestial cue. This is exciting and significant research, however, the\nmodeling requires more explanation and it is not clear how a fundamental change in behaviour\n(directed backward facing routes) is achieved between the main work and the supplementary\nmaterials (see comments below). Overall, this work puts forward a novel, biologically plausible and\nparsimonious strategy for visual route following. We only have sparse knowledge how brains achieve\nthe remarkable navigational behaviour observed in insects and this work significantly improves our\nunderstanding how the brains of insects integrate information from egocentric and allocentric cues\nfor navigation. Please see the specific comments below.\nAnswer: Thanks for your enthusiasm! You will find detailed answers to your concerns (including\nthe backward facing routes) below.\nMain text:\nL33-35: This assumes that ants are unable to perform mental rotation, as being able to do so would\nprovide an indication as to whether to turn left or right. Ardin et al (Frontiers 2016) don’t rule out\nmental rotation when considering the ability of ants to home when travelling backward. Either this\nneeds to be considered as a possibility for generating lateral commands, or its discounting should be\nappropriately justified.\n+ L34: Requires unpacking of what recognition means and what the steps of this process are,\nexplaining visual recognition signals (as later appearing in L60).\nAnswer: Indeed. We added a small section in the first paragraph of the introduction to clarify what\nwe meant by ‘visual recognition’, and simultaneously discount mental rotation. “These visual\nmemories are stored in an egocentric way, requiring the insects –despite their close to full-panoramic\nvisual field16–18 –to align their gaze direction as experienced previously to recognise a view 3,19–21. For\ninstance, ants can no longer recognise a one-way familiar route if their gaze is oriented away from the\nusual direction, such as when dragging a heavy food item backwards22. This calls into question their\nability to perform mental rotations of the perceived scene, and explains why ants, wasps and bees need\nto physically ‘scan’ multiple directions to recognise memorised views and guide their path accordingly\n22–26. The observation that these insects regularly ‘turn back and look’ while departing from their nest\nor a feeder led to the dominant idea they memorise views while facing the goal 22,26–32 (or anti-goal 33–\n35), and must consequently align their body in these same directions to recognise a learnt view as\nfamiliar when subsequently returning to the goal 21,29,34,36–42. This is parsimonious as view familiarity\nindicates that the goal is ahead and can simply trigger a ‘go forward’ command39,43. However, it implies\nthat recognition bears no indication as to whether to turn left or right21,29,34,36–42. “\nL50-54 & L86-88: It seems that this response at 0 degrees could be because the route is not perfectly\nstraight. Especially if distinct left/ right responses are triggered for individual ants but are not\napparent on a global scale. The routes of ants are tortuous and idiosyncratic (e.g. Kohler & Wehner\n2005, Mangan & Webb 2012). Are these ants placed at orientations towards the ‘average’ overall\ndirection of the route, but might they, on a smaller scale, actually be oriented with a rotational offset\nto locally learned views? Particularly given the statements in lines L191-193 (and also to some degree\nL14-15 in the abstract) on naive ants. These ants exhibit exploratory routes and, as stated, ‘expose\ntheir gaze in all directions’, perhaps as a result of this exploratory route structure.\nAnswer: Agreed. We added the following sentence: “Perhaps it is possible to identify, for each\nindividual, a precise orientation—slightly offset from the general route and nest direction—that\ntriggers purely forward movement. However, we note that even when ants are free to recapitulate\ntheir familiar route on the ground, they continuously alternate between left and right turns rather\nthan proceed straight37–41.”\nL52: Should probably be figure 1d?\nAnswer: Yes, thank you\nL107: Explain what ‘SIP’ is.\nAnswer: done. “Such signal may originate from the MB, directly or indirectly through the many relays\nobserved in the dorsal Protocerebrum, notably the Superior Intermediate Protocerebrum (SIP) 1–3,38.”\nL119: how did you model visual recognition, what was the structure of the proxy like?\n+ L118-121: How are ‘sporadic signals transmitted to the CX’, if there is a strong familiarity signal at\n‘each time step’? This sentence requires clarification. Also, it is also not entirely clear what ‘fortunate\nalignment’ means.\n+L108-121: Additionally to the above, the modelling could use more explanation. For example, figure\n3b needs to be briefly explained in the text. Additionally, some of the explanations in the\nsupplementary materials are better suited to the main body to aid in explanation of the modelling,\nfor example, some of the parameter explanation (L33-63 in supplementary materials) and some of\nthe information in extended figure 2 could be briefly highlighted here.\nAnswer: Indeed, we opted for a brief description of the model and left the technical details out of\nthe main rationale. This is because those details are directly drawn from previous literature and\nare not essential to understanding the core, novel contribution of our study. Given the broad\nreadership of Nature Communications, we felt it was better to keep the main text concise and\nfocused (as you noted, the technical details are provided elsewhere). Nonetheless, in light of your\ncomment, we have now added clarifications to the main text regarding how visual recognition is\nmodelled: “we modelled visual recognition as requiring, at each time step, a precise alignment of the\nagent with a memorised ‘offset’ direction (i.e., the ‘directional bias’ in Fig. 3) to generate a strong\nfamiliarity signal. Departures from this gaze orientation lead to a rapid decay of the familiarity signal\n(Fig. 3b), as observed in Mushroom Body models 7,21,67,68. As a result, views are often not recognised—\nor only recognised with a low familiarity signal—resulting in sporadic, lateralised MB signals being\ntransmitted to the CX during navigation.”\nL147-157: Very little comment on the results, needs more description. What are the nonlinear\ndynamics at play? Why is it remarkable? How is the model in agreement with the experimental\nresults?\nAnswer: The detailed explanation was included in figure 4’s legend to save space. I now unpacked\nthe description directly in the main text:\n“The results highlight a complex closed-loop interaction between the use of long-term terrestrial\nmemory cues and heading estimates derived from compass information. Notably, the sudden shift in\nthe sun’s position triggered the ants to veer immediately, confirming that—even while on a familiar\nroute—their instantaneous guidance is at least partly influenced by their internal compass\nrepresentation. After walking a few centimetres in the wrong direction, the ants reoriented to realign\nwith the correct route, indicating that terrestrial cues provided corrective directional information (Fig.\n4a, green).\nInterestingly, this was followed by a period of meandering, suggesting transient uncertainty in\ndirectional control (Fig. 4b, green). Remarkably, despite the nonlinear dynamics at play, the simulated\nresponses closely matched those observed in the ants under sun manipulation (Fig. 4a, b, blue). The\nperiod of directional uncertainty in the model emerges from a superimposition in the FB of the CX: the\nprevious goal direction (now misaligned with the route given the updated compass representation)\noverlaps with the newly updated goal direction based on the novel compass frame. The agent resumes\na straight path as the old memory trace in the FB decays (Fig. 4c). The fact that ants resume a straight\npath within seconds suggests a relatively fast decay of this memory trace.\nOverall, these results bolster the model’s credibility and offer valuable insights into the mechanisms\nunderlying insect navigation.”\nL191-193: For clarity here it would be useful to reiterate that left/ right visual memories are tied to\nsignaling which way to turn, rather than a ‘step forward’ (e.g., as in L176-177). Perhaps when leading\ninto line L194, as this feels like a jump.\nAnswer. Done “providing ample opportunities to form a rich set of left-to-the-goal and right-to-the-\ngoal visual memories 25,28,33,70,84, therefore enabling corrective turns from most positions.”\nL216: There is more information about the data analyses required in the method sections. For\nexample: (1) For figure 1: How did you calculate (right-left)/(right+left)? Did you define full turns and\nthen calculated if they were left or right turns or did you add up the angular changes between\nframes? Also, say how you defined ‘preferred side’. (2) For figure 2: Over what time period did you\ncalculate angular velocity in b and c? (3) For figure 4b: Were turns and bearings calculated over a\nsegment? You have to briefly say how you have calculated these.\nAnswer. This info is now indicated directly in the figure legends (it has been added in fig. 1, and\nwas already present for fig. 2 and 3). All are highlighted in yellow in the new manuscript version.\nL228-237: Please provide the nest-to-feeder distance for each experiment. How wide were the\nroutes?\nAnswer: Done. “Nest to feeder distances were 10 m (2 m wide corridor) in experiment 1 (Fig. 1), 4 m\n(1 m wide corridor for the straight part) in experiment 2 (Fig.2), and 10 m (2 m wide corridor) in\nexperiment 3 (Fig. 4).”\nL240: Should probably be figure 1 instead of 2.\nAnswer: Indeed, thanks.\nL242: How far away were these trees?\n+L245-246: State more clearly why you had to know their exact route. Did you track the routes as\nwell or did you simply do some measurements while the flags were there?\n+ L249: Please say in this section how far away from the nest you placed the trackball.\nAnswer: Information added: “We captured the ants just before they entered their nests and subjected\nthem to the test, with the trackball (see next section) placed exactly on their route (thanks to the flags)\nat a distance of at least 8 metres away from their nest. Total route lengths varied from 10 to 27 metres\nand were overall straight.”\nL267: Should be figure 1.\nAnswer: Indeed, thanks.\nL270: Ants were tested either at unfamiliar or familiar sites but tested in all directions. Hence, ants\nwere tested multiple times. I would thus slightly rephrase this to make it clearer.\nAnswer. Done. “In Experiment 1, Myrmecia ants were tested successively in familiar and unfamiliar\nterrain, in randomised order.”\nL284: What did you use to hide the sun?\nAnswer: Added. “Two experimenters simultaneously hid the real sun using a 50 x 50 cm wood board,\nand projected the reflected sun using a mirror, so that the sun appeared in the opposite half of the\nsky relative to the ant (as in 22), for at least 8 seconds.”\nL284-286: I suggest citing here a reference where this was done before.\nDone\nL287: See comment for L270.\nAnswer: Left unchanged as ants were tested only once.\nFigures\nFigure 1\na/b: A panoramic image representing the ants’ view would aid this figure.\nc/d: Examples of the path from -90, -45, 0, 45 etc. would assist this figure, however, this could also go\nin the supplementary information. Also, would there be a way to add some statistics here that backs\nup your conclusion?\nAnswer: We have now added an extended data figure (4) that includes the ants’ turning dynamics\nacross time. These are more informative than paths (which are hard to interpret as ants were fixed\non the trackball, and the paths typically loop over themselves). We also added a supplementary\ntable of the desired statistics. We unfortunately do not have panoramic pictures of the ants’\nviewpoints.\nFigure 2\nL523: Clarification on 5s is required, is this instantaneous or over a period?\nDone: “Average angular velocity (positive = right turn) of each ant (dots) over the 5-second period\nbefore (white) and after (yellow) the apparent sun’s position was mirrored by 180°.”\nL524-527: The wording ‘turn towards the right...’ is confusing (e.g. in the middle or right panel they\ndon’t have a preference to turn right, isn’t?). Did you test if data is different from 0? It needs to be\nmuch clearer what exactly you have tested and about which panel you are talking about. As this is\npaired data, it seems like an opportunity for a pairwise comparison between each of the pairs of\n‘natural’ and ‘mirrored’ sun.\nAnswer: We tested for whether the ants ‘turned towards the right with natural sun’, with a\nWilcoxon signed rank test, which amount to test whether the average angular velocity is > 0, which\nis now mentioned “(signed-rank test for angular velocity < 0)”. This first test is not for paired data,\nas it concerns here only the ‘natural sun’ condition (as indicated). This is just to ensure that the\nfirst group is oriented towards the right, and that the two controls (middle and right panel), are\nnot. Note: the next test is necessarily individually paired, as we mention testing for ‘turn direction\nreversal’.\nOnly one statement of ‘power’ in the stats, rather than throughout stats in 2b.\nAnswer: Done for the 3 groups x 2 tests in the legend of Figure 2.\nc: Arrows are not in the panels described (middle and left as opposed to middle and right), not\nspecifically pointing at the reversed trajectory velocities.\nAnswer: “middle and right”, thanks.\nFigure 3\na: DNs needs to be explained, was the intention MNs?\nAnswer: indeed, corrected, thanks\nc: need to say that the yellow/ orange arrows represent the directional bias (if this is the case!).\nAnswer: Indeed, done\nBetween b/c: To aid in a more step by step explanation, a subfigure perhaps like that of the top right\npanel of extended 2.\nAnswer: We prefer stowing this in the extended data to keep the main manuscript length\nmanageable. Also, showing both inputs across 360° on a same graph provides a better\nrepresentation of how the azimuths are covered. In case doubts persist for some readers, I added\nin the figure legend panel b: “(see top right panel of Extended Data fig. 3, 4 for a broken-down\nrepresentation)”.\nFigure 4\nb: you need to say if this is the mean. What do the error bars represent?\nAdded “(mean ± se)”.\nAlso, would there be a way to add some statistics here that backs up your conclusion?\nAnswer: Designing a statistical test in this case would be somewhat post-hoc, as the experiment\nwas not originally designed to test a specific behavioural hypothesis, but rather to observe the\nemerging dynamics and assess whether a model could qualitatively capture some of them. It is also\nworth noting that the difference between the Sham and Mirrored Sun conditions is visually\napparent in both the trajectories and the quantified data shown here (as evidenced by the very\nlarge separation between standard error bars in Fig. 4b).\nc: Black dot in ant heading representation is confusing, perhaps a different marker and a title in the\nkey saying ‘route direction according to celestial compass’?\nc: Colours behind arrows are confusing, as the yellow is very similar to the background colour when\nthe sun has been rotated. Remove or modify.\nc: You need to explain the width and height of the yellow and orange bars around the ant heading\nrepresentation diagrams (circles). L573: wrong figure reference (Figure 3, not 1)\nL577-578: more details required on segment analysis, what does a segment consist of?\nAnswer: All done, thanks\nL570: for consistency, counter clockwise or left?\nAnswer: Clockwise (or counterclockwise) when it refers to a rotation, left or right when it refers to\na direction.\nSupplementary Materials\nExtended figure 1:\nThe colour bar covers the same colour ranges as in figure 3, but now indicates ‘directed’ (blue) to\n‘random’ (red). For consistency with figure 3, use the range ‘directed’ (blue) to ‘random’ (green).\nAnswer: Reducing the colour range from blue (0°) to green (90°) would strongly decrease the\nreadability of panels b and c, of which ranges are small. Also, it is important to understand that 90°\nis the maximum possible value here (contrary to figure 3), so the full colour range should reflect\nthis. See next comment for more explanation.\nImportantly, what is the difference between extended figure 1 and figure 3 from the main paper?\nWhat is the difference in implementation or parameter which means that the opposite paths don’t\noccur anymore? This is not clear, the explanation in L10-18 requires more detail.\nAnswer: We now clarified the explanation: “a. Same as Fig. 3d, except that, here, the search\nexplores the entire parameter space to find regimes minimizing path directional error. In Fig. 3d,\nunrepresented parameters (motor noise, motor gain, memory decay) were fixed at average values,\nwhich led to routes in the opposite direction (i.e., directional error ~180°) when directional bias < 0. In\ncontrast, the full parameter searches here can select combinations (typically with high motor noise)\nthat produce very tortuous paths—resulting in random directions (directional error ~90°) – rather\nthan routes in opposite directions (~180°). This shows that when visual familiarity bias is < 0, no\nparameter regime can yield straight, goal-oriented paths (directional error ~0°)”\nExplain the difference between ‘visual recognition directional basis (average)’ in extended figure 1a\nand ‘visual recognition directional basis’ in main figure 3d.\nAnswer: They are the same. “(average)” has now been removed to avoid confusion\nExtended figure 2:\nTop right panel labelling is confusing, use subheadings to denote the steps and keep a, b, c etc for\nmain panels of this figure.\nSee also the comments for figure 4 in main text.\nBe a little bit more specific how the yellow and orange histograms relate to the arced bars in the\ncircular representation in (e).\nAnswer: All done, thanks\nL72: as observed in other insects, please specify.\nL73: ‘spaced’ 45 degrees apart, or similar phrasing, for clarification.\nL99-100: ‘Numbers on the left indicate neuron numbers’, please clarify (e.g. ‘where brain areas are\npreceded by a number, this indicates the number of neurons’)\nAnswer: All done (added references for the ‘other insects’)\nExtended figure 3:\n3f is not in agreement with 3g. If 3g is correct, why doesn't the backward behaviour happen here but\nin the paths shown in 3f?\nAnswer: This was because the paths were achieved with fixed parameters, and the map was\nachieved by exploring throughout the parameter space. The map has been remade using fixed\nparameters (as in fig. 3), making both consistent. Thanks for spotting this.\nAs above, top right panel labelling is confusing, use subheadings to denote the steps.\nAnswer: Done\nWhy is ‘average’ missing from the x axis title in 3g?\nAnswer: Average has been removed everywhere\nL139: HΔB, PFNs etc. are not defined, make sure any new regions or abbreviations are introduced.\nAnswer: Done\nGeneral\nLines 4, 10, 12 13, 44, 79, 131. Referring to figure 1 in main text, when this should be figure 3.\nAnswer: Done, thanks!\nSome typos for main and extended:\nL40: ensure → ensured\nL42: cues → cue\nL47: was → were\nL49: orientation → orientations\nL68: feed → feeds\nL96: produces → produce\nL113: signal → signals\nL113/114: accommodates instead → instead accommodates\nL119: as → at\nL159: brackets instead of these hyphens\nL166: enables to steer the direction … → enables steering in the direction…\nL184: eyes → eye\nL198: feedbacks → feedback\nL229: them to → their\nL267/268: sec → seconds\nFigure 3, L546: bracket\nFigure 4, L583: situation → situations\nAnswer: All fixed, thanks\nSupplementary:\nL151: …neurons (DN), which difference… → …neurons (DN), for which difference…\nAnswer: Done\nWe hope these comments will help improve your paper. Nice work!\nAmany Amin & Cornelia Buehlmann\nAnswer: Your comments definitely helped improving the manuscript, thanks heaps for your time\nand accurate read.\nThank you both very much for your detail read and helpful comments.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nThe manuscript by Wystrach et al is a very interesting combination of behavioral experiments and\ncomputational modeling that provides surprising novel insight into the possible neural mechanisms\nunderlying ant navigation. It addresses how view based memories are integrated with vectorial\nrepresentations of traveling directions to enable robust route following. The experiments are elegant\nand combine track-ball setups used outdoors in the natural environment of desert ants to investigate\nhow these insects respond to familiar and unfamiliar scenes presented at specific angles relative to\ntheir body axis. The main result is that ants appear to recognize familiar views irrespective of their\nown orientation, but respond with distinct steering movements when the familiar scene is towards\nthe right or left compared to their goal heading along the familiar route. This elegantly combines\nview recognition and celestial compass navigation, gives a first idea of why two memory centers\n(mushroom bodies) are needed in the brain, but only one central complex (i.e. an explanation for\nlateralization), and provides a clear concept of how insects can use the same steering circuits for\nhighly different navigational strategies.\nThe predictions from the presented computational models are nicely checked against real data and\nmake this model highly convincing. The emerging concepts are also relevant beyond ants and\nilluminate how neural systems can evolve new navigation strategies despite constraints imposed by\nancient circuit components (or in fact by taking advantage of them). The presented circuit level\nhypotheses are testable by experiments at the neural level and thus have the potential to move the\nfield forwards significantly. Overall, the work is of high quality, clear writing and nicely illustrated. I\nhave two main points below that need to be addressed, as well as several minor points, but generally\nthink that this paper would be a suitable contribution for Nature Communications.\nMajor comments\n1. The experiments are directly interpreted in a neural context and the text leaves the impression\nthat the neural conclusions directly follow from the data. This is however not the case, as no neural\nmanipulations are presented that allow the confirmation of the involvement of the CX and the MB in\nthe processes described. While I agree that the explanations are perfectly in line with the current\nunderstanding of these brain regions, and likely are not incorrect, these points are still only\npredictions that match the data, and no direct conclusions from the data. The authors should thus\nchange the language of the text to adjust accordingly. E.g. it states: “…results indicate that MB\nrecognition of route memories send lateralized information to the CX.” The results do not show that\ninformation is sent to the CX. While likely, other possibilities also exist in the brain and cannot be\nruled out without direct experimental manipulations of the involved neural pathways. This would of\ncourse be far beyond the scope of the paper, and the issue can be fixed with careful rewording.\nSimilar statements can be found throughout the paper.\nAnswer: You are absolutely right. We now corrected these parts, which now read for instance:\n“Our previous results indicate that the recognition of visual route memories – presumably in the MB –\nsend lateralised information to a centre processing compass information – presumably the CX –”\nor\n“Taken together, these results demonstrate that guidance based on learnt views is a two-stage\nprocess: the recognition of visual memories – assumed to be through the MBs – does not directly\ndrive motor commands, but it instead signals a desired heading – likely in the CX “.\n2. It is currently not fully clear what the second ant species adds to the paper, especially as only\nexperiment 1 was carried out with both, and the other figures only show Cataglyphis data. It should\nbe clarified why the second species was used for experiment 1 and why this was not needed for the\nremaining experiments. Showing that the identified mechanisms are generally important across\ndistantly related ants is important in my view, but this idea should be highlighted more if that point\nshould come across as a main conclusion.\nAnswer: Indeed, we now mention the reason in the method: “Note that the present and following\nexperiment could not be achieved with Myrmecia ants, whose compass system, probably adapted to\nthe forest environments where the sun is often occluded, is rather insensitive to the sun’s position.\nThe compass system of the desert ant Cataglyphis velox, on the other hand, relies heavily on the sun’s\nposition22”\nAlso, we have leveraged the use of two species in the discussion to give more credence to the\nevolutionary scenario proposed. “The lateralised design shown here, where visual memories indicate\nwhether the goal is on the left- or right-hand side rather than straight ahead, is present in the two\nphylogenetically and ecology distant ant species we tested, which suggests an interesting take on the\nevolution of navigation in bilateral animals.”\nMinor comments\n1. Minor grammar edits needed throughout the paper.\nAnswer: Indeed, we have now performed a careful re-read.\n2. Extended data figure 3, schematic network diagram: EPG neurons are only described as excitatory\nin the literature, but here are both excitatory and inhibitory. Maybe replace with delta7 (also\nindicating heading), as those have been shown to be mostly inhibitory, but also excitatory towards\none PFN neuron type in flies (so that would fit the model very nicely).\nAnswer: Excellent point, thank you.\n3. Extended data figure 2: Using fly neuron names in one figure and the old bee neuron names in\nanother is confusing. I recommend using the fly names consistently, as these will be more widely\nknown in the field.\nAnswer: Done\n4. Modelling figures: Yellow lines and fonts are nearly impossible to read on a print out version of the\npaper.\nAnswer: Yellow has been replaced by a darker green\n5. line 205: remove ’the’ before Prof Hansjürgen Dahmen\nAnswer: Done\nThank you very much for your appreciation and helpful comments.\nReviewer #4 (Remarks to the Author):\nDecades of remarkable field studies in different species of foraging ants have revealed their use of\npath integration and route following in navigating away from and then back to their nests.\nMeanwhile, anatomical and physiological insight from studies, mainly in other insects, have\nhighlighted the how specific highly conserved insect brain regions, particularly the central complex\n(CX) and mushroom body (MB), might play complementary roles in implementing such algorithms.\nModels inspired by behavioral, physiological, and anatomical studies have suggested specific\ncomputations for circuits within these brain regions, but these models have yet to be fully tested.\nSpecifically, the CX is known to carry out head-direction or compass-based computations while the\nMB provides the substrate for associative memory that could include some form of visual-template\nlearning. This manuscript uses a fascinating and clever open-loop track-ball setup in the field to\nrecord the behavioral responses of ants to views at familiar locations along their foraging route. The\nauthors show that views trigger directional turning responses which seek to align the animal to the\nroute direction. Further, the directionality of the responses is dependent on the location of an\nallocentric (global) sun-compass cue in the ant’s visual field. This, along with known anatomy and\nprior experimental work, enabled the authors to build a computational model that utilizes a\nfeedforward signal from the MB to the CX. The experimental data and the modeling work in this\nmanuscript have the potential to be of considerable interest to researchers working in animal\nbehavior and studying the neuroscience of navigation, although there are some issues that deserve\nfurther attention before publication.\nThe authors argue that the left and right MBs (L and R MBs) store views that the ant experiences to\nthe left and the right of the learnt route direction at a given location (“left view” and “right view”). To\ndetermine which direction to face at a given location along the ant’s return journey, the current view\nis independently compared with the left and right views which generate two independent familiarity\nsignals that are then passed on to the central complex (CX), specifically the fan shaped body (FB)\nneurons (also known as PFNs). These independent left and right familiarity signals can then be used\nby extensively modeled CX circuitry to compute a turning direction. Overall, the behavioral data is\nnovel in its approach and findings and certainly indicates the presence of a feedforward signal from\nthe MB to the CX. However, the data seem insufficient to suggest that the MB stores left and right\nviews, which are independently compared with the current view presented to the ant. The\nmanuscript might benefit by modifying the model to explicitly account for these gaps.\nMajor Concerns\nIn the experiment, the ant is “clamped” to one of 8 directions at a single familiar location along its\nroute and its turning response is recorded for a 20 second duration. The authors find that the\npresentation of a view not along the route direction directs turns that seek to align the animal with\nthe route. The claim that the MBs store left and right views at every point along the route is not\nuniquely (or strongly enough) supported by the behavioral data. It is possible that the ant stores a\nsingle view in the direction of the route across synapses in both the L and R MB, and that the\ncomparison of the current view with this single stored view contains directional information. This\nmight still lead to distinct familiarity signals in the L and R MBs which could drive the L and R PFNs\ndifferently as proposed in the model. The manuscript might benefit from explicit descriptions, if\npossible, of the scenes viewed by the ant at the given experimental location to clarify whether\ndirectional information can be extracted by a comparison of a side view with the view in the route\ndirection.\nAnswer: Yes, this requires some clarifications. As you mentioned, our main conclusion is agnostic\nto how left and right signal arise from the MB. Nonetheless, we modelled familiarity as assuming\nmultiple stored memories, which –contrary to the assumption that the ant could derive a direction\nfrom a current view misaligned with its memory – is in phase we the previous behavioural and\nneurological literature. We now emphasize in the introduction the previous literature showing that\nants store and recognise views in a fundamentally egocentric way, and cannot derive directional\ninformation when misaligned with their stored views.\n“These visual memories are stored in an egocentric way, requiring the insects –despite their nearly\nfull-panoramic visual field16–18 –to align their gaze direction with what they experienced previously in\norder to recognise a view 3,19–21. For instance, ants can no longer recognise the natural scene along a\nfamiliar one-way route if their gaze is oriented away from the usual direction, such as when dragging\na heavy food item backwards22. This calls into question their ability to perform mental rotations of the\nperceived scene and explains why ants, wasps, and bees need to physically ‘scan’ multiple directions\nto recognise memorised views and guide their path accordingly 22–26”\nAlso, we give more details onto why we modelled visual recognition as multiple memory banks:\n“Egocentric visual scene recognition in the MB is inherently noisy and sensitive to gaze orientation\n7,21,67,68. To account for this, we modelled visual recognition as requiring, at each time step, a precise\nalignment of the agent with a memorised ‘offset’ direction (i.e., the ‘directional bias’ in Fig. 3) to\ngenerate a strong familiarity signal. Departures from this gaze orientation lead to a rapid decay of\nthe familiarity signal (Fig. 3b), as observed in Mushroom Body models 7,21,67,68. As a result, views are\noften not recognised—or only recognised with a low familiarity signal—resulting in sporadic,\nlateralised MB signals being transmitted to the CX during navigation.”\nFinally, we emphasize in the discussion some aspects of behaviour which further back up the idea\nthat ants do not only learn views that aligned with or away from the nest direction:\n“This fits with the observation that naive insects exploring the world for the first time expose their\ngaze in all directions –and not only in the nest or anti-nest direction– providing ample opportunities\nto form a rich set of left-to-the-goal and right-to-the-goal visual memories 25,28,33,70,84, therefore\nenabling correct corrective turns from most positions.”\nFurther, explicitly modeling how the MB signal is computed from the scene (as opposed to proposing\na form for the signal relative to the route direction) might provide useful insight into the possible\nways in which views are stored and compared in the MB, and exactly how these different ways either\naccount for the observed data or fall short.\nAnswer: We fully agree. However, given the scope and length of implementing a full-fledged MB\nneural model—and importantly by the fact that it is not necessary to support our main conclusions\nin the first place—we have treated this task separately. Specifically, we developed a neural MB and\nCX model embedded in an agent operating within a 3D environment. This constitutes a separate\npaper, currently available on bioRxiv, which we now reference more explicitly in the Discussion\nsection: “A neural model of the MB and CX implementing this design produces remarkably robust\nnavigation in realistic environments69”\nMinor Concerns\nFigure 1: “Cumulative turn ratio” instead of “Cumulated turn ratio”?\nAnswer: Indeed, done\nMethods don’t contain a description of the turn ratio.\nAnswer: It is now explained in the figure legend. Turn ratio (integral of absolute angular velocity:\n(right - left) / (right + left);\nWhy was 12 seconds chosen for averaging?\nAnswer: This is explained in the method “The data shown in Figure 1 for each orientation is\naveraged across 12 seconds of recording (from 3 to 15 seconds post- ring lifting). We decided to let 3\nseconds after ring lifting, because the experimenter’s movements before leaving the area might\ndisturb the ants.”\nHow is goal direction experimentally determined? Is it the direction towards the nest? Is it the\ndirection tangential to the route? Are these the same for the chosen route? From previous work one\nwould imagine it’s tangential to the route, but the methods section should be more explicit about\nthis as well. Here it might be useful to see the shape of the route.\nAnswer: Figure 1a shows the relation between the trackball and the route. We also added a\ndescription of the route for both species used, in the method. “Nest to feeder distances were 10\nm (2 m wide corridor) in experiment 1 (Fig. 1), 4 m (1 m wide corridor for the straight part) in\nexperiment 2 (Fig.2), and 10 m (2 m wide corridor) in experiment 3 (Fig. 4).”\n33: Lent 2010, 2013 suggest ants can compute turn direction based on the presented view. This\nshould be cited and discussed.\nAnswer:\nIndeed, we now cite both references. In the introduction: “It should be noted that ants and flies\nsubjected to a sudden jump of the surrounding scene without other source of compass information\ncan correct their course accordingly12,51.”\nAnd later: “Perhaps it is possible to identify, for each individual, a precise orientation that triggers\npurely forward movement. However, we note that even when ants are free to recapitulate their\nfamiliar route on the ground, they continuously alternate between left and right turns rather than\nproceed straight47–51”\n48: The sinusoidal curve in Fig. 1c can be explained without the need for lateralized signals from the\nMB to the PFNs. The existence of some goal direction vector in the FB aligned with the route\ndirection should produce the curve shown.\nAnswer: Absolutely, and the subsequent mirror experiment (fig. 2) is in agreement with this. The\ncurrent experiment (fig. 1) shows that visual scene recognition can update/set this goal direction\nvector in the FB.\nIt would be useful to plot the turn ratio for each individual ant in the unfamiliar location to see if it\nhas a similar curve which is not centered at the route direction. In general, individual data points\nmight be more informative than averaged curves.\nAnswer: Agreed. We now show individual data in Extended data fig. 1.\nIt might also be useful to analyze behavior conditioned on the order in which the ant was exposed to\neach of the 8 directions. Does the turn size (mean angular velocity) change as the ant samples each\norientation? Is the change in turn size predicted based on which view the ant was previously exposed\nto?\nAnswer: We agree that this would be interesting to investigate, however it would require another\nexperimental design. Indeed, here the orientation sequence was pseudo-randomised so that no\ntwo ants underwent the same sequence among the 4030 (factor 8) possible ones (times 2 if we\ntake into account the actual direction of rotation, which was also randomised). Also, each\norientation*sequence_number was equally represented across the tested population. As a result\nof this design we have therefore very little data per possible transition, or orientation*sequence\nnumber, which makes it impossible to conduct meaningful statistics about such interactions; thus\npreventing to draw conclusions about the effects of the previously exposed orientation.\n141: Doesn’t it still requires two separate plasticity events at the same location when the ant is facing\nleft and when it is facing right of the route? How would this happen?\nAnswer: We assume this is achieved in different MB output neurons, so separate plasticity events\nin different location. We clarified the sentence: “Instead, both MBs likely process both left and right\nvisual memories in different MB output neurons with opposing valences, a characteristic feature of\nsuch neurons69.”\n184-185: Unclear if individual KCs pool over multiple VPNs and what the proportion of ipsilateral\ndominant vs contralateral dominant KCs is.\nAnswer: indeed, this quantification has not yet been achieved in these ant species.\n198: Motor feedback to the MB from the CX in general might not be sufficient, it should be specific to\nthe DANs. Is there evidence in the connectivity for this?\nAnswer: Indeed, such dopaminergic feedback from the LAL targeting specific compartment of the\nmushroom body exists. We now emphasize it: “Lateralised dopaminergic feedback from the Lateral\nAccessory Lobes – a pre-motor area receiving path integration outputs – to specific compartments of\nthe MBs could represent an ideal candidate to orchestrate such a categorisation of left/right\nmemories70. Indeed, the insect brain receive massive lateralised signal from motor feedback85,86,\nincluding such LAL to MB neurons (see for instance87).”\n49: orientations instead of orientation\n65: Cartwright and Collett (1983) suggest that landmark navigation might depend on the allocentric\nreference frame. This should be cited.\n82: remove extra \"ants\"\nAnswer: All done\nThank you very much for your time and helpful comments.\nDear editor,\nWe are grateful to the reviewers for their second round of feedbacks. We have incorporated all\ntheir suggestions into the revised manuscript and figures. Below our detail answers. With best\nwishes,\nAntoine\nReviewer #1 (Remarks to the Author):\nThank you for addressing the comments in our previous review, we are certainly satisfied with the\nchanges and clarifications provided and raise only a few minor comments:\nL45-48: These lines are a helpful addition but they need a little bit of clarification/elaboration in my\nview.\nIs the sentence beginning ‘however’, and in particular ‘short-term mapping’, referring to path\nintegration as calculated with terrestrial cues? I think it could help to relate this to what I think you\nmean by ‘path integration’ in the next sentence (i.e path\nintegration in general).\nWith respect to ‘different surroundings’, perhaps clarifying that you mean somewhere distinct from\ntheir collection point but still familiar e.g ‘distinct yet familiar surroundings’?\nResponse: thanks, it is now clarified (Line 43 – 49). Explanation about path integration is provided\nin the next paragraph (Line 53-56).\nTypos/ minor clarifications:\nL44: ‘without other source' -> without any other source/ without other sources/ without sources\nother than the terrestrial view (for more clarification).\nL596: The part ‘Turn ratio (integral of absolute angular velocity: (right - left) / (right + left)...’ looks a\nlittle like the formula relates to the integral rather than the turn ratio, perhaps clarification with e.g.\n‘(right - left) / (right + left), with right/left angles derived from the integral of absolute angular\nvelocity’\nL647: This should probably say ‘green and orange arrows’.\nL680, 689 & 691: check that colours in legend and figure are the same.\nL681: just a slight rewording to ‘yellow and orange marks, for which extent and thickness illustrate\nrespectively the agent’s recently sampled directions and familiarity signals received ‘\nResponse: all done\nSupplementary materials:\nFigure 1 is certainly a helpful addition, one minor point would be to consider the issue with the\nformula in line 7 (as discussed for the main text above).\nL113: should say c)\nResponse: All done\nThanks again for your feedbacks.\nReviewer #3 (Remarks to the Author):\nIn the revised version of the manuscript the authors have addressed all my previous concerns. I have\nonly three more minor points.\n1. As far as I understand, a coordinating role of the LAL in locking turning signals to specific phases of\nan ant's motor oscillations has also been proposed in a recent review paper (Collett, T., Graham, P.\nand Heinze, S. (2025). The neuroethology of ant navigation. Current Biology 35, R110–R124.), maybe\nworth including in line 241 in the discussion.\nResponse: absolutely, done.\n2. The dual role of delta7 cells, i.e. an ability to excite some neurons and inhibite other downstream\nneurons, was suggested via physiologically methods in Franconville, R., Beron, C. and Jayaraman, V.\n(2018). Building a functional connectome of the Drosophila central complex. eLife 7, e04577. This\npaper should be cited to justify the modeling choice shown in extended data figure 4.\nResponse: done. Indeed, it’s worth highlighting this remarkable property.\n3. The headings on top of the pages of the extended figures are out of sync with the figure legends\n(figure 3 is referred to as figure 2, and figure 4 is referred to as figure 3)\nResponse: corrected\nThanks again for your feedbacks.\nReviewer #4 (Remarks to the Author):\nThe authors have made changes that clarify some of the issues that we raised. These largely address\nthe main concerns, but some additional edits would also be helpful. Specific points below:\nIn their first answer, the authors suggest that the views experienced by the ant are stored in a\n“fundamentally egocentric way” — i.e. that a static view cannot be used by the ants to extract an\nappropriate turning direction. To convince us they cite 3, 19, 20, 21, all of which propose that the\nparsimonious explanation for ant behavior is a view-matching strategy in which the ant only has\naccess to a single familiarity value in each direction at one point along the route. They suggest that\nthis is why they need stored views in multiple directions at each point along the route and the reason\nwhy ants need to scan left or right to perform route navigation. This is consistent with the result that\nants carrying their food backwards cannot recognize a scene along a route they usually take in the\nforward direction. They also point out that the ants experience multiple views at each point along\nthe route providing lots of opportunities to store “left of goal” and “right of goal” views.\nThese are all fair points. However, the authors' argument still relies on parsimony and does not rule\nout the suggested alternative. In other words, IF the ant can compare two distinct familiarity values\nfrom the left and right MB for a single gaze direction to arrive at an instantaneous signed turning\nsignal, this would also be consistent with the experimental results cited. This is why we requested\nthat the authors show some of the scenes viewed by the ants at a few locations. If that is not\nfeasible, we would suggest that the authors include a single sentence, perhaps in General Discussion\nif not Results, stating that further experimentation is required to definitively rule out the possibility\nthat the comparison of an instantaneous view with a single stored snapshot at each point along the\nroute is used by insects to immediately estimate which direction to turn.\nResponse: Agreed, we added this in the discussion (L219 -223).\nFor Answer 2, the citation in the text needs to be to the authors' bioRxiv manuscript, not Aso et al.,\n2014 (which is what reference 69 seems to point to).\nResponse: Indeed. Both citations are now present and the sentence clarified (L 166)\nFor Answer 8, the authors agree that the sinusoidal curve in Fig 1c can be explained without the need\nfor lateralized signals from the MB to the PFNs. They should update the text to include that\npossibility.\nResponse: Indeed. The result of Fig.1c are interpreted purely behaviourally, no mention of neural\ncircuit here (L50-72). When later referring to neural circuits, the uncertainty regarding the origin of\nthis signal is clearly and systematically conveyed (e.g. L14 ‘assumed to be encoded in the\nMushroom Bodies 3,7–9’ ; L 116 ‘presumably computed in the MB’ - L127 ‘ presumably\ncomputed in the MB’)\nFor Answer 9, Extended data fig 1 only shows data for a familiar location, but it would be useful to\nalso see it for an unfamiliar location, as we suggested.\nResponse: Done. We added these detailed figure to extended data fig. 1\nWith the few changes above, the manuscript would be appropriate for publication without further\nreview.\nThanks again for your feedbacks.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts."}]

Decision letter