Antoine Wystrach
email: antoine.wystrach@univ-tlse3.fr

Neurons from pre-motor areas to the Mushroom bodies can orchestrate latent visual learning in navigating insects

Keywords: insects, navigation, vision, latent learning, homing, memory, mushroom bodies, central complex, lateral accessory lobes, neural modelling Path integration, MB: Mushroom Body, CX: Central complex, LAL: Lateral accessory lobes, MBON: Mushroom body output neuron. DAN: Dopaminergic neuron

Spatial learning is peculiar. It can occur continuously and stimuli of the world need to be encoded according to some spatial organisation. Recent evidence showed that insects categorise visual memories as whether their gaze is facing left vs. right from their goal, but how such categorisation is achieved during learning remains unknown. Here we analysed the movements of ants exploring the world around their nest, and used a biologically constrained neural model to show that such parallel, lateralized visual memories can be acquired straightforwardly and continuously as the agent explore the world. During learning, 'left' and 'right' visual memories can be formed in different neural comportments (of the mushroom bodies lobes) through existing lateralised dopaminergic neural feedback from pre-motor areas (the lateral accessory lobes) receiving output from path integration (in the central complex). As a result, path integration organises visual learning 'internally', without the need to be expressed through behaviour; and therefore, views can be learnt continuously (without suffering memory overload) while the insect is free to explore the world randomly or using any other navigational mechanism. After learning, this circuit produces robust homing performance in a 3D reconstructed natural habitat despite a noisy visual recognition performance. Overall this illustrates how continuous bidirectional relationships between pre-motor areas and visual memory centres can orchestrate latent spatial learning and produce efficient navigation behaviour.

MAIN

Insect navigators such as ants, bees and wasps rapidly learn the visual surroundings to navigate efficiently to places of interest such as the nest or food sources 1 . These long-term visual memories are formed in a brain area called the Mushroom Bodies (MBs) [START_REF] Buehlmann | Mushroom bodies are required for learned visual navigation, but not for innate visual behavior, in ants[END_REF][START_REF] Kamhi | Vertical lobes of the mushroom bodies are essential for view-based navigation in Australian Myrmecia ants[END_REF] . The neural circuitry of the MBs is ideally suited to encode, store and compare arbitrary input (visual, olfactory, or other) in a way that enables a visually navigating insect to learn and then assess whether the visual scene currently perceived is familiar or not [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF][START_REF] Webb | Neural mechanisms of insect navigation[END_REF] . After learning, the MBs output 'familiarity signals' that can be used for guidance along familiar routes or back to place of interest [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF][START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF][START_REF] Sun | A decentralised neural model explaining optimal integration of navigational strategies in insects[END_REF] . However, how learning is orchestrated in the first place is unclear.

As during experimental conditioning, experiencing an event bearing an innate positive or negative valence (so called the US in learning theory) can trigger the learning of the surrounding visual scenery (which can be viewed as the CS). For instance, experiencing sucrose at a feeder location will trigger visual learning events that are useful to return to this rewarding place [START_REF] Bolek | What counts for ants? How return behaviour and food search of Cataglyphis ants are modified by variations in food quantity and experience[END_REF][START_REF] Nicholson | Learning walks and landmark guidance in wood ants (Formica rufa)[END_REF][START_REF] Wei | Deciding to learn: modulation of learning flights in honeybees, Apis mellifera[END_REF] .

Inversely, ants experiencing a negative event such as falling into a pit-trap will memorise the visual scenes experienced just before falling as aversive; and hence avoid this region of the world in the subsequent trips [START_REF] Freas | Aversive view memories and risk perception in navigating ants[END_REF][START_REF] Wystrach | Rapid Aversive and Memory Trace Learning during Route Navigation in Desert Ants[END_REF] . These examples involve a reinforcer (reward or punishment) and thus fall under the umbrella of 'reinforcement learning'. However, spatial learning also occurs in the absence of distinctive reward or punishment, for instance, when exploring the world. Indeed, navigating insects tend to learn continuously: weather along routes, around their nest (during so-called learning walks or learning flight) but also when at novel albeit quite neutral locations [START_REF] Kohler | Idiosyncratic route-based memories in desert ants, Melophorus bagoti: How do they interact with path-integration vectors?[END_REF][START_REF] Mangan | Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox)[END_REF][START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF] . This tendency to learn continuously when exploring the world is shared with other navigating animals too, and has been dubbed 'latent learning' in opposition to 'reinforcement learning' in learning theory [START_REF] Thistlethwaite | A critical review of latent learning and related experiments[END_REF] Whether 'latent' or 'reinforcement-based', spatial learning implies that the stimuli of the world are encoded according to some spatial organisation. For navigating ants, it has been suggested that learning of the visual surroundings may happen only when the ant is facing specific directions of interests, such as when its gaze is oriented towards the goal [START_REF] Dewar | What is the relationship between visual environment and the form of ant learning-walks? An in silico investigation of insect navigation[END_REF][START_REF] Graham | Animal cognition: multi-modal interactions in ant learning[END_REF][START_REF] Müller | Path Integration Provides a Scaffold for Landmark Learning in Desert Ants[END_REF][START_REF] Wystrach | Snapshots in ants? New interpretations of paradigmatic experiments[END_REF][START_REF] Zeil | The learning walks of ants (Hymenoptera: Formicidae)[END_REF] the anti-goal [START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF][START_REF] Jayatilaka | The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi[END_REF][START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF] or along their route direction [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF][START_REF] Sun | A decentralised neural model explaining optimal integration of navigational strategies in insects[END_REF][START_REF] Baddeley | A model of ant route navigation driven by scene familiarity[END_REF][START_REF] Goulard | A unified mechanism for innate and learned visual landmark guidance in the insect central complex[END_REF][START_REF] Wystrach | Geometry, features, and panoramic views: Ants in rectangular arenas[END_REF] . At the naïve stage, this directional information can be provided by path integration (PI). PI continuously provides the insect's current position relative to its goal, whether the nest or a food source [START_REF] Collett | Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms[END_REF] . It has thus been suggested that path integration is used to both enable the physical alignment of the insect's body and gaze towards its goal (or anti-goal) and trigger a visual learning event at such appropriate times [START_REF] Müller | Path Integration Provides a Scaffold for Landmark Learning in Desert Ants[END_REF][START_REF] Collett | Navigational memories in ants and bees: Memory retrieval when selecting and following routes[END_REF] .

PI is computed in a brain area called the Central complex (CX), the seat of the insect representation of directions [START_REF] Moël | The Central Complex as a Potential Substrate for Vector Based Navigation[END_REF][START_REF] Pfeiffer | Organization and Functional Roles of the Central Complex in the Insect Brain[END_REF][START_REF] Seelig | Neural dynamics for landmark orientation and angular path integration[END_REF][START_REF] Stone | An Anatomically Constrained Model for Path Integration in the Bee Brain[END_REF] . However, how the right information from the path integrator is mediated to the MBs for orchestrating visual learning remains entirely unknown [START_REF] Collett | How does the insect central complex use mushroom body output for steering?[END_REF] .

Recently, it was shown that ants (and likely wasps [START_REF] Stürzl | How Wasps Acquire and Use Views for Homing[END_REF] ) may not learn views specifically when facing the goal or anti-goal direction, but when facing left and right from their goal [START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF] . Left-tothe-goal and right-to-the-goal memories explains why ants can recognise egocentric views when misaligned with their goal and trigger the appropriate turning commands [START_REF] Stürzl | How Wasps Acquire and Use Views for Homing[END_REF][START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF][START_REF] Differt | A generalized multi-snapshot model for 3D homing and route following[END_REF] , but summon an explanation for how left/right categorisation of visual memories is achieved neurally during learning.

Here we show how such categorisation can be achieved neurally and continuously, providing a mechanistic explanation for spatial, latent learning. We analysed the movements of ants displaying learning walks around their nest, and used computational modelling to show that existing neural feedback from pre-motor area (the lateral accessory lobes, LAL) receiving the path integration output from the CX, can organise the formation of these lateralized visual memories in the MBs. Our biologically constrained architecture shows that learning events can then be achieved randomly or continuously; literally sparing both the need to 'control the timing of learning' as well as the need to align the agent's body in any particular direction.

Remarkably, the MBs can support continuous learning of thousands of views without suffering memory overload, because only novel information recruits new synapses. After learning, the architecture can produce remarkably robust homing performance in reconstruction of complex natural habitats [START_REF] Stürzl | Three-dimensional models of natural environments and the mapping of navigational information[END_REF] , as observed in homing ants [START_REF] Collett | Novel landmark-guided routes in ants[END_REF][START_REF] Fleischmann | Early foraging life: spatial and temporal aspects of landmark learning in the ant Cataglyphis noda[END_REF][START_REF] Narendra | Mapping the navigational knowledge of individually foraging ants, Myrmecia croslandi[END_REF][START_REF] Wystrach | Ants might use different view-matching strategies on and off the route[END_REF] .

Ants look in all directions during learning walks.

During learning walks, naïve ants display meandering trajectories around their nest, often exploring different directions multiple times before venturing further [START_REF] Zeil | The learning walks of ants (Hymenoptera: Formicidae)[END_REF] . Artificially restraining these exploratory movements (in both time and space) reduces the ants' subsequent navigational performance based on terrestrial cues, showing that they do learn the scenery during these exploratory behaviours [START_REF] Fleischmann | Early foraging life: spatial and temporal aspects of landmark learning in the ant Cataglyphis noda[END_REF][START_REF] Wystrach | Ants might use different view-matching strategies on and off the route[END_REF] . At a finer scale, these meandering trajectories are interspaced with regular slowing down up to complete halts, producing behaviours so-called votes or scans, whose expression varies across species and individuals [START_REF] Deeti | Scanning behaviour in ants: an interplay between random-rate processes and oscillators[END_REF][START_REF] Fleischmann | Species-specific differences in the fine structure of learning walk elements in Cataglyphis ants[END_REF] . Slowing down and pausing helps the ants obtain a stable (and thus non-blurry) view and therefore surely contributes to visual learning. This is corroborated by the fact that these pauses are typically displayed in situation when learning is needed [START_REF] Nicholson | Learning walks and landmark guidance in wood ants (Formica rufa)[END_REF][START_REF] Freas | Aversive view memories and risk perception in navigating ants[END_REF][START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF][START_REF] Müller | Path Integration Provides a Scaffold for Landmark Learning in Desert Ants[END_REF][START_REF] Collett | A desert ant's memory of recent visual experience and the control of route guidance[END_REF][START_REF] Fleischmann | Magnetosensation during re-learning walks in desert ants (Cataglyphis nodus)[END_REF][START_REF] Graham | Bi-directional route learning in wood ants[END_REF][START_REF] Schwarz | Route-following ants respond to alterations of the view sequence[END_REF][START_REF] Wystrach | Views, landmarks, and routes: how do desert ants negotiate an obstacle course?[END_REF] . It was shown in some species that ants tend to display longer pauses when their gaze is aligned towards the nest [START_REF] Müller | Path Integration Provides a Scaffold for Landmark Learning in Desert Ants[END_REF][START_REF] Fleischmann | Species-specific differences in the fine structure of learning walk elements in Cataglyphis ants[END_REF] or anti-nest [START_REF] Jayatilaka | The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi[END_REF] directions.

Here, by analysing the learning walks recorded at high frame rate of two species of ants (Myrmecia croslandi and Melophorus bagoti), we found no systematic association between such pauses and some particular nest-centred or allocentric directions (Fig. 1c, Extended data Fig. 3). In contrasts, ants (pausing or not) exposed their gaze to a large, homogenous diversity of directions and locations around their nest (Fig. 1c, Extended data 3). While we acknowledge that ants may sometimes pause for a longer period of time while looking in the direction of their goal, what is clear is that learning walks are rather optimised to collect a diverse sample of views in all directions. This is in line with previous works showing that pauses and scans are not tightly controlled by the relation with the ant and its environment, but rather are the result of 'blind' internal motor processes such as the continuous production of regular oscillations in the ant's angular and forward speed [START_REF] Jayatilaka | The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi[END_REF][START_REF] Clement | An intrinsic oscillator underlies visual navigation in ants[END_REF] as well as the random triggering of pauses [START_REF] Deeti | Scanning behaviour in ants: an interplay between random-rate processes and oscillators[END_REF] . This stochasticity is further highlighted by the great variability in the expression of learning walks observed across individuals [START_REF] Haalck | CATER: Combined Animal Tracking and Environment Reconstruction[END_REF] .

Visual learning in the MB lobes can be organised by lateralized dopaminergic feedback from pre-motor areas.

Previous works have suggested that ants and wasps categorise views as whether their gaze is oriented towards the left vs. right in relation to the nest heading direction [START_REF] Stürzl | How Wasps Acquire and Use Views for Homing[END_REF][START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF] ; but if ants look in all directions during their learning walks, how do they achieve such a left vs. right categorisation?

We realised that due to the Path integrationthe ability to integrate compass and distance information to keep track of the nest relative position [START_REF] Müller | Path integration in desert ants, Cataglyphis fortis[END_REF] the output of the Central complex (CX) to the Lateral Accessory Lobe (LAL) provides the desirable information: the left (or right) LAL's hemisphere activity correlates with moments when the nest relative position is on the left (or right) of the ant current heading direction [START_REF] Stone | An Anatomically Constrained Model for Path Integration in the Bee Brain[END_REF][START_REF] Honkanen | The insect central complex and the neural basis of navigational strategies[END_REF] . The LAL are pre-motor areas sending steering commands to neurons descending to the thorax [START_REF] Clement | An intrinsic oscillator underlies visual navigation in ants[END_REF][START_REF] Steinbeck | Connecting brain to behaviour: a role for general purpose steering circuits in insect orientation[END_REF] . When PI is controlling guidance for homing, these lateralized output signals are used to trigger 'turn left' and 'turn right' compensatory motor commands to align the insect's body towards its nest, hence resulting in homing behaviour. However, when PI is not used to home -such as when ant display a learning walk -we reason that the LAL's output could nonetheless be used, not for steering, but to control 'internally' whether the current view should be categorised as left or right from the goal during learning (Fig. 1a).

Interestingly, the insect brain possesses the perfect neural candidate to do so: direct dopaminergic projections from the LALs to the MBs lobes [START_REF] Wendt | Immunocytochemistry of dopamine in the brain of the locust Schistocerca gregaria[END_REF] (Fig. 1a), that is, where long-term visual memories are formed due to dopamine release [START_REF] Aso | The neuronal architecture of the mushroom body provides a logic for associative learning[END_REF][START_REF] Heisenberg | Mushroom body memoir: from maps to models[END_REF] . The left and right dopaminergic feedback from the LAL -which could thus indicate in real time whether the nest is left or right of the current headingproject to different compartments of the MBs lobes (see Fig. 4D of [START_REF] Wendt | Immunocytochemistry of dopamine in the brain of the locust Schistocerca gregaria[END_REF] ); so that 'left-to-the-goal' and 'right-to-the-goal' memories could be formed in the input synapses of different MB output neurons (MBONs), literally updating separated memory banks as the individual explores the scene: the 'left-to-the-goal' memory bank is updated when the nest direction is on the left side of the insects current facing direction, and vice versa. Note that other neural candidates could equally achieve the desired LAL-to-MBs learning signals, albeit indirectly. For instance, some feedback from pre-motor areas modulate dopaminergic neurons that in turn, trigger synaptic modulation in the MBs lobes [START_REF] Cohn | Coordinated and Compartmentalized Neuromodulation Shapes Sensory Processing in Drosophila[END_REF][START_REF] Marquis | Locomotor and olfactory responses in dopamine neurons of the Drosophila superior-lateral brain[END_REF] .

Homing can be achieved through opponent lateralized signals from the MBs to the CX.

Once the views are memorised and categorised as left vs right, subsequent homingbased on these learnt viewsrequires to convey the familiarity signals from the MB lobes to the LAL for steering. We have strong behavioural and neurobiological evidence to constrain our explanation of how this might happen.

1-Connectomic [START_REF] Honkanen | The insect central complex and the neural basis of navigational strategies[END_REF] and experimentation in ants [START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF] shows that navigation based on learnt views is achieved indirectly, by updating a goal heading compass direction, likely in the fan-shaped body of the CX, which in turns control steering in the LAL. Interestingly, this seems to work only if the familiarity signals sent to the CX are decorrelated between the left and right hemispheres, with the signal from the left (and right) hemisphere indicating when the animal current heading is biased towards the left (or right) compared to the goal direction [START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF] .

2-Connectomic suggests that the MBs lobes send the familiarity signals to the CX either directly [START_REF] Scaplen | Transsynaptic mapping of Drosophila mushroom body output neurons[END_REF] , or through one relay in the dorsal brain areas (such as the SIP) [START_REF] Honkanen | The insect central complex and the neural basis of navigational strategies[END_REF] , where most MBONs converge ipsilaterally ( 59 . Such a relay notably enables the integration of antagonist MBON (and other) signals conveying opposite valences through simple inhibition ([good -bad], or [badgood]) [START_REF] Eschbach | Circuits for integrating learned and innate valences in the insect brain[END_REF] . This produces an opponent-like process which improves the estimation of the valence of the current situation and appears to be at play during visual navigation in ants [START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF][START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF] .

3-Some dopaminergic neurons from the left and right LALwhich are thought here to organise learning at the first place (see previous section)both project bilaterally in the MB lobes in a symmetrical manner (see Fig. We realised here that these three points naturally converged into one picture (Fig. 1a,b), which produces a set of predictions:

1-In each hemisphere, 'left and right memories' are formed in MBs compartment conveying opposite valence, in a symmetrical manner (Fig. 1a 'DAN', point 3 above).

A robust circuit for navigation in noisy visual environments.

To proof-test the validity of this circuit as a whole, we modelled both the MBs and the CX based on connectomic data as achieved before (for MB: [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF] , for CX: [START_REF] Stone | An Anatomically Constrained Model for Path Integration in the Bee Brain[END_REF] ), and coupled these two brain regions using the mentioned LAL-to-MBs connections for learning views, and MB-SIP-CX connections for using views (Fig. 1, Extended data Fig. 1). This circuit was implemented into an agent with ant-eye-like resolution (10°/pixel, Fig. 1.h), immersed in a realistic VR reconstruction of Myrmecia ants' natural habitat [START_REF] Stürzl | Three-dimensional models of natural environments and the mapping of navigational information[END_REF] (Fig. 1g).

For learning, we let the agent reproduce recorded natural learning walks of a Myrmecia ant (Fig. 1c, Extended data Fig. 2; data provided by Jochen Zeil), with the outputs of its path integrator (computed in the CX) to the left and right LALs (Fig. 1c, yellow and orange) driving the dopaminergic feedbacks that control learning. Learning in the MB is achieved following what is observed in insects: by depressing the currently active KCs' output synapses onto the MBONs, only in the MBs compartments where the associated dopaminergic feedback is concomitantly active [START_REF] Aso | The neuronal architecture of the mushroom body provides a logic for associative learning[END_REF][START_REF] Cognigni | Do the right thing: neural network mechanisms of memory formation, expression and update in Drosophila[END_REF] . After completion of the learning walk, we transferred the agent deprived of PI (i.e. as a so called Zero-Vector agent) to a new location in the world and let the visual familiarity based on the left and right memoriesas outputted by the MBONsdrive the two FB inputs to update the desired heading direction in the CX. The CX, in turn, outputs left and right turning commands to the LAL, which drives the agent (Fig. 1,b, Extended data Fig. 1). This produces a remarkably robust homing ability, as well as a tight search around the nest without the need to fine tune parameters (Fig. 1f, Extended data Fig. 2), providing further credibility to this circuit.

When to learn? No need to bother.

Interestingly, our model shows that the timing and positions at which the view is sampled along learning walks are not important. The model can afford to learn sporadically, at random position or continuously (it is operational whether it uses 90 views or 30,000 views for learning) (see Extended data Fig. 2), bypassing the need for additional mechanisms that controls 'when to learn' or 'how to align the body to learn'. Memory load is not a problem either: even when learning continuously (i.e., here at 100fps), 20,000 KCs proved largely sufficient to store the information from the large amount of views (e.g., 13638 views) experienced along a learning walks sampled at high frame rate (100 fps). Saturation is prevented because additional memory space is used only when significantly novel views are perceived: already learnt views activate KCs that have already switched their synaptic output, and thus yield no further change.

Acquiring visual memories in this way results in a steep learning curve at first, but then spontaneously plateaus as the explored regions around the nest become familiar (Fig. 1e). In addition, our model vastly underestimates the ants' (and hymenopteran navigators in general) memory capacity (>100,000 KCs [START_REF] Ehmer | Mushroom body volumes and visual interneurons in ants: Comparison between sexes and castes[END_REF][START_REF] Habenstein | The brain of Cataglyphis ants: neuronal organization and visual projections[END_REF] and >100 MBONs [START_REF] Rybak | Anatomy of the mushroom bodies in the honey bee brain: The neuronal connections of the alpha-lobe[END_REF] ), ignores pruning principles that can increase effective memory capacity, bypasses any visual pre-processing that can compress information, and reduces the complexity of synaptic connections to simple binary connections between neurons. Hence, we are confident that learning views around the nest in such a way should occupy only a fraction of the real ants' memory capacity.

General discussion

We have shown how existing neural connections between the CX (computing path integration), the LAL (a pre-motor area) and the MB (seat of the visual memories), could enable an insect to orchestrate spatial learning. An agent equipped with this circuit can quickly learn the visual surrounding and subsequently home from novel locations using very low-resolution views (10°/pixel, Fig. 1h) of its natural environment (Fig. 1). Several differences contrast this circuit from previous accounts.

First, views are encoded egocentrically (i.e. recognition is view-point dependant), which is in adequation with most of the literature [START_REF] Baddeley | A model of ant route navigation driven by scene familiarity[END_REF][START_REF] Baddeley | Models of visually guided routes in ants: Embodiment simplifies route acquisition[END_REF][START_REF] Schwarz | How Ants Use Vision When Homing Backward[END_REF][START_REF] Wehner | Visual navigation in insects: Coupling of egocentric and geocentric information[END_REF][START_REF] Wystrach | Embodiment, movements and the emergence of decisions. Insights from insect navigation[END_REF][START_REF] Zeil | Visual homing: an insect perspective[END_REF] and contrast with recent models who assumed that visual memories can be rotationally invariant (recognition is independent of the gaze orientation) [START_REF] Sun | A decentralised neural model explaining optimal integration of navigational strategies in insects[END_REF][START_REF] Stone | Rotation invariant visual processing for spatial memory in insects[END_REF] . The latter requires computational steps, such as Fourier transforms, which potential implementation in insect circuits remains unknown. Instead, assuming an egocentric encoding enables to remain faithful to the known insect neural circuits and corroborates the behavioural evidence that ants' visual scene recognition requires the insect to align its gaze as during training [START_REF] Schwarz | How Ants Use Vision When Homing Backward[END_REF][START_REF] Collett | Insect navigation: what backward walking reveals about the control of movement[END_REF][START_REF] Pfeffer | How to find home backwards? Navigation during rearward homing of Cataglyphis fortis desert ants[END_REF][START_REF] Schwarz | How do backward walking ants ( Cataglyphis velox ) cope with navigational uncertainty?[END_REF] as well as the regular need of insects to scan multiple directions [START_REF] Zeil | The learning walks of ants (Hymenoptera: Formicidae)[END_REF][START_REF] Deeti | Scanning behaviour in ants: an interplay between random-rate processes and oscillators[END_REF][START_REF] Clement | An intrinsic oscillator underlies visual navigation in ants[END_REF][START_REF] Baird | The dung beetle dance: an orientation behaviour?[END_REF][START_REF] Zeil | Structure and function of learning flights in bees and wasps[END_REF] .

Second, our circuit is built on the evidence that memorised views are categorised as oriented left vs. right rather than towards or away from the goal [START_REF] Stürzl | How Wasps Acquire and Use Views for Homing[END_REF][START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF] . This was key, as this left/right information can be directly provided by the LAL during learning, due to the mechanism of path integration in the CX.

Third, it was previously assumed that path integration serves as a scaffold for visual learning through behaviour: the insect would use PI to physically align its body in a direction of interest (towards or away from the nest, or along a route) and then memorise the view perceived at this precise moment [START_REF] Müller | Path Integration Provides a Scaffold for Landmark Learning in Desert Ants[END_REF][START_REF] Wystrach | Snapshots in ants? New interpretations of paradigmatic experiments[END_REF][START_REF] Fleischmann | Species-specific differences in the fine structure of learning walk elements in Cataglyphis ants[END_REF] . Here, PI still serves as a scaffold for visual learning, but does so 'internally' without the need to express through behaviour. As a consequence, views can be learnt continuously while the insect freely explores the world using other mechanisms. With this in mind, ants learning walks [START_REF] Zeil | The learning walks of ants (Hymenoptera: Formicidae)[END_REF] and early meandering trajectories [START_REF] Haalck | CATER: Combined Animal Tracking and Environment Reconstruction[END_REF][START_REF] Muser | Foraging ecology of the thermophilic Australian desert ant, Melophorus bagoti[END_REF][START_REF] Wehner | The ontogeny of foraging behaviour in desert ants, Cataglyphis bicolor[END_REF] appear optimised to sample views quickly in as many locations and orientations around the nest as possible (Fig1 c,d; Extended data Fig. 3). Also, since all views becomes useful whatever their orientation, it explains how views acquired during outbound trips can be equally categorised effectively to serve subsequent homing [START_REF] Graham | Bi-directional route learning in wood ants[END_REF][START_REF] Schwarz | How do backward walking ants ( Cataglyphis velox ) cope with navigational uncertainty?[END_REF][START_REF] Freas | Terrestrial cue learning and retention during the outbound and inbound foraging trip in the desert ant, Cataglyphis velox[END_REF] , as well as the ants' ability to recognise views whatever their body orientation when on highly familiar regions [START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF] .

Fourth, navigation robustness arises here from multiple reasons. As shown before, visual recognition in the MBs is intrinsically noisy, partly because of the way memories are compressed in the KCs, and partly because of the proximal clutter (blade of grasses, etc…) that an insect may encounter when walking on the floor or flying through bushes [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF][START_REF] Webb | Neural mechanisms of insect navigation[END_REF] . Part of this noise is alleviated by having the noisy MBs familiarity signal being send to the CX (as in [START_REF] Sun | A decentralised neural model explaining optimal integration of navigational strategies in insects[END_REF][START_REF] Goulard | A unified mechanism for innate and learned visual landmark guidance in the insect central complex[END_REF][START_REF] Collett | How does the insect central complex use mushroom body output for steering?[END_REF][START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF] , which acts as a directional buffer due to its much more stable heading encoding based on multiple sources of compass [START_REF] Pfeiffer | Organization and Functional Roles of the Central Complex in the Insect Brain[END_REF] . The current circuit provides an additional level of robustness, through redundancy, as each view is simultaneously compared to four memory banks in parallel (Fig. 1b,'MBONs'). Although the input from each eye is sent to both left and right hemispheric MBs [START_REF] Habenstein | The brain of Cataglyphis ants: neuronal organization and visual projections[END_REF] , the random pattern of connectivity in the KCs input [START_REF] Caron | Random convergence of olfactory inputs in the Drosophila mushroom body[END_REF][START_REF] Litwin-Kumar | Optimal degrees of synaptic connectivity[END_REF][START_REF] Szyszka | Sparsening and temporal sharpening of olfactory representations in the honeybee mushroom bodies[END_REF] makes each MBs different.

As a result, this architecture literally provides four independent assessments of whether the insect's current heading is too much on the left vs. the right of its goal direction. Additional assessments, for instance based on recent vs. longer-term memories, may likely recruit more MBs compartments and make the recognition process more redundant.

Finally, the dopaminergic internal feedback from the LALnecessary to categorise learning into different MB lobes compartmentsalso predicts a strong link between locomotion and activity in the MB, with rapid shifts of dopaminergic activity across the MB lobes as the insects is moving in the world. This corroborates recent observations in drosophila that dopamine release in the brain strongly correlates with the animals' movements [START_REF] Marquis | Locomotor and olfactory responses in dopamine neurons of the Drosophila superior-lateral brain[END_REF][START_REF] Aimon | Fast near-whole-brain imaging in adult Drosophila during responses to stimuli and behavior[END_REF] , and indeed, shifts dynamically across the MBs lobes [START_REF] Cohn | Coordinated and Compartmentalized Neuromodulation Shapes Sensory Processing in Drosophila[END_REF][START_REF] Siju | Valence and State-Dependent Population Coding in Dopaminergic Neurons in the Fly Mushroom Body[END_REF] . Overall, this supports the view that the MBs provide an active coding that operates in a tight closed-loop with the ongoing behaviour.

Which actual neurons convey the desired PI information from the LAL to the MBs to categorise visual learning in ants, wasps or bees remains to be identified, but given the existence of such direct (or indirect) connections in various insects that are not central place foragers [START_REF] Wendt | Immunocytochemistry of dopamine in the brain of the locust Schistocerca gregaria[END_REF] , we can easily envision how the ability to perform path integration may have enabled the evolution of the ability to home using learnt views.

MATERIALS AND METHODS

Neural model

We used a simple agent-based model in a closed loop in a 3D virtual environment. All simulations were performed in Python 3.x. [START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF] . The neural model connectivity is described in Extended data Fig. 1, and code is available on request.

Parameters description

Motor noise: at each time step, a directional 'noise angle' is drawn randomly from a Gaussian distribution of ± SD = motor noise, and added to the agent's current direction. This corresponds to the speed at which the memory of the vector representation in the FBN decays. A memory decay = 1 means that the vector representation in the FBN is used only for the current time step and entirely overridden by the next inputs. A memory decay = 0 means that the vector representation acts as a perfect accumulator across the whole paths (as for Path Integration), which is probably unrealistic.

Memory decay: proportion of

Motor gain:

Sets the gain to convert the motor neuron signals (see extended Fig. 1 for details) into an actual turn amplitude (turn amplitude = turning neuron signal × gain). Note that here, the motor gain is presented across orders of magnitude. One order of magnitude higher means that the agent will be one order of magnitude more sensitive to the turning signal.

3D world and view rendering

The virtual environment used in our model was generated by the software Habitat3D [START_REF] Risse | Software to convert terrestrial LiDAR scans of natural environments into photorealistic meshes[END_REF] , an open-source tool which provides photorealistic meshes of natural scenes from point clouds acquired with help of a LiDAR scanner (IMAGER 5006i). This environment is mapped on the habitat of Myrmecia ants from Canberra, Australia [START_REF] Stürzl | Three-dimensional models of natural environments and the mapping of navigational information[END_REF] . The mesh spans a diameter of around 65 metres and features large eucalyptus trees and the distant panorama cues (Fig. 1g). This dataset can be found on the Insect Vision webpage (https://insectvision.dlr.de/3d-reconstructiontools/habitat3d). For speed optimization purposes, we down-sampled the originally highresolution mesh with the open-source software Blender into a lower number of vertices; the rendering was then realized in OpenGL, with the Python libraries Plyfile and PyOpenGL. This 3D model enabled us to render panoramic views from any location as a 360-degree picture. We chose input only the blue channel of the RGB scene, resulting in only one luminance value per pixel. Also, the skybox was a pure blue uniform colour. That way, as with UV in natural scenes [START_REF] Differt | Insect models of illumination-invariant skyline extraction from UV and green channels[END_REF][START_REF] Stone | Sky segmentation with ultraviolet images can be used for navigation[END_REF] , blue provides a strong contrast between the sky and the terrestrial scene. This approximates the type of visual information used by navigating ants [START_REF] Schultheiss | Crucial role of ultraviolet light for desert ants in determining direction from the terrestrial panorama[END_REF][START_REF] Graham | Ants use the panoramic skyline as a visual cue during navigation[END_REF] . Views were cropped vertically so that the bottom, floor-facing part was discarded. Finally, views were downsampled at 10°/pixel (see Fig. 1h), and we extracted the edges by subtracting for each pixel the summed value of its 8 neighbours, mimicking lateral inhibition across ommatidia. As a result, the visual information that the model receives is a small rectangular matrix of single-channel, floating point values representing the above-horizon panorama (see Fig. 1h).

Learning walks trajectories analysis

We analysed learning walks trajectories of Myrmecia croslandi ants recorded at 100fps (courtesy of Jochen Zeil, from a data set used in [START_REF] Jayatilaka | The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi[END_REF] ) and Melophorus bagoti ants recorded at 300fps (from a data set used in [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF] ). Trajectories were analysed using Matlab (R2016b Matworks). Code and data are available on request. b. Each view cell projects (via Projection Neuron, PN) to both hemispheres' Mushroom Bodies (MB), where it makes pseudo-random connections with KCs: we set each KC to connect to 4 randomly chosen PNs, roughly matching what is observed in insects. We chose 20,000 KCs per hemisphere, which underestimates the number of KCs in ants (> 100,000). At each time step, the 0.1% KCs with the strongest input (i.e., the sum of the 4 PNs activities connecting to the KC, which can be seen as the KC's dendritic excitatory postsynaptic potential) activity would be set to 1 (reflecting one action potential), the other KCs would be set to 0. This represents the effect of the inhibitory activity of APL-like-neurons (black neuron) across all KCs, ensuring that only a few KCs (the ones with strongest input activity) can fire an action potential at a time, as observed [START_REF] Szyszka | Sparsening and temporal sharpening of olfactory representations in the honeybee mushroom bodies[END_REF][START_REF] Cassenaer | Conditional modulation of spike-timing-dependent plasticity for olfactory learning[END_REF][START_REF] Honegger | Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body[END_REF] .

ACKNOLEDGMENTS

c. We modelled (in each hemisphere) two compartments of the MBS lobes (surrounded by black ovals): both compartments are composed of 1 dopaminergic neuron (DAN) associated to 1 MBS output neuron (MBONs), mediating opposite valences as observed across insects [START_REF] Aso | Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila[END_REF][START_REF] Schleyer | Associative learning of stimuli paired and unpaired with reinforcement: Evaluating evidence from maggots, flies, bees and rats[END_REF] .

These antagonistic DANs engage in a winner-take-all competition (symbolised by the black reciprocal inhibition) so that only one kind is active at a time in each hemisphere, as observed in insects [START_REF] Cohn | Coordinated and Compartmentalized Neuromodulation Shapes Sensory Processing in Drosophila[END_REF] . Initially, all KCs connect to both MBONs with a synaptic weight of 1. At each time step, synaptic depression happens for the active DAN's compartment mimicking coincidence detection [START_REF] Aso | The neuronal architecture of the mushroom body provides a logic for associative learning[END_REF] : the KC-to-MBON weights of each currently active KCs is set to 0, and will stay so permanently (we did not wish to model forgetting). Due to the activity of the CX (see (e)), the DANs activity correlates with moments when the nest is left (orange DAN)

or right (yellow DAN) relative to the current body orientation.

d. Current compass direction is modelled in the protocerebral bridge (PB) as a bump of activity across 8 neurons forming a ring-attractor, as observed in insects [START_REF] Honkanen | The insect central complex and the neural basis of navigational strategies[END_REF] . Each neuron responds maximally for a preferred compass direction, 45° apart from the neighbouring neurons (neuron 1 and 8 are neighbours). Change in the agent's current compass orientation results in a shift of the bump of activity across the 8 neurons (we did not model how this is achieved from sensory compass cues (see [START_REF] Pfeiffer | Organization and Functional Roles of the Central Complex in the Insect Brain[END_REF][START_REF] El Jundi | Neural coding underlying the cue preference for celestial orientation[END_REF][START_REF] Green | Building a heading signal from anatomically defined neuron types in the Drosophila central complex[END_REF][START_REF] Kim | Generation of stable heading representations in diverse visual scenes[END_REF] for studies dedicated to this matter).

e. During learning, two representations of the Path Integration (PI) home vector are updated in the Fan-shaped Body Neurons (FBN) by integrating current speed and compass heading information (as in [START_REF] Moël | The Central Complex as a Potential Substrate for Vector Based Navigation[END_REF][START_REF] Stone | Rotation invariant visual processing for spatial memory in insects[END_REF] ). Speed input activates all 8 FBN neurons equally, but simultaneous inhibition from the PB (see d) results in a negative imprinting of the current bump of activity (inhibition is effected between each paired neuron: 1 inhibits 1; 2 inhibits 2; etc…). FBN activity is sustained (given a slow decay), and thus acts as a PI home vector accumulator (Stone). Neurons, called CPU1 in some insects, compare each version of the home vector neurally shifted by 1 neuron (as if rotating the ring attractor representation by 45° clockwise or counter-clockwise depending on the hemisphere) with the current compass heading, resulting in an overall activity in the CPU1 (sum of the 8 CPU1) indicating whether the nest is rather on the left-(higher activity in the left hemisphere) or right-hand side (higher activity in the right hemisphere). This left/right differential activityinstead of driving the agent homeis integrated in a DAN connecting the LAL to the MBs (described in Fig. 4D of [START_REF] Wendt | Immunocytochemistry of dopamine in the brain of the locust Schistocerca gregaria[END_REF] ) and thus used to categorise visual learning (see c).

f. The current view results in a specific pattern of KC activity (a), which activates MBONs differentially according to the weight of the KC-MBONs connections set during learning (c).

For instance, views similar to the one experienced when the nest was on the left (orange DAN in (c), trigger KCs with KC-MBON weight set to 0 in this compartment, and thus will activate mostly the MBON of the other compartment. This differential activity between MBONs is integrated in the SIP (Superior Intermediate Protocerebrum) in each hemisphere, resulting in an opponent-like process providing a measure of the likelihood of having the nest on the left or right that is independent of the overall level of visual familiarity (similarly to [START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF][START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF] 5), albeit the nest location within the world varied. Note that the scale of movements relative to the world, which can be chosen arbitrarily, is here higher than in figure 5, indicating that the model is effective across various amounts of visual change in relation to movements. This also suggests that the amount of visual change experienced does not need to be precisely controlled by the agent when effecting a learning walk.

Extended data figure 3. Ants expose their gaze in all direction during learning walks.

a. Examples of learning walks recorded in Myrmecia crosslandi (courtesy of Jochen Zeil, see also [START_REF] Jayatilaka | The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi[END_REF] and Melophorus bagoti (from a previous data set used in [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF] anything, in M. bagoti ants, low angular speed tends to happen slightly more often when the nest lies on the sides rather than in front or behind. d. Relative probability distributions of angular velocities according to whether the nest stands rather in front (0° to 60°, red), in the back (120° to 180°, blue) or on the sides (between 60° and 120°, green line + area). Left and right sides are pulled together using absolute values, so that each of the three categories covers 120° (a third) of the directional space. The similarity of the distributions indicates, here also, no strong tendency to regulate angular speed according to the direction faced relative to the nest; apart from a tendency in M. bagoti to display low angular velocities slightly more often when the nest is on the sides.

  4D[START_REF] Wendt | Immunocytochemistry of dopamine in the brain of the locust Schistocerca gregaria[END_REF] , suggesting that both left and right hemispheric MBs encode both left and right view memories in two different MBs lobes compartments each (4 different compartments in total) (Fig.1a'DAN').

2 -

 2 During homing, the resulting 'left and right familiarity signals' (mediated by different MBONs) are then integrated ipsilaterally (in the dorsal brain area relay) as an opponent process (Fig. 1b 'MBON', point 2 above). 3-Due to the symmetry, this integration is achieved in an opposite manner in each hemisphere (Left -Right familiarity in the left hemisphere; and Right -Left familiarity in the right hemisphere) Fig. 1b 'SIP'). 4-Both opponent signals are then sent to the CX ipsilaterally, providing the desired uncorrelated left and right familiarity input to the CX (point 1 above) (Fig. 1b, 'FB').

  Fan-shaped Body Neurons (FBN, see extended Fig.1 for details) activity lost at each time step: For each FBN: Activity(t+1) = Activity(t) x (1 -memory decay).

I

  am grateful to Florent Le Moël for setting up the python environment to run agent-basedsimulations, as well as to Rüdiger Wehner, Paul Graham, Tom Collett and Michael Mangan for their useful feedbacks on an earlier draft of the manuscript. This study was funded by the European Research Council 759817-EMERG-ANT ERC-2017-STG REFERENCES 1. Collett, M., Chittka, L., and Collett, T.S. (2013). Spatial memory in insect navigation. Current biology : CB 23, R789-R800.

  Figure 1.

Figure 1 .Fig. 2 )

 12 Figure 1. A Mushroom Bodies and Central Complex circuit produces robust visual navigation. a,b. Schematic of the model's functional circuitry. The agent's current panoramic view results from its position in the reconstructed world (g), down-sampled at 10 °/pixel (h).

  ) around their nest (green circle). The absence of recording in the centre for M. bagoti results from an experimental funnel around the nest that the ants had to climb before reaching ground level. Dots indicate position of the head (for M. crosslandi) or body centroid (for M. bagoti) and vectors indicate gaze direction across the recorded frames. Blue vectors mark the instant of 'fixations' (when both angular velocity and forward speed are simultaneously < 1 st decile of their respective distribution, for each individual). b. Circular histogram of the number of views experienced according to their orientation relative to the nest (scale indicated for the circles' rim), during the whole learning walks or during fixations only (see a). Ants show no tendency to bias their gaze towards the nest direction. c. Instantaneous angular velocity of the head according to the direction faced relative to the nest, for the whole learning walks or only moments of low angular velocities. In abscises, 0° indicates facing towards the nest, 180° indicates facing in the antinest direction, left and right bias are pooled together (by using absolute values). Linear (red) and quadratic fit (yellow) are shown. The flatness of the fits indicates that ants show no tendency to regulate their angular speed according to the direction faced relative to the nest. If

data figure. 2. Homing is robust to various training regimes.

  ). Numbers on the left indicate neuron numbers. Letters on the right indicate brain areas (OL: Optic Lobes, MB: Mushroom Bodies, SIP: Superior Intermediate Protocerebrum, PB: Protocerebral Bridge, FB: Fan-shaped Body, LAL: Lateral Accessory Lobe). Paths displayed by the agent when released around the nest with randomly chosen parameter values (g=: motor gain; d=: FBN decay; n=: motor noise, see the 'parameter description' section for detailed explanation) (right column) after learning views in different configurations (left column). Orange and yellow indicate how views are categorised as facing right or left from the goal, and thus being respectively learnt in the left or right MB lobes compartments (see Extended data figure 2 for details of the model implementation). Circular histograms show the number of left and right views experienced for learning. a. 90 views taken at random positions around the nest (up to 3m away from the nest) and facing in random directions, are enough for the agent to subsequently home and display a search at the nest. The failure of some agents suggests that the catchment area is nonetheless restricted here. b,c. Using a large amount of views sampled continuously (at 100fps) from multiple real ants learning walks enable the agent to home robustly, and demonstrates that memory load is not a problem. a,b,c. All agents were equipped with 20,000 Kenyon Cells per hemispheres, and embedded in the reconstructed natural world of Canberra (see figure

	Extended
	g. These lateralized signals from the SIP excite a dedicated set of FBN, literally updating a
	'view-based vector' representation. The sustainability of such a 'view-based vector' depends
	on the FBN activity's decaying rate, which can be varied in our model and has little incidence
	on the agent success (Extended Data Fig. 2, parameter decay). Motor control is effected using
	the same circuitry than for PI 29,32 : the CPU1 neurons control descending motor neurons (MN),

which difference in activity across hemispheres triggers a left or right turn of various amplitude, given a 'motor gain' that can be varied to make the agent more or less reactive (see parameter description).


Sebastian Schwarz
email: sebastian.schwarz@gmail.com

Leo Clement

Lars Haalck

Benjamin Risse

Antoine Wystrach

Paul Sabatier

Compensation to visual impairments and behavioral plasticity in navigating ants

Keywords: compound eye, desert ants, insect vision, monocular, navigation, orientation

Desert ants are known to rely heavily on vision while venturing for food and returning to the nest. During these foraging trips, ants memorize and recognize their visual surroundings, which enables them to recapitulate individually learnt routes in a fast and effective manner. The compound eyes are crucial for such visual navigation; however, it remains unclear how information from both eyes are integrated and how ants cope with visual impairment. Here we manipulated the ants' visual system by covering one of the two compound eyes and analyzed their ability to recognize familiar views in various situations. Monocular ants showed an immediate disruption of their ability to recapitulate their familiar route. However, they were able to compensate for the visual impairment in a few hours by restarting a route-learning ontogeny, as naïve ants do. This re-learning process with one eye forms novel memories, without erasing the previous memories acquired with two eyes. Additionally, ants having learnt a route with one eye only are unable to recognize it with two eyes, even though more information is available. Together, this shows that visual memories are encoded and recalled in an egocentric and fundamentally binocular way, where the visual input as a whole must be matched to enable recognition. We show how this kind of visual processing fits with their neural circuitry.

Significance Statement

If humans look at the world with both eyes, they have no problem to then recognize it with one eye only, and vice-versa. Thus, our way of encoding the world is robust to changes of the visual field. Yet ants do so very differently. Views learnt with two eyes can only be recognized with two eyes, and views learnt with one eye can only be recognized with one eyes (the same eye).

However, this rigidity is compensated by a remarkable behavioral flexibility. Upon covering one eye, ants -which can no longer recognize their familiar surroundings -will restart a learning process to store these novel visual inputs in a parallel memory and resume their normal foraging activity.

Introduction

Self-organized living beings and engineered machines are both able to fulfill their tasks reliably.

However, living organisms show flexibility in the way they achieve their functions and can often compensate for unexpected impairments, whereas machines cannot [START_REF] Kelso | Coordination dynamics. Encyclopedia of Complexity and Systems Science[END_REF]. The aptitude for compensation has been well-studied in human with regards to impairments such as cognitive pathologies, aging or brain damage [START_REF] Sacks | The mind's eye[END_REF][START_REF] Barulli | Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve[END_REF], and is evident after morphological impairments, such as when one manages to achieve with one hand what one used to do with two. These forms of compensations require time and likely involve neural rewiring, so-called structural or network plasticity [START_REF] Johansen-Berg | Structural plasticity: rewiring the brain[END_REF][START_REF] Chklovskii | Cortical rewiring and information storage[END_REF] In insects, which are often assumed to be less versatile than vertebrates [START_REF] Chittka | Are bigger brains better?[END_REF][START_REF] Wystrach | What can we learn from studies of insect navigation?[END_REF], the ability to compensate for impairments is usually studied as an instantaneous response and viewed as the product of the evolved natural redundancy and robustness of these systems, rather than the result of neural plasticity through a life time. For instance, the instantaneous change in gait following a single our double leg amputation may be interpreted as a robust and 'spontaneous' response of the neural machinery governing leg coordination (e.g., in ants [START_REF] Steck | Estimation of homing distance in desert ants, Cataglyphis fortis, remains unaffected by disturbance of walking behaviour[END_REF], cockroaches [START_REF] Delcomyn | Pertubation of motor system of freely walking cockraches. I. Rear leg amputation and the timing of motor activity in leg muscles[END_REF], stick-insects [START_REF] Graham | The effect of amputation and leg restraint on the free walking coordination of the stick insect Carausius morosus[END_REF] and other arthropods like crabs [START_REF] Barnes | Leg Co-OrdinationduringWalkingin the Crab, Uca pugnax[END_REF]). Robustness through redundancy in insects is also well-appreciated in the context of navigation. For instance, representation of directions is based on the integration of a vast array of sensory cues such as visual terrestrial cues [START_REF] Wystrach | Landmarks or panoramas: what do navigating ants attend to for guidance?[END_REF][START_REF] Graham | Ants use the panoramic skyline as a visual cue during navigation[END_REF][START_REF] Wehner | Visual navigation in insects: coupling of egocentric and geocentric information[END_REF][START_REF] Collett | View-based navigation in Hymenoptera: multiple strategies of landmark guidance in the approach to a feeder[END_REF], multiple celestial cues [START_REF] Wystrach | Multiple sources of celestial compass information in the Central Australian desert ant Melophorus bagoti[END_REF][START_REF] Dacke | Dung beetles use the Milky Way for orientation[END_REF][START_REF] Homberg | Central neural coding of sky polarization in insects[END_REF][START_REF] Jundi | Neural coding underlying the cue preference for celestial orientation[END_REF][START_REF] Wehner | The significance of direct sunlight and polarized skylight in the ant's celestial system of navigation[END_REF], olfactory cues [START_REF] Steck | Just follow your nose: homing by olfactory cues in ants[END_REF][START_REF] Bühlmann | Path integration controls nest-plume following in desert ants[END_REF], wind cues [START_REF] Wystrach | Ants use a predictive mechanism to compensate for passive displacements by wind[END_REF][START_REF] Wolf | Pinpointing food sources: olfactory and anemotactic orientation in desert ants[END_REF], magnetic cues [START_REF] Buehlmann | Desert ants learn vibration and magnetic landmarks[END_REF][START_REF] Fleischmann | The geomagnetic field is a compass cue in Cataglyphis ant navigation[END_REF] and also self-motion cues [START_REF] Seelig | Neural dynamics for landmark orientation and angular path integration[END_REF]. Hence, depriving a navigating insect from one modality -or all modalities but one -does not necessarily disrupt their ability to orient. Unilateral suppression of one eye input, however, has a direct impact on the navigational performance of ants [START_REF] Wehner | Does interocular transfer occur in visual navigation by ants?[END_REF][START_REF] Woodgate | The routes of one-eyed ants suggest a revised model of normal route following[END_REF]. Monocular ants may still show evidence of recognition of learnt terrestrial cues but their navigational behavior is drastically affected, revealing an intriguing mix between flexibility and rigidity [START_REF] Wehner | Does interocular transfer occur in visual navigation by ants?[END_REF][START_REF] Woodgate | The routes of one-eyed ants suggest a revised model of normal route following[END_REF]. For instance, monocular desert ants that have learnt a landmark array with one eye only are incapable of recognizing it if the eye cap has been swapped to the other eye, suggesting that there is no inter-ocular transfer of visual terrestrial cues [START_REF] Wehner | Does interocular transfer occur in visual navigation by ants?[END_REF]. Here again, these studies focused on the insects' response that immediately follows the manipulation. But whether some plastic, compensatory mechanisms are at play, and given time, can enable a recovery of a functional behavior remains unknown.

Here, we investigated this question by conducting various eye-capping manipulations on visually navigating desert ants (Cataglyphis velox). Both, their immediate response as well as the potential compensatory effects emerging after a longer period of time were observed and analyzed. The results show that visual manipulations via eye-capping caused substantial impairments that disrupt the ant's ability to home. However, a few hours upon the visual impairment, ants recovered a functional navigation behavior, indicating the existence of a profound plasticity and behavioral flexibility. We explored the mechanisms underlying this plasticity and, as a corollary, gained insight into the way visual information is stored in their brain.

Results and Discussion

One-eye-capping disrupt learnt route-following. Iberian desert ants (Cataglyphis velox) were individually marked and let free to navigate back and forth along an 8.0 m long route between their nest and a feeder (Fig. 1A). The surrounding natural landscape provided plenty of visual information and the route floor was covered with a 1.2 m wide wooden board ensuring an even substrate for the navigating ants. Once experienced to the route (after at least 10 foraging trips), ants were captured at the feeder one by one for the eye-cap treatment: either their left or right compound eye was covered with opaque paint. The eye-capped ant was then provided with a food item, released near the feeder and her homing path was recorded. Regardless of the side of the eye cap, ants failed to navigate directly toward their nest. Instead, their initial direction showed a bias toward the side opposite to the eye cover (Fig. 1B) as previously reported in Tunisian desert ants [START_REF] Wehner | Does interocular transfer occur in visual navigation by ants?[END_REF]. All eye-capped ants (10 out of 10) veered sidewise and fell off their usual route corridor, defined by the wood plank on the floor (Fig. 1A). To enable them to reach home, ants falling off the wood boards were systematically captured and re-released back onto the route beeline (Fig. 1A, dashed line) where path recording resumed. Eye-capped ants veered off the boards 3.42 times on average before reaching their nest surrounding, where recording was stopped (Fig. 1A, Fig. SI1). The difficulty to head home was obvious along the complete homing path (Fig. 1A). By comparison, Sham ants, which received a paint mark on the head but untouched compound eyes and ocelli, showed clearly oriented (Fig. 1B) and much straighter paths (Fig. 1A, Fig. SI1) and almost never left their route corridor (1 out of 17 ants fell one time, so a probability of 0.06 time/ants on average). Thus, covering one compound eye drastically affects navigation in homing ants. Fig. 1. Initial side biases of homing paths in eye capped ants. Black arrows: travel direction of eye capped ants from feeder to nest along a straight foraging route; dashed line: middle of the foraging route; grey lines: ant paths, gaps between paths occurred when ants ran off the board and were re-released in the middle of the homing route; ant head sketch: eye cap condition (this applies to all other Figures). Left-(LEC) and right (REC) eye capped ants were pooled. Paths of REC ants were mirrored and depicted as LEC ants (painted blue eye in ant head). (A) Homing paths from trial 1 to 4 in full vector-(FV) and zero vector ants (ZV) together with homing paths of Sham ants with uncovered eyes. Side biases in paths are visible in both FV (blue example paths) and ZV (red example paths) ants but not in Sham controls. (B) Dashed line: feeder nest direction; number at the outer rim of diagrams: number of ants per sector. Circular histograms depicting directional distribution of ant's paths after 0.2 m of travel across trials and condition. Significant differences from random distributions are indicated with a star (Rayleigh test) at the center of the diagrams and significant differences from the nest direction (N) are indicated with and open star (S-test, for statistical details see Table S1). (C) Meander and straightness of ant paths across trails and condition. FV and ZV vector ants differ significantly in the level of straightness in their first homing path but not in the level of meander. With increasing trial number both FV and ZV paths resemble more Sham paths with low meander and high level of straightness. The compound eyes of ants extract information from both, celestial compass cueswhich are key for path integration (PI) -and terrestrial cues -which are key for learnt views during route-following [START_REF] Schwarz | The properties of the visual system in the Australian desert ant Melophorus bagoti[END_REF]. To test whether the behavioral defect observed in eye-capped ant is due to a disruption of the PI system or the use of learnt views, the experiment was repeated by using this time so-called zero-vector (ZV) ants. While full-vector (FV) ants are captured at the feeder, ZV ants were captured on their way home just before entering their nest, then received an eye-cap and were released with their food item right near the feeder. The PI vector of a ZV ant does no longer point toward the nest; hence the ant can solely rely on learnt terrestrial cues for homing [START_REF] Knaden | Ant navigation: resetting the path integrator[END_REF].

As for FV ants, covering one eye of ZV ants strongly disrupted their ability to navigate home (Fig. 1A). Their initial directions were also biased toward the open eye side (Fig. 1B) and all of them (16 out of 16) repeatedly ran off their usual route corridor (4.43 times/ant on average) to the contrary of ZV sham ants (0.33 times/ant on average). Their overall paths were significantly less straight (Anova: F1, 24 = 10.7, P = 0.003) but only marginally more meandrous (Anova: F1, 24 = 2.42, P = 0.133; Fig. 1C) than eye-capped FV ants. Consequently, eye capping strongly impairs the use of learnt terrestrial cues and the directional input provided by the PI is helping only slightly the FV eye-capped ants to maintain straighter paths. This is in line with previous evidence that information based on celestial but not terrestrial compass cues undergoes inter-ocular transfer [START_REF] Wehner | Does interocular transfer occur in visual navigation by ants?[END_REF].

Eye-capped ants spontaneously recover their route-following behavior. Surprisingly, within a few hours, some of the tested eye-capped ants reoccurred at the feeder again, which provided the opportunity to record their subsequent homebound trips. With increasing homing trials, eye-capped ants gradually recovered their navigational efficiency (Fig. 1). To ensure this recovery was not simply due to an increased reliance on PI, eye-capped ants reaching their nest were captured as ZV ants and released again at the feeder for a second run home. Whether as FV-or ZV ants, their initial direction upon release still tended to be biased toward the open eye side until the 3 rd homing trial (Fig. 1B; see Table S1 for circular statistics). However, both FVand ZV paths showed progressively less local meander (Anova: F0. 1,49.4 = 25.170, P < 0.001) and more overall straightness (Anova: F1. 1,51.3 = 32.52, P < 0.001; Fig. 1C). During the 4 th homing trial the path straightness of eye-capped ants resembled the one from the sham ants (Anova: F1, 30 = 2.68, P = 0.112), and even though they still showed slightly more local meandering (Anova: F1, 30 = 5.39, P = 0.027; Fig. 1C), most eye-capped foragers (5 out of 6) managed to home while no-longer exiting their route corridor. Within a relatively short time period, eye-capped ants can thus recover the ability to follow their familiar route again, using terrestrial cues. These insects are therefore able to compensate impressively fast the strong impairment caused by losing the visual input of one eye, showing a remarkable plasticity in their navigational capacities.

Eye-capped ants show a persistent lateralized sensory-motor defect. The fact that the initial direction of both FV-and ZV ants upon eye-covering was biased toward the open eye side as compared to the correct route direction (Fig. 1B) provides two insights. First, because ZV ants did not head randomly (or backtracked) like they do on visually unfamiliar terrain ((32), Fig. 2B), freshly eye-capped ants must still be able to derive some information from their visual route memory. Second, because the side of the directional bias depends purely on the side of the capped eye (Fig. 2A,C), a lateralized sensory-motor defect caused by losing one-eye input might be at play. To investigate this lateralized sensory-motor defect independently of the expression of the recognition of visual memories, freshly eye-capped ants were released in unfamiliar surroundings and their behavior was analyzed based on video recordings. Eye-capped ants tended to regularly alternate between brief bursts of speed when turning toward the open eye side and pauses by rotating on the spot toward the covered eye side (Fig. 2C). This can be simply quantified by assessing whether turning direction is biased on one side when the ants forward speed is above the individual's average. While sham ants with both eyes open showed no bias (Sham ants: one-sided Wilcoxon test: V = 32, P = 0.715, Fig. 2A) freshly one-eye capped ant turned more toward the open eye when walking fast (Just-capped ants: one-sided Wilcoxon test: V = 188, P = 0.001; Fig. 2A).

To test whether the observed recovery of route-following behavior is due to a compensation over time of such a lateralized sensory-motor defect, we tested whether the bias persisted in ants after they had recovered their route-following behavior with one eye. Eyecapped ants that had recovered their route still displayed the lateralized defect on unfamiliar terrain (Compensated ants: one-sided Wilcoxon test: V = 113, P = 0.044, Fig. 2). We noted that, given longer period of time with the eye cap, this bias eventually tended to fade (data in preparation) showing the ability of experienced ants to also correct for such a sensory-motor defect. However, the persistence of the lateralized bias here (Fig. 1, 2) shows that the ant's route recovery is due to a process that is quicker and different from overcoming such a sensori-motor defect. Also, this longer lasting sensori-motor defect may explain why one-eye ants having just recovered their route still showed more meandering than sham ants (Figure 1).

Eye-capped ants compensate by re-engaging in a new route ontogeny. We next investigated whether the recovery of route-following behavior in eye-capped ants is based on the ability to eventually recall previous binocular memories, or alternatively, based on the formation of novel, monocular route memories. To do so, we covered the left or right eye of a new cohort of experienced, individually marked ants, and released them back to their nest. Their behavior was recorded once they emerged outdoors again. Upon leaving their nest entrance these freshly eyecapped ants behaved remarkably similar to naïve foragers exiting their nest for the first time:

they displayed typical learning walks (Fig. 3). These convoluted paths and pirouettes enable them to expose their gaze in multiple directions for visual learning [START_REF] Zeil | The learning walks of ants (Hymenoptera: Formicidae)[END_REF]. Learning walks are likely a consequence of perceiving an unfamiliar scenery when leaving the nest. Indeed, experienced ants may also display a few zigzags and pirouettes upon departure if an alteration of the visual surrounding has occurred around the nest; but in general, they rapidly scoot along their familiar outbound route again [START_REF] Zeil | The learning walks of ants (Hymenoptera: Formicidae)[END_REF][START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF]. Here, the eye-capped ants remained at first very close to the nest and re-entered their colony often, like naïve ants do (Fig. 3C). They displayed on average more than 15 subsequent learning trips before reaching the familiar feeder located 5.0 m away (Fig. 3A,B) supporting the idea that the scenery appeared strongly unfamiliar to these eye-capped ants. Interestingly and contrary to naïve ants, learning walks of freshly eyecapped ants were biased toward the feeder direction (Fig. 3A); this was true from the first learning walks onward (Fig. 3C, One-tailed T-test: P = 0.019) indicating that previous memory of the feeder direction persisted despite the eye-cover. Whether this directional memory was due to remnant memories of terrestrial cues learnt with both eyes, or the expression of a stored food-ward celestial compass vector [START_REF] Fernandes | Acquisition and expression of memories of distance and direction in navigating wood ants[END_REF][START_REF] Bolek | Establishing food site vectors in desert ants[END_REF] could not be disentangle here. Fig. 3. Learning walks (LWs) in eye capped ants. Horizontal lines: one-way baffles on the foraging route that could be traversed during outbound trips but had to be negotiated during inbound trips (this also applies for Figs. The subsequent out-and inbound (i.e., homing) trips of these eye-cap ants were also recorded, which, as expected, showed gradual improvements (Fig. SI3 andSI4). After 8 successful trips between the nest and feeder, all the recorded eye-capped ants (In-and-Out) had fully recovered their ability to run between the nest and the feeder without colliding into baffles (Fig. 4A). Tested as ZV ants, these individuals could home equally well, (Fig. 4A) showing as previously, that they used terrestrial cues. To ensure that this recovery was actually due to performing learning walks, and not simply due to the time passed while navigating outdoors, the experiment was replicated by using two additional cohorts of freshly eye-capped ants that had previous experience (with both eyes) of the route. In one group (In-only), eye-capped ants were systematically captured upon exiting the nest and released at the feeder for homing, preventing them to display learning walks around the nest and outbound trips to the feeder. In the other group (Out-only), eye-capped ants were free to display learning walks but upon reaching the feeder, these ants were systematically captured and released inside their nest entrance, preventing them to perform their homing runs (inbound trips). This latter group of ants (Out-only) displayed similar learning walks than the previous condition (Fig. 3), and after 8 successful outbound trials up to the feeder, foragers were able to home quite well, albeit not as successful as ants that had experienced both out-and inbound trips (Fig. 4 and Fig. SI3). This shows that inbound trips are helpful but not crucial for route recovery. Contrastingly, ants deprived of learning walks (In-only) showed no improvement in their homing ability despite spending a long time navigating outdoor (Fig. 4).

On the contrary, they showed a decrease in homing performance across trials (Fig. SI3), suggesting again that freshly eye-capped ants have strongly impaired but remnant binocular visual memories of the route, but that subsequent experience outdoor does not enable to reaccess them, or form new functional ones. Note that In-only ants, as being transferred from the nest to the feeder, could not rely upon path integration. After eight attempts of homing without learning walks, these ants searched at length around the release point (feeder) and only one individual eventually managed to home (Fig. 4). It was rather difficult to conduct this condition, as In-only ants mostly failed to reach their nest during training. After 10 min of search and an unsuccessful inbound trip the foragers had to be put back manually to the nest. Unfortunately, many of those individuals stopped their foraging activity and hence could not be tested anymore.

In sum, performing learning walks and outbound trips is crucial for eye-capped ants to recover their route. This echoes what is observed in naïve ants with untouched eyes, for whom learning walks and outbound trips are key for learning an inbound route, while inbound experience may help but seems to be insufficient on its own [START_REF] Schwarz | How do backward-walking ants (Cataglyphis velox) cope with navigational uncertainty?[END_REF][START_REF] Freas | Terrestrial cue learning and retention during the outbound and inbound foraging trip in the desert ant, Cataglyphis velox[END_REF][START_REF] Schwarz | Ants' navigation in an unfamiliar environment is influenced by their experience of a familiar route[END_REF][START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF][START_REF] Freas | Landmark learning, cue conflict, and outbound view sequence in navigating desert ants[END_REF]. Together, this suggests that eye-capped ants cannot recognize the learnt route with both eyes to full capacity and thus perceive at first the world as quite unfamiliar. This unfamiliarity triggers learning walks upon leaving their nest, which enables them to form novel memories of the terrestrial cues and eventually re-learn the route monocularly. Therefore, ants with previous experience possess the flexibility to recapitulate the natural ontogeny of route learning for a second time.

Ants learn binocular visual memories. Previous behavioral work in ants and bees has shown

that visual memories learnt with one eye cannot be retrieved using the other eye, suggesting that these insects form two separated visual memories for each eye, with an absence of interocular transfer between these visual memories [START_REF] Wehner | Does interocular transfer occur in visual navigation by ants?[END_REF][START_REF] Giger | Honeybee vision: analysis of orientation and colour in the lateral, dorsal and ventral fields of view[END_REF]. The current study revealed that visual memories acquired with both eyes cannot be retrieved with one eye. Rather than two separated visual memories for each eye, one possible explanation is that visual memories are fundamentally binocular, that is, their recall implies the correct and simultaneous combination of both left and right visual input. Indeed, neurobiological studies show that each eye sends bilateral visual projections to the Kenyon cells (KCs) in both the left and right hemispheres of the mushroom bodies (MBs) [START_REF] Habenstein | The brain of Cataglyphis ants: Neuronal organization and visual projections[END_REF], where visual memories for route-following are formed [START_REF] Webb | Neural mechanisms of insect navigation[END_REF][START_REF] Kamhi | Vertical lobes of the mushroom bodies are essential for view-based navigation in Australian Myrmecia ants[END_REF][START_REF] Buehlmann | Mushroom bodies are required for learned visual navigation, but not for innate visual behavior, in ants[END_REF]. What's more, visual projection to the KCs are pseudo-random, therefore, it seems quite likely that individual KCs, whether in the left or right hemisphere, receives input from both eyes, and thus must receive the correct bilateral input to be activated (Fig. 6). If this hypothesis is correct, it predicts, rather counterintuitively, that memories acquired with one eye could not be retrieved with both eyes.

To test this prediction, a novel cohort of ants from a new nest was eye-capped either on the left or the right eye. Crucially, the training to the foraging route and the transformation of the visual scenery by clearing the floor and altering the natural bushes and other terrestrial cues around the route was done afterward. The set-up was similar to the former experiment: a 5.0 m long route containing two baffles in the middle and one feeder at the end. These ants had binocular memories of the previous natural surroundings but experienced the foraging route and its novel surroundings only with an eye-cap (i.e., monocular). After a few hours, the eyecapped ants were familiarized with the new surroundings, discovered the feeder and learnt to home successfully along the novel route (Fig. 5A). When captured at the nest and released again as ZV ants near the feeder, these now experienced eye-capped foragers had no difficulties to recapitulate the route, confirming that they had formed (monocular) visual memories of the terrestrial cues (Fig. 5A, left panel). However, when uncapped, that is, when exposed for the first time to this particular route with two eyes, the ants struggled immensely to home (Fig. 5A, right panel). All uncapped ants searched predominantly in the upper section of their foraging route incapable of negotiating the baffles, only 2 out of 10 eventually managed to reach the nest entrance (Fig. 5A, right panel). Even when some (5 out of 10) of these ants were re-released beyond the baffles, closer to the nest, they were not able to home successfully and wandered around seemingly without purposeful orientation (paths not shown). Thus, ants with two eyes were unable to recognize the route learnt with one eye. In other words, adding the visual input of the second eye prevents the access to the visual memories acquired with one eye only.

Therefore, when it comes to navigation, visual memories are binocular.

Fig. 5.

Visual impairment before route learning. (A) Ants that learnt the route with one eye covered, hence no previous experience (no prev. exp.) of the route with uncovered eyes. After compensation (comp.) and reaching the feeder, homing paths are displayed with the eye cap still in place (left panel) and with uncapped eyes (right panel). Ants without previous experience of the route struggle to find back to the nest once the eye cap is removed. (B) Ants that learnt the route with both eyes, hence previous experience (prev. exp.) of the route before eye capping. After eye capping and reaching the feeder, homing paths are displayed with the eye cap still in place (left panel) and the eye cap removed (right panel). Ants with previous experience of the route have no difficulties to find back to the nest. (C) Path characteristics of ants with and without previous experience of the route before eye capping. Ant with previous experience show paths with low meander and high level of straightness after eye cap was removed whereas ants without previous experience of the route show paths with high meander and low level of straightness with both eyes uncovered (Anova meander: F-0. 13,0.05 = -2.487, P = 0.018, Anova straightness: F0. 07,0.02 = 3.281, P = 0.002). In terms of neural implementation, this supports the idea that many KCs in the MB receive input from both eyes simultaneously (Fig. 6). It remains likely however that some KCs receive input from one eye only, which could explain why some freshly eye-capped ants, or freshly uncapped ants, even though strongly impaired, could still derive a rough estimation of the nest direction, as if they recognized the scene only partially or sporadically. The variation in homing performance observed across individuals may be explained, at least in part, by the more or less fortunate random connectivity in their mushroom body. Eye-cap ants do not forget past binocular memories. To control whether the visual impairment observed with freshly uncapped ants (Fig. 5A, right panel) was indeed due to an inability to access visual memories sorted with one eye and not just an inherent consequence of the recent recovery of bilateral visual input, the previous experiment was re-run with a cohort of ants that, this time, had previous two-eyed experience of the route. These ants were eyecaped, let free to re-learn the route with one eye, then 'uncapped' and tested. Contrary to the previous cohort of ants with no previous experience of the route, these foragers did not struggle whatsoever to home toward the nest with uncapped eyes, even when tested as ZV ant (Fig. 5B).

This shows that uncapping the ants bears no inherent issue and thus confirms that memories acquired with one eye are no longer accessible with two eyes (Fig. 5A). In addition, it shows that the latter cohort of ants had not forgotten their former visual memories of the route acquired with two eyes. Learning the route anew with one eye does not override the memories of the previous two-eye memories.

In neurobiological terms, one-eyed memories and two-eyed memories are likely to recruit different set of KCs in both hemispheres, with perhaps a certain amount of overlap between them (Fig. 6). This can be viewed as learning and remembering two different routes, which desert ants can also do [START_REF] Sommer | Multiroute memories in desert ants[END_REF]. Indeed, ants possess hundreds of thousands of KCs ( 43) and models of the MBs show that this offers memory space for recognizing a large amount of visual sceneries [START_REF] Ardin | Using an insect mushroom body circuit to encode route memory in complex natural environments[END_REF][START_REF] Möel | Opponent processes in visual memories: a model of attraction and repulsion in navigating insects' mushroom bodies[END_REF]; enough to remember views around the nest, along multiple routes or as shown here, along the same route but with monocular and binocular inputs.

Conclusion

The response of navigating ants to visual impairments shows a surprising mix between rigidity and flexibility. Rigidity in the sense that the recognition of visual memories is not robust to changes in the visual field, highlighting a fundamental difference with the way human encode the visual world. Flexibility in the sense that experienced ants manage to compensate such a deficit by re-engaging a route learning process in a similar way than naïve ants do at the onset of their foraging ontogeny. With time, ants re-learn and recognize a route with ease, and the newly acquired memory does not override previous ones. Investigating the plastic neural mechanisms underlying these feats will form a great agenda for future research. exhibit behaviors typical for solitary foraging ants that venture out of the nest to find food without the help of pheromone trails [START_REF] Cerda | Behavioural and physiological traits to thermal stress tolerance in two Spanish desert ants[END_REF]. Instead their navigational guidance is primarily based on visual input derived from celestial and terrestrial sensory cues [START_REF] Mangan | Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox)[END_REF].

Material and Methods

Experiments

General experimental set-up

All set-ups shared a similar basic design, which is described in the following while specific differences were appropriately mentioned above. Ants were trained to follow a route from the nest to a feeder that provided food ad libitum in form of a variety of buttery, sweet biscuit crumbs. Nests were enclosed with thin white plastic planks, a smooth material, too slippery for the tarsi of the ants and hence preventing them to forage elsewhere. A square plastic bowl was sunk into the ground and served as feeder. The walls of the feeder were covered with fluon to prevent ants from climbing out. During training ants dropped into the feeder and could return to the nest via a small wooden ramp that led the ants out of the feeder onto the foraging route.

Training continued until the ants familiarized with the foraging route and scuttled fast and straight forward between the nest and the feeder at least five times. For tests, ants were either caught at the feeder or close to the nest entrance. Ants caught at the feeder have both the familiar visual scenery and the homing vector of their path integrator as scaffold for homing: hence fullvector ants (FV). Ants caught close to the nest ran off their homing vector and can solely rely on the familiar visual scenery during homing: hence zero-vector ants (ZV). All tested ants were subjected to an eye-cap procedure, which was non-invasive and reversible. For that foragers were manually caught and the first two pair of legs including one of the antenna were carefully fixed between two fingertips. Thus, the head of the ant was immobile and one of the compound eyes could be covered with a drop of opaque enamel paint (Tamiya). The tip of a thin pin acted as brush and painted ants were subsequently checked for an even and complete cover of the targeted eye with the help of a 10´ magnifying glass. Afterward ants were transferred into a small plastic vial and tested as soon as the foragers held on to a crumb. Paths of tested ants were recorded with pen and paper and the help of a square grid (0.5´0. with a Savitzky-Golay filter of order 3 and filter length of 41 frames, followed by a double smoothing of the Dtheta signal by moving the average of window length by three. Finally, all pauses and events longer than one second were removed. A one-sided Wilcoxon test was used to calculate the significance of each pooled group against random choice.

Fig. 2 .

 2 Fig. 2. Initial side bias in eye-capped ants released in unfamiliar surroundings. (A) Distribution of side preferences of ants with just capped eyes (Just capped) and ants that had compensated (Compensated) for the visual impairment in their familiar foraging route. The characteristic motor deficit, displayed by capped ants toward the side bias of the uncapped eye is evident in Just capped ants, as represented by the higher mean angular speed when turning to the uncapped eye side. Compensated ants show a lower side bias and resemble more the even distribution of Sham ants. Stars indicates significant difference to random, one-sided Wilcoxon test (see text for details). (B) Tracked paths of Sham, Just capped and Compensated ants (upper panels) and initial (60 cm radius) heading directions (lower panels). Black arrows depict mean heading directions. (C) Example paths of ants with (Just capped, Compensated) and without (Sham) the characteristic motor deficit caused by eye-capping.

  Fig.3. Learning walks (LWs) in eye capped ants. Horizontal lines: one-way baffles on the foraging route that could be traversed during outbound trips but had to be negotiated during inbound trips (this also applies for Figs.4 and 5). (A) LWs displayed by In-and-Out and Outonly ants during training. Black bold lines: example paths. (B) Distribution of the number of LWs in In-and-Out and Out-only ants. (C) Comparison between directional path bias of first LWs in In-and-Out and naïve ants. In-and-Out ants show a significantly higher bias toward the feeder direction (grey arrow) than naïve ants.

Fig. 4 .

 4 Fig. 4. Paths and path characteristics of eye capped ants after compensation with in and outbound trip (In-and-Out), no inbound trip (Out-only) and no outbound trip (In-only). (A) Paths of homing ants after 8 training trials. All In-and-Out and Out-only ants were able to home whereas In-only ants struggled and only one individual succeeded. (B) Meander and straightness of homing paths after 8 training trials. In-and-Out ants displayed paths with the lowest meander (Anova: In-and-Out vs. Out-only: F0. 29,0.07 = 4.122, P = 0.001; In-and-Out vs. Inonly: F0. 63,0.11 = 5.981, P < 0.001) and highest level of straightness (Anova: In-and-Out vs. Outonly: F-0. 02,0.06 = -3.436, P = 0.003, In-and-Out vs. In-only: F-0. 69,0.09 = -7.327, P < 0.001), followed by Out-only and In-Only ants.

Fig. 6 .

 6 Fig. 6. Simplified model depicting neurobiological implications. (A) Both compound eyes feed into both Mushroom Bodies and elicit via projection neurons (PN) a certain set of Kenyon cells for each perceived view along a familiar route. (B) If the left eye is impaired (eye cap) the patterns of Kenyon cells are disrupted as they perceive only input from the uncapped right eye. Hence, the familiarity of the visual scenery is altered and homing ants need to compensate for the lack of visual information by relearning the surrounding scenery. The different bar codes represent different activated sets of Kenyon cells for a given view, for each compound eye.

  took place during June and July 2017-19 on a plain open field with grassy vegetation close to the harbor in the metropolitan area of Seville, South of Spain. Three different nests of the Iberian desert ant Cataglyphis velox were used for training and testing. Workers

  5 m) made of string and tent pegs mounted on the ground. Paths were digitized with GraphClick (Arizona Software) and statistical analyses were calculated with Matlab (Mathworks, Matick, MA, U.S.A.). Paths in the unfamiliar environment (Sham, Just capped and Compensated ants) were recorded with a Panasonic Lumix camera (DMC FZ200) fixed on a tripod, digitized via a novel video tracker system of the Risse group at the University of Münster (52, 53) and analyzed with R studio. LEC and REC ants were pooled by mirroring paths of REC ants to LEC. Differences in their tendency to turn left or right were determined by comparing the angular velocity via a calculation of Dtheta and forward velocity. The X and Y values of the paths were smoothed

ACKNOWLEDGEMENTS

We are grateful for logistic and administrative support of Xim Cerda and his team at the Spanish National Research Council (CSIC Seville) and appreciate the help during field work preparation and data collection of Cornelia Buehlmann, Florent Le Moël and Christelle Gassama. This study was funded by the European Research Council 759817-EMERG-ANT ERC-2017-STG.