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

Keywords:

Highlights

-Movements can fundamentally transform the task for the brain.

-A same brain in different bodies or situations produces different behaviors.

-Clear-cut apparent decisions can arise without top-down control.

-A cognitive being may result from synchronization of distributed processes Highlights

Embodiment in ants

A first approach to highlighting the interplay between brain and body is to consider examples of embodiment, where cognitive problems are outsourced to body structures and movements.

Among classic examples of embodiments is our tendency to display hand gestures when solving a spatial reasoning problem, or to mimic facial movements when judging the emotion of others. Performing such gestures does improve performance, and, in contrast, preventing them or displaying other gestures non-related to the task decreases the cognitive performance [START_REF] Wystrach | Embodying Emotion[END_REF][START_REF] Moreau | Constraining movement alters the recruitment of motor processes in mental rotation[END_REF][START_REF] Wohlschläger | Mental and manual rotation[END_REF]. If someone ask you the way to the train station, stand up and free your hands before answering, your choice of words will likely be better. However, these examples illustrate merely a facilitating role to the body. Body movements can be viewed as helpful but non-essential, and may not fundamentally modify the type of cognitive process involve to solve the task. Research in ant navigation offers starker examples of embodiment, where movements transform the cognitive problem and enable novel solutions to emerge. For instance, when a foraging ant is recapitulating a well-known route using learnt visual cues [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 | Views, landmarks, and routes: how do desert ants negotiate an obstacle course?[END_REF], she needs first to compare the currently perceived visual scene to its memory in order to recognise whether the location is familiar. If that so, the ant must then derive information about which is the correct direction to go. A priori, this task requires complex visuo-spatial representations and abilities such as mental rotations or allocentric spatial reconstruction. Indeed, when engineers design robot that must follow a route, such as during the DARPA Grand Challenge race for autonomous cars [START_REF] Seetharaman | Unmanned vehicles come of age: The DARPA grand challenge[END_REF], they resort to high accuracy sensors and impressively heavy computing power. Ants, in contrast, not only solve the same problem more robustly -they do not get lost -but do so in an apparently simpler way. Ants have low-resolution vision [START_REF] Schwarz | The properties of the visual system in the Australian desert ant Melophorus bagoti[END_REF][START_REF] Zollikofer | Optical Scaling in Conspecific Cataglyphis Ants[END_REF][START_REF] Ogawa | Spatial resolving power and contrast sensitivity are adapted for ambient light conditions in Australian Myrmecia ants[END_REF], their eyes are rigid and immobile, and they walk mostly forward, very rarely sideways. At first, such a reduction of sensory information and degree of freedom seem like a serious handicap. In fact, these bodily constraints are a key part of the solution. Low resolution can greatly facilitates the recognition of natural scenes -in addition to reducing memory load [START_REF] Graham | Vision for navigation: what can we learn from ants?[END_REF][START_REF] Wystrach | How do field of view and resolution affect the information content of panoramic scenes for visual navigation? A computational investigation[END_REF]. Second, the fact that their gaze cannot depart much from their walking direction ensure that visual input and direction of movement are profoundly linked. As a consequence, the recognition of a scene as projected on the insect eyes -that is, without the need for any cognitive spatial reconstruction -directly indicates that the body is roughly aligned in the same orientation as when the scene had been memorised in the first place [START_REF] Zeil | Visual homing: an insect perspective[END_REF]. In theory, all the ant needs to do is : first, memorise views while facing the route direction and second, when comes the time to retrieve the correct route direction, rotate on the spot and move forward as soon as the current view appears familiar [START_REF] Baddeley | A model of ant route navigation driven by scene familiarity[END_REF][START_REF] Wystrach | Ants might use different view-matching strategies on and off the route[END_REF]. This minimal model is supported by the fact that ants typically show such rotational scanning movements when trying to recover their route direction [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF]. The trick is particularly obvious when these insects are dragging heavy items backwards: they regularly have to drop their precious load and scan around to retrieve the route direction [START_REF] Pfeffer | How to find home backwards? Navigation during rearward homing of Cataglyphis fortis desert ants[END_REF][START_REF] Schwarz | How Ants Use Vision When Homing Backward[END_REF]. To put it simply: physical rotations bypasses the need for mental rotations. With this embodied strategy, all that is left for the brain to do is to store and compare low resolution visual inputs encountered along the route. This simplified neural task can be readily achieved by brain neuropils called the mushroom bodies [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], a structure that is indeed necessary for learnt visual navigation in ants [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[END_REF]. Overall, given these bodies and movements, a few thousand neurons is all it takes to recapitulate remarkably long routes through complex environments [START_REF] Baddeley | A model of ant route navigation driven by scene familiarity[END_REF][START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF].

Evidently, the existence of these movements do not imply that the insect lack additional, more sophisticated neural abilities [START_REF] Wystrach | Ants might use different view-matching strategies on and off the route[END_REF][START_REF] Thomas | Rotation invariant visual processing for spatial memory in insects[END_REF][START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF][START_REF] Stone | An Anatomically Constrained Model for Path Integration in the Bee Brain[END_REF], which could be necessary for tasks requiring simulations beyond what physical movements can provide, or simply useful when producing such movements is too costly. However, through this example, one can envision how movements and body structures profoundly change the means to solve a problem. The neural circuit needed to store and compare egocentric low resolution views can be quite different -and arguably much simpler-from the one required for mental rotation or allocentric spatial reconstruction. Motor movements literally transform the information provided to the brain through sensors. After all, movements evolved before brains, and the first neurons to appear were used to coordinate muscles. This is the ground on which higher cognition is built. Scanning in ants, like human gestures when solving spatial tasks, are not just helpful tricks. They disclose a deeper, more fundamental way of functioning.

Same neural mechanism, different behaviours.

We have seen how body senses and movements can contribute to a successful behaviour, but what about a change of behaviour? Suitable changes of behaviours are typically thought to be the consequence of so-called 'decisions'; but does this necessarily implies a dedicated mental process?

When recapitulating a route home, desert ants run remarkably fast and straight [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] Pfeffer | High-speed locomotion in the Saharan silver ant, Cataglyphis bombycina[END_REF], but having arrived within close proximity of the nest, their behaviour changes drastically: they slow down and engage in convoluted searches for the nest entrance, similarly to what they do when in totally unfamiliar terrain [START_REF] Wehner | Visual navigation in insects: Coupling of egocentric and geocentric information[END_REF][START_REF] Schultheiss | Information content of visual scenes influences systematic search of desert ants[END_REF][START_REF] Buehlmann | The interaction of path integration and terrestrial visual cues in navigating desert ants: what can we learn from path characteristics?[END_REF][START_REF] Schultheiss | Finding the nest: inbound searching behaviour in the Australian desert ant, Melophorus bagoti[END_REF][START_REF] Merkle | Uncertainty about nest position influences systematic search strategies in desert ants[END_REF][START_REF] Müller | The Hidden Spiral -Systematic Search and Path Integration in Desert Ants, Cataglyphis-Fortis[END_REF]. 'Route following' and 'goal pinpointing' (also called 'homing') are two different navigational tasks. While the first demands the ability to maintain a correct direction of travel, the latter requires one to pinpoint a very specific location, often by turning around. Therefore, it initially feels intuitive that two different mechanisms are at play, and this is probably why these tasks have been typically investigated separately ('nest pinpointing': [START_REF] Wehner | Visual spatial memory in desert ants, Cataglyphis bicolor (Hymenoptera: Formicidae)[END_REF][START_REF] Cartwright | Landmark learning in bees -experiments and models[END_REF][START_REF] Wehner | Visual navigation in insects: Coupling of egocentric and geocentric information[END_REF][START_REF] Zeil | Catchment areas of panoramic snapshots in outdoor scenes[END_REF][START_REF] Dittmar | Goal seeking in honeybees: matching of optic flow snapshots?[END_REF][START_REF] Schultheiss | Information content of visual scenes influences systematic search of desert ants[END_REF][START_REF] Collett | Insect navigation en route to the goal: Multiple strategies for the use of landmarks[END_REF][START_REF] Macquart | Ant navigation en route to the goal: signature routes facilitate way-finding of Gigantiops destructor[END_REF]; 'route following': [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 | Views, landmarks, and routes: how do desert ants negotiate an obstacle course?[END_REF][START_REF] Collett | Insect navigation en route to the goal: Multiple strategies for the use of landmarks[END_REF][START_REF] Macquart | Ant navigation en route to the goal: signature routes facilitate way-finding of Gigantiops destructor[END_REF][START_REF] Collett | Route learning by insects[END_REF][START_REF] Lent | A Motor Component to the Memories of Habitual Foraging Routes in Wood Ants?[END_REF] and called for different types of models [START_REF] Wystrach | Views, landmarks, and routes: how do desert ants negotiate an obstacle course?[END_REF][START_REF] Wystrach | Ants might use different view-matching strategies on and off the route[END_REF][START_REF] Wehner | Visual spatial memory in desert ants, Cataglyphis bicolor (Hymenoptera: Formicidae)[END_REF][START_REF] Cartwright | Landmark learning in bees -experiments and models[END_REF][START_REF] Zeil | Catchment areas of panoramic snapshots in outdoor scenes[END_REF][START_REF] Möller | Local visual homing by matched-filter descent in image distances[END_REF][START_REF] Collett | How desert ants use a visual landmark for guidance along a habitual route[END_REF]. In fact, both behaviours may well emerge from the same mechanism. The trick, revealed by behavioural observations of ants, is again about movements rather than brains. Upon leaving their nest, ants (similarly to bees and wasps [START_REF] Zeil | Orientation flights of solitary wasps (Cerceris, Sphecidae, Hymenoptera) .1. Description of flight[END_REF][START_REF] Philippides | Bumblebee calligraphy: the design and control of flight motifs in the learning and return flights of Bombus terrestris[END_REF][START_REF] Zeil | Structure and function of learning flights in bees and wasps[END_REF][START_REF] Tinbergen | Über die Orientierung des Bienenwolfes (Philanthus triangulum Fabr.)[END_REF][START_REF] Lehrer | Bees which turn back and look[END_REF]) display well-choreographed dances around the nest where they regularly turn around to pause while fixating their gaze in a specific direction, such as the nest direction [START_REF] Müller | Path Integration Provides a Scaffold for Landmark Learning in Desert Ants[END_REF][START_REF] Jayatilaka | The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi[END_REF][START_REF] Fleischmann | Early foraging life: spatial and temporal aspects of landmark learning in the ant Cataglyphis noda[END_REF][START_REF] Zeil | The learning walks of ants (Hymenoptera: Formicidae)[END_REF]. The same happens when the insect is departing from a food source; it turns around multiple times to fixate the newly discovered goal [START_REF] Nicholson | Learning walks and landmark guidance in wood ants (Formica rufa)[END_REF][START_REF] Judd | Multiple stored views and landmark guidance in ants[END_REF][START_REF] Wei | Deciding to learn: modulation of learning flights in honeybees, Apis mellifera[END_REF]. Assuming that ants memorise the egocentric scene perceived during these specific goal-oriented moments is enough for a goal pinpointing behaviour to emerge from the exact same minimal 'route-following mechanism' described above [START_REF] Baddeley | A model of ant route navigation driven by scene familiarity[END_REF][START_REF] Wystrach | Snapshots in ants? New interpretations of paradigmatic experiments[END_REF][START_REF] Graham | Animal cognition: multi-modal interactions in ant learning[END_REF]. In other words, all around the nest, the 'remembered route direction' is a constellation of memories pointing towards the nest; so that a goal-centred search emerges naturally once the end of the route is reached. The rule is always to go straight when the view is familiar, without any need for additional mechanisms, but an apparent decision to start searching emerge.

Understanding this embodied way of functioning as proved remarkably powerful in insect navigation research. Based on novel experimental and neurobiological insights, this model has been developed further to capture a wider diversity of behaviours. Notably, assuming that ants also memorise aversive views pointing away from the nest [START_REF] Jayatilaka | The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi[END_REF][START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF], or categorise views as being to the left or right of 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] can explain why ants behave similarly close to the nest and those on unfamiliar terrain [START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF], how ants could home equally successfully while walking backwards [START_REF] Schwarz | How do backward-walking ants (Cataglyphis velox) cope with navigational uncertainty?[END_REF], or how landmark information could be readily combined with other heading information in the brain [START_REF] Wystrach | A lateralised design for the interaction of visual memories and heading representations in navigating ants[END_REF]. Even though these revised models explain a large number of observed behaviours and are more faithful to the insects' neural circuits, the mechanism remains in essence the same. The agent does not need to decide or even 'know' whether it is close or far from its goal, yet its behaviour will still change appropriately.

The lesson from this body of work is that a suitable transition between different-looking behaviours may not necessarily result from neural top-down decisions, but can simply rely on the way the animal's body is -or has been-exposed to the environment. The learning walks displayed by naïve individuals illustrate well how this bodily factor participate to ontogeny and produce, in fine, behavioural diversity without the need to revise brain circuits.

The oscillating animal

If embodiment and environmental exposure -so-called situatedness [START_REF] Beer | 6 -The Dynamics of Brain-Body-Environment Systems: A Status Report[END_REF]-favour the emergence of behavioural transitions within an individual, what about the more drastic differences observed across species?

Flying wasps [START_REF] Stürzl | How Wasps Acquire and Use Views for Homing[END_REF] or moths [START_REF] Namiki | The neurobiological basis of orientation in insects: insights from the silkmoth mating dance[END_REF][START_REF] Kuenen | A non-anemotactic mechanism used in pheromone source location by flying moths[END_REF], walking ants [START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF][START_REF] Lent | Phase-Dependent Visual Control of the Zigzag Paths of Navigating Wood Ants[END_REF][START_REF] Hangartner | Spezifität und inaktivierung des spurpheromons von Lasius fuliginosus Latr. und orientierung der arbeiterinnen im duftfeld[END_REF] or beetles [START_REF] Lönnendonker | Dynamic Properties of Orientation to a Visually Fixated Target by Walking Colorado Beetles[END_REF], crawling larvae [START_REF] Wystrach | Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae[END_REF] or nematodes [START_REF] Iino | Parallel use of two behavioral mechanisms for chemotaxis in Caenorhabditis elegans[END_REF][START_REF] Izquierdo | Evolution and Analysis of Minimal Neural Circuits for Klinotaxis in Caenorhabditis elegans[END_REF], swimming vertebrates [START_REF] Wolf | Sensorimotor computation underlying phototaxis in zebrafish[END_REF][START_REF] Lansner | Realistic modeling of burst generation and swimming in lamprey[END_REF] as well as moving cells [START_REF] Yang | Zigzag turning preference of freely crawling cells[END_REF], share a strange habit: they display regular lateral oscillations along their paths, slowly alternating between left and right turns and producing a zigzag trajectory. In insects, these oscillations are rather slow, with one cycle every five seconds or so and appear to result from neural reciprocal inhibition between left and right hemisphere's pre-motor conserved brain areas [START_REF] Namiki | The neurobiological basis of orientation in insects: insights from the silkmoth mating dance[END_REF][START_REF] Steinbeck | Connecting brain to behaviour: a role for general purpose steering circuits in insect orientation?[END_REF]. The ancestral and universal nature of lateral oscillations suggests that they are fundamentally important across a wide range of insects with different lifestyles and body morphologies. But why? Interestingly, when applied to ant visual navigation models, assuming that the body spontaneously generates oscillations drastically improves the efficiency of both route following [START_REF] Kodzhabashev | Route Following Without Scanning[END_REF] and homing [START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF]. In fact, all the agent needs to do is to modulate the amplitude of its oscillations based on the familiarity of the scene currently perceived… and it will safely go along its route and pinpoint home [START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF][START_REF] Kodzhabashev | Route Following Without Scanning[END_REF] (figure 1A). While unfamiliar views stimulate stronger lateral oscillations, and hence changes of directions, familiar scene inhibit them and thus favour movement in the currently facing direction. The mechanism is rather trivial, but the robustness of the emerging phenomena, whether for route following and nest pinpointing, is baffling. This model, in addition to being parsimonious, gain credence as it explains how aversive and appetitive visual memories are combined and captures particular movement signatures observed in ants [START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF].

Remarkably, keeping the same oscillatory model but swapping the eyes for olfactory organs, and providing a gradient of odour concentration instead of a visual world, leads to a very different but well-known behaviour: chemotaxis, that is, the ability to climb up odour gradients towards the odorous food source as observed in crawling maggots [START_REF] Wystrach | Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae[END_REF] (figure 1). Here as well, the model -now a model for chemotaxis -can readily explain how aversive and appetitive odour memories combine, and capture particular signatures observed in maggots. Notably, the agent, just like real larvae, may switch between two apparently different strategies, one where it approaches and circles around the source as if 'orbiting', and one where it displays a dense search by triggering strong turns and repeatedly crosses over the source [START_REF] Wystrach | Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae[END_REF][START_REF] Gomez-Marin | Active sampling and decision making in Drosophila chemotaxis[END_REF] (figure 1B). As with the ant model, this behavioural switch does not result from a top-down neural decision, but arise from a spontaneous change in the 'body movements-environment' contingency: when orbiting around the source, the larva agent experiences little changes of odour concentration, which trigger only small turns biased towards the odour side and thus sustains the orbiting activity; however when close to the odour source, the gradient tends to be sharper, increasing the probability that the agent displays sharper turns that break the orbiting trajectory. Crossing over the odour source (rather than circling around it) leads the larva to experience stronger upward and then downward changes of concentration, and thus increases the likelihood of going straight and then displaying a sharp turn after passing the odour source (figure 1B). The novel behavioural regime is thus self-sustained in a sort of positive feedback (i.e., crossing over exposes the agent to situation that favour crossing over behaviours), but given some noise, the agent may still switch back and forth between both regimes [START_REF] Wystrach | Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae[END_REF].

Traditionally, chemotaxis and route following are considered fundamentally different behaviours, with the former regarded as simple and primitive, while the latter viewed as more complex [START_REF] Wiener | Animal navigation. A synthesis[END_REF]. It is true that both behaviours look different, happen at different spatial scales, concern different sensory modalities and species, and involved different cognitive demand: learning and comparing visual scenes appears wholly more complex than following your nose. However, these apparent differences in behaviour conceal the remarkable similarity in the mechanism that produce them: the modulation of a lateral intrinsic oscillator [START_REF] Webb | Neural mechanisms of insect navigation[END_REF] (figure 1). Turns out, the similarity goes further. Ants and maggots do recruit homologous neural circuit to achieve these different looking behaviours. In theory, a larvae does not need the mushroom bodies to display chemotaxis, but in reality, they exploit the circuitry of this neuropil to learn which odour is worth following or not [START_REF] Eichler | The complete connectome of a learning and memory centre in an insect brain[END_REF][START_REF] Saumweber | Functional architecture of reward learning in mushroom body extrinsic neurons of larval Drosophila[END_REF]. It seems that larva's mushroom bodies continuously compares the currently perceived odour with previously learnt odours in a remarkably similar fashion than ants with visual scenes [START_REF] Webb | Neural mechanisms of insect navigation[END_REF]. The strong behavioural difference emerge not so much from difference in neural circuitry, but from the difference in the movement-to-sensory relationships of vision and olfaction: oscillating paths allow for simultaneous change in rotation and position, but while the perception of odour concentration is sensitive to change in position rather than rotation, it is the other way around for visual input [START_REF] Zeil | Catchment areas of panoramic snapshots in outdoor scenes[END_REF].

It is astonishing that meaningful but different behaviours can emerge from a same neural machinery, embodied in different sensory environments (figure 1). Over large evolutionary time scales, designs that are easily malleable may be favoured. Distributed embodied systems, when based on oscillatory movements, offer such malleability and appear to have provided a fertile ground for the evolution of novel behaviours.

Do discrete behaviours mean decisions?

When we design toy robots or video games, each specific action is typically triggered by one specific command such as 'jump', 'crouch' or 'run'; and it may feel intuitive that it should be so for animals too. One example of such discrete behaviour is the ants' actual scanning behaviour discussed earlier, where the insects suddenly stop to rotate on the spot [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF]. But could such clear-cut actions arise without clear-cut decisions? By implementing the previously mentioned oscillation models on a robot ant (i.e., a smartphone with wheels and a panoramic camera), and realising it within a natural landscape to see whether it could recapitulate a learnt route, Kodjabachev and Mangan were surprised to see their robot occasionally displaying scanning behaviours even thought there was no such command in the program [START_REF] Kodzhabashev | Route Following Without Scanning[END_REF]. The explanation is rather straightforward: in cluttered natural environments, the visual scene as perceived from the floor (at an ant's height) may become highly unfamiliar even after quite small displacements [START_REF] Philippides | How might ants use panoramic views for route navigation?[END_REF], and in Kodjabachev and Mangan's model, the more unfamiliar the scene, the more the robot's intrinsic oscillator is activated. Sudden perception of highly unfamiliar scenes strongly activates the oscillator and thus induce left and right turns that are so sharp that they physically override forward movement as the robot, like ants, will not drift sideways. That is, a scanning behaviour emerge. The apparent discreetness of the behaviour here does not result from a dedicated command, but from a non-linearity in the interaction between the robot and its sensory environment.

In drosophila larvae, distributed bodily interactions suffice to explains the emergence as well as the transitions between a large panels of stereotypical actions, such as forward and backward crawling motions, stops or 'head sweep' behaviours -a larva equivalent of scanning behaviours. These actions are mostly derived from the body structure itself, aided by local neural loops that trigger muscle contractions to supplement the movement [START_REF] Ross | A model of larval biomechanics reveals exploitable passive properties for efficient locomotion[END_REF][START_REF] Loveless | A Neuromechanical Model of Larval Chemotaxis[END_REF][START_REF] Loveless | Modelling the mechanics of exploration in larval Drosophila[END_REF]. The body dynamics at play are not at a stable equilibrium, and transition between actions result from unpredictable, chaotic dynamics in the system. As the result, the model larvae explore its environment through a diversity of behaviour and apparent decisions that matches the one observed in real larvae, despite the absence of any neural mechanisms for gathering sensory information or triggering actions [START_REF] Loveless | A Neuromechanical Model of Larval Chemotaxis[END_REF][START_REF] Loveless | Modelling the mechanics of exploration in larval Drosophila[END_REF]. Remarkably, real larvae with genetically silenced brain activity equally display their usual panel of behaviours in a manner and frequency that seems indistinguishable from intact larvae [START_REF] Berni | Autonomous circuitry for substrate exploration in freely moving Drosophila larvae[END_REF], proving that the natural transitions between discrete actions do not require brain decisions.

Obviously, signals from the brain do influence behaviour, for instance, by modulating the forward speed or the oscillations' amplitude as in the route following or chemotaxis models mentioned above. Discrete actions such as head-sweeps (or scans), stopping, or resuming forward motion after a pause can be strongly favoured by a strong or a weak signal [START_REF] Tastekin | Sensorimotor pathway controlling stopping behavior during chemotaxis in the Drosophila melanogaster larva[END_REF], but such signals should not be seen as discrete commands because 1-they are non-essentials for the action to emerge, 2-their consequence remain probabilistic and 3-they operate equally at lower range in a graded manner, [START_REF] Gomez-Marin | Multilevel control of run orientation in Drosophila larval chemotaxis[END_REF][START_REF] Ohashi | A novel behavioral strategy, continuous biased running, during chemotaxis in Drosophila larvae[END_REF], reflecting the continuous nature of the sensory environment. In other words, apparently clear cut behaviours do emerge without clear cut decisions.

'I know therefore I do' and 'I do therefore I know'

We have showcased how a navigating ant does not need to know whether or not it is close to its goal to engage in the correct behaviour; similarly, a larva does not even need a brain to execute a whole panel of actions. However, 'Does not need to know' does not imply 'Cannot know'. Quite the contrary, the fact that a relevant behaviour can emerge -without prior awareness -provides an additional opportunity for the system to gather knowledge from the situation, by monitoring not only the environment, but its own behavioural state.

Evidence shows that insects do monitor their own behavioural state -whether through proprioception or efference copy -to modulate various regions of their brain. For instance, whether a fly is resting still, walking or flying, will differentially activate neurons in multiple brain areas, such as motion vision neurons in the optic lobes [START_REF] Chiappe | Walking Modulates Speed Sensitivity in Drosophila Motion Vision[END_REF][START_REF] Maimon | Active flight increases the gain of visual motion processing in Drosophila[END_REF], direction sensitive neurons in the fan-shaped body of the central complex [START_REF] Weir | Functional divisions for visual processing in the central brain of flying Drosophila[END_REF], or perhaps more intriguingly, dopaminergic neurons conveying positive or negative reinforcement in the mushroom bodies [START_REF] Cohn | Coordinated and Compartmentalized Neuromodulation Shapes Sensory Processing in Drosophila[END_REF]. Not only: "I see danger therefore I'm flying away", but also: "I'm flying away therefore there might be danger". Both are probably true, and the system is surely most effective if cause and consequence are reverberating in a closed-loop (figure 2B).

In this way, movements during sequential inspections of visual patterns can provide a simple solution for bees to solve discrimination tasks or complex concept learning paradigms, eschewing the need to extract the shape or concept per se [START_REF] Guiraud | High-speed videography reveals how honeybees can turn a spatial concept learning task into a simple discrimination task by stereotyped flight movements and sequential inspection of pattern elements[END_REF][START_REF] Le Moël | A killjoy perspective on object representation by bumblebees[END_REF][START_REF] Vasas | Insect-inspired sequential inspection strategy enables an artificial network of four neurons to estimate numerosity[END_REF]. Similarly, human patients with visual agnosia may not recognise objects but can still perfectly adjust their grasping movements to seize them, as the action involves a different neural pathway [START_REF] Milner | The visual brain in action[END_REF]. We can see how monitoring their own movements could provide information about the size or orientation of the object, and thus transcend what is available to them through direct perception of the external world. Perhaps a most demonstrative example of the transcendental propriety of self-monitoring is path integration. During navigation, the ability for path integration is nothing else than monitoring one's own movements to estimate its position relative to a starting position. This internal estimation, in addition to enable returning home and displaying shortcuts, can serve as a scaffold for the establishment optimal routes passing through multiple goals, a behaviour that is usually assumed to involved a mental map or knowledge about the relative position between the goal themselves [START_REF] Le Moël | The Central Complex as a Potential Substrate for Vector Based Navigation[END_REF][START_REF] Cruse | No need for a cognitive map: decentralized memory for insect navigation[END_REF]. Whether insects do or do not possess this knowledge is another debate [START_REF] Cheeseman | Way-finding in displaced clock-shifted bees proves bees use a cognitive map[END_REF][START_REF] Cheung | Still no convincing evidence for cognitive map use by honeybees[END_REF], but what is certain, self-monitoring can bypass the need for complex reconstruction of the world.

What difference does it make to the system whether the informative signal comes from the environment via an external sense, or from the body via an internal sense or motor copy (figure 2B)? After all, the body can be viewed as a perceptual environment, one that can be particularly well controlled.

Is there a ghost in the machine?

The works presented here point at a distributed and dynamic nature of behavioural control (figure 2B), which contrast strongly with our intuitive sense of a cognitive being, in the sense of a higher awareness -or ghost in the machine -resulting from a global integration of information and producing top-down decision (figure 2A). However, both views are not necessarily mutually exclusive.

Perhaps a promising research for tackling the existence of such top-down decision is the study of attention-like processes, for which a 'selection' in the brain must precedes behavioural choices. There is strong evidence for such processes in insects [START_REF] Nityananda | Attention-like processes in insects[END_REF][START_REF] De Bivort | Evidence for selective attention in the insect brain[END_REF]. Insects can 'focus' on specific cues and 'disregard' others not only based on the current sensory cues themselves (which can be achieved through neural filtering and winner-takes-all competition, forming a so-called bottom-up control of attention) or their own behavioural state (previous paragraph), but also based on their past experiences and current internal state. For instance, a fly will recall olfactory memories and track an odour previously associated with food only if it is hungry [START_REF] Krashes | A neural circuit mechanism integrating motivational state with memory expression in Drosophila[END_REF]. Similarly, a larva will not bother recalling olfactory memories associated with sugar, if sugar is already present in the environment [START_REF] Schleyer | The impact of odor-reward memory on chemotaxis in larval Drosophila[END_REF]. Likewise, depending on its motivational state, an ant will recall either food-ward or homeward visual memories and disregard the other [START_REF] Harris | Ant navigation -Priming of visual route memories[END_REF][START_REF] Wehner | Ant navigation: One-way routes rather than maps[END_REF].

One key question is whether such 'internal' decisions result from local and specific gating mechanisms or from a global information integration. The latter could be implemented in brain-wide networks of highly connected neurons such as the so-called 'rich club', which we observe in human [START_REF] Van Den Heuvel | Rich-Club Organization of the Human Connectome[END_REF] but also animals including insects or nematodes' connectome [START_REF] Griffa | Rich-club neurocircuitry: function, evolution, and vulnerability[END_REF][START_REF] Towlson | The Rich Club of the C. elegans Neuronal Connectome[END_REF][START_REF] Shih | Connectomics-Based Analysis of Information Flow in the Drosophila Brain[END_REF]. However, evidence in insects rather support the former. For instance, neurons releasing the neuropeptide F, which convey specific homeostatic information such as lack of a given nutrient, modulate equally nutrient-specific dopaminergic neurons, thereby directly gating the learning and/or expression of the memories associated with this specific nutrient need [START_REF] Krashes | A neural circuit mechanism integrating motivational state with memory expression in Drosophila[END_REF][START_REF] Schleyer | Identification of Dopaminergic Neurons That Can Both Establish Associative Memory and Acutely Terminate Its Behavioral Expression[END_REF]. Some internal modulations have less specific targets: hormones and giant neurons innervating multiple regions can diffuse information throughout the brain to modulate behaviour [START_REF] Dacks | Octopamineimmunoreactive neurons in the brain and subesophageal ganglion of the hawkmoth Manduca sexta[END_REF][START_REF] Roeder | Octopamine in invertebrates[END_REF][START_REF] Hammer | Learning and memory in the honeybee[END_REF][START_REF] Perry | Unexpected rewards induce dopamine-dependent positive emotion-like state changes in bumblebees[END_REF]. But here also the information transmitted is more direct and situation specific than the product of a brain-wide like integration process; and the consequence on behaviour is more probabilistic than the result of a clear cut decision.

But then, what would the role of 'rich club' like brain-wide networks be? Instead of -or in addition to -'integration', these networks may be key for 'synchronisation' of neural activity across far-flung regions [START_REF] Van Den Heuvel | Rich-Club Organization of the Human Connectome[END_REF][START_REF] Towlson | The Rich Club of the C. elegans Neuronal Connectome[END_REF][START_REF] Shih | Connectomics-Based Analysis of Information Flow in the Drosophila Brain[END_REF], thereby imposing the coherence underlying attention [START_REF] Fries | Rhythms for Cognition: Communication through Coherence[END_REF] at a larger scale. Rather than decision centres, perhaps such networks can be viewed as blind orchestrators, crystallising the many local and specific 'decisions' achieved in different parts of the brain-bodyenvironment system into a globally co-ordinated, and thus meaningful, activity (figure 2B). This neural resonance may well support a rich subjective experience, and hence, a cognitive being. If that is the case, the cognitive being would be more the consequence of a democratic poll than a monarch imposing decisions.

Conclusion

The minimal models of insect navigation presented here illustrate the power of dynamic and closedloop processes, where behaviours arise from distributed interactions between brain, body, movements and environment, where the distinctions between internal or external source of information is blurred, and the notions of cause and consequence are supplanted by the one of emergence. These processes enable clear-cut actions and appropriate shift in behaviours to happen despite worryingly simple neural mechanisms, far removed from what we could call decisions. As a corollary, they provide insight into how novel behaviours can arise along ontogeny and evolution without much change in the underlying mechanisms. The motivation behind this approach is not to assume that insects are non-cognitive beings, but to seek the most parsimonious explanation behind these apparently complex phenomena, while departing as much as possible from our anthropomorphic intuitions [START_REF] Wystrach | What can we learn from studies of insect navigation?[END_REF]. However, one should keep in mind that it is our models that have simple brains, not the insects; and this approach is not mutually exclusive to a more cognitive view. On the contrary, the distributed mechanisms presented here form a formidable basis on which higher level decision-making could operate, as they produce meaningful behaviour that can be readily modulated given minimal variation in neural activity. Also, such distributed systems might need to be globally synchronised to be operational on a given task. Rather than a ghost in the machine taking decisions, perhaps it is the need for such a synchronisation, implemented through richly connected neural networks imposing a system-wide resonance, that may provoke what we could call a cognitive being. A variety of functionally relevant behaviours can arise from a same algorithm based on an intrinsic oscillator embodied in different sensorymovement contingency. A) Given panoramic visual scene as input, the algorithm (left) will result in route following or nest pinpointing behaviours depending on the movements that were achieved during previous learning stage. (Red dots: overall path taken by the agent in a realistic reconstructed visual world [START_REF] Stürzl | Three-dimensional models of natural environments and the mapping of navigational information[END_REF], as modelled in [START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF]). B) Given olfactory receptors able to detect change in odour concentration, the same neural algorithm (left) can produce two different chemotaxis behavioural regimes that self-sustain purely because of body movements-environment contingency. (blue dot: odour source; blue lines: isoclines of odour concentration; dark dots: overall path taken by the agent, as modelled in [START_REF] Wystrach | Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae[END_REF]). A,B) Both models can involve the same circuitry based on the insect's mushroom body [START_REF] Webb | Neural mechanisms of insect navigation[END_REF]. Changing the nature of the sensory input suffice to switch from visual navigation to chemotaxis behaviours.

Figure 2. Two non-mutually exclusive views of the flow of information underlying an organism's behaviour.

A) The first scheme highlights integration of external world information and subsequent top-down decisions. 'Rich-club' brain-wide neural networks may achieve a global integration of information to support a cognitive being that actively produce decisions (green). B) The second scheme emphasizes parallel and reverberating closed-loop processes, body/environment interactions as well as the distributed and self-emerging nature of decisions. A cognitive being may arise from a need for overall coherence of the system through synchronisation (and hence selection) of process activity, which could be achieved by 'rich-club' brain wide networks (in green).

Figure 1 .

 1 Figure 1. One neural mechanism, multiple behaviours.A variety of functionally relevant behaviours can arise from a same algorithm based on an intrinsic oscillator embodied in different sensorymovement contingency. A) Given panoramic visual scene as input, the algorithm (left) will result in route following or nest pinpointing behaviours depending on the movements that were achieved during previous learning stage. (Red dots: overall path taken by the agent in a realistic reconstructed visual world[START_REF] Stürzl | Three-dimensional models of natural environments and the mapping of navigational information[END_REF], as modelled in[START_REF] Möel | Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies[END_REF]). B) Given olfactory receptors able to detect change in odour concentration, the same neural algorithm (left) can produce two different chemotaxis behavioural regimes that self-sustain purely because of body movements-environment contingency. (blue dot: odour source; blue lines: isoclines of odour concentration; dark dots: overall path taken by the agent, as modelled in[START_REF] Wystrach | Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae[END_REF]). A,B) Both models can involve the same circuitry based on the insect's mushroom body[START_REF] Webb | Neural mechanisms of insect navigation[END_REF]. Changing the nature of the sensory input suffice to switch from visual navigation to chemotaxis behaviours.

Acknowledgments

I am grateful to Patrick Shultheiss and Cody Freas for polishing the writing of a previous version of this manuscript, as well as one anonymous reviewer for his helpful suggestions.

Funding

This work was funded by the European Research Council (ERCstg: EMERG-ANT 759817).


Basil El Jundi

Antoine Wystrach

Neuroecology of insects: when ultimate and proximate explanations unite

Neuroscience and ecology are traditionally two very different schools of research. Still today, each discipline is arguably represented by people with directly opposed points of view and daily routines.

Anybody can easily picture the neuroscientist in a white coat dissecting and exploring the brain substrate in the lab, or the field ecologist in rubber boots outdoors, claiming her/his holistic view of nature. Certainly, some truth is enshrined in these depictions; however, the more the neuroscientist explores the general principles of the brain in the lab, the more she/he needs to understand the ecologist's point of view (and vice versa). This is because the animal's habitat will inevitably influence the neuroarchitecture and activity of a brain throughout evolution; resulting in a neural machinery that both acts upon, and is crafted by, the animal's environment and goals. Today, we see a growing community of scientists that combine both neurobiology and ecology in their research.

The common points that bind these scientists are clear: the theory of evolution, behaviour, and a comparative approach. Together, this combination forms what can be called 'Neuroecology' [START_REF] Sherry | Neuroecology[END_REF].

The study of insects, with their wealth of species adequate to both lab and field studies, has proven to be a fertile ground for neuroecology. These tiny animals execute a remarkably large repertoire of behaviours in any kind of ecological habitat, proving that, despite the apparently conserved core design shared by all insects' brains, selective pressures can tweak these brains in ways that fit drastically different lifestyles, from flying visual predators to crawling olfaction-guided plant eaters [START_REF] Strausfeld | Arthropod Brains: Evolution, Functional Elegance, and Historical Significance[END_REF]. How does the insect brain conserve such a core design while simultaneously enabling the rapid evolution of diverse behaviours? How does the insect brain deal with rapid changes in the environment, such as the changes induced by humans? The aim of this issue is to provide several mini-reviews on the neuroecology of insects as diverse as bees, wasps, ants, dragonflies, and butterflies, to grasp the state of the art in the field and demonstrate the power of this integrative approach.

In the first article, Renou and Anton review how olfactory information is processed in the insect brainfrom the input stages in the antennae to the processing of information in the mushroom bodies [START_REF] Martin | The neurobiology of insect olfaction: Sensory processing in a comparative context[END_REF][START_REF] Hansson | Evolution of insect Olfaction[END_REF] but do so in the context of the species ecology. The processing of olfactory information is the most investigated, and arguably the predominant sensory modality in insects. In addition, the authors review our knowledge of how olfactory and visual information is combined in the brain and how pesticides and light pollution affect the coding of olfactory information.

In the second review, Haverkamp and Hans Smid exploit the long-term relationship between parasitoid wasps and their larval host [START_REF] Hoedjes | Natural variation in learning rate and memory dynamics in parasitoid wasps: opportunities for converging ecology and neuroscience[END_REF] to shed light on their respective neurophysiology. It turns out that both parties have evolved remarkably fine-tuned adaptations dedicated to beat the other.

Thus, mechanosensory receptors of the prey are tuned to the wing beat frequency of the predator, while specific neural pathways in the predator's brain enable the presence of host cues to act as appetitive reinforcer for memorising the current surroundings cues. The authors disentangle the adaptations based on the sequence of events happening in a natural encounter, from the distant search for the host to the final contact. The authors also show that the dynamic of learning involved at all stages is equally tuned to the other party's ecology and these differences can be traced right back to neurophysiological peculiarities observed in their respective brains. A co-evolutionary lens was clearly the right way to approach it! In addition to olfaction, visual information, such as colour information, is crucial for pollinators, such as butterflies, to recognize their host plants [START_REF] Van Der Kooi | Evolution of insect color vision: from spectral sensitivity to visual ecology[END_REF]. One of these butterflies, the swallowtail butterfly has been studied extensively in recent years both behaviourally and neurophysiologically.

In their article, Kinoshita and Stewart review and compare the visual systems of these butterflies to those of other pollinators. The results give insight into how butterflies use visual information for flower detection. In contrast to many other insects, the swallowtail butterfly uses four photoreceptors (uv, blue, green, red) for colour vision. In addition to colour, brightness and polarization vision are used to learn the visual qualities of their food source unambiguously, in a complex environment.

Neuroscience needs to diversify its model species [START_REF] Hale | Toward diversification of species models in neuroscience[END_REF], so why not butterflies? Butterflies are ideal candidates for study by neuroecologists, but are yet underutilised. This is the point made by Couto, Wainwright, Morris and Montgomery. Their review explains that butterflies have gone through a late evolutionary radiation, leading to an abundance of species showing various behavioural innovations adapted to specific ecological tasks. Despite the relatively recent origin of this radiation, butterflies show quite different sensory systems as well as brain morphology, which can thus be related precisely to each species' specific ecology. What's more, the authors argue that the number of genomic and neurobiological tools available in butterflies are growing. All the ingredients are in place to offer a rare opportunity to compare brain circuits in the light of ecology.

Baird, Tichit and Guiraud, also embarked on a comparative study, but this time across the flight styles of bees. Flight control has traditionally been studied in honeybees and bumblebees under laboratory conditions [START_REF] Srinivasan | How honeybees make grazing landings on flat surfaces[END_REF]. These hymenopterans have been used to derive the general strategies by which insects use the pattern of motion on the bee's retina, termed optic flow, to stabilize their flights, avoid collisions with objects in their environment, and execute their landings. The authors review the recent studies that have focused on other species of bee and found differences in the use of optic flow information for flight control that correlate well with of the animal's the visual ecology.

Notably, we now understand how and why some species perform kamikaze landings. The authors discuss the eye architecture of the different bee species, likely the key structure that allows the bees to fine-tune their natural navigational tasks.

Can we find differences in brain anatomy between insects that exhibit different ecological lifestyles, such as nocturnal vs. diurnal, migratory vs. non-migratory, or eusocial vs. solitary? Bouchebti and Arganda review, in detail, how the neural substrate correlates with the animal's ecology and how the brain is adapted to certain modus vivendi. The authors focus in particular on the first integration centres, the optic and antennal lobes, and point out that where strong differences in the neuroanatomy are found in these regions, they relate to differing ecologies [9.10]. While many studies have focused on the study of the sensory input stages in the brain, higher brain centres, such as the mushroom bodies and the central complex, have received little attention but might well be the focus of future studies, as technical advances allow a more accurate analysis of brain morphology.

Modern neurobiological tools yield a massive amount of data … however, focusing on the neural level only may not be a good strategy [START_REF] Krakauer | Neuroscience needs behavior: correcting a reductionist bias[END_REF]. Le Moël and Wystrach show convincingly that the field of insect navigation benefited a lot from combining neural data with ecology, and modelling. This field has been blessed with a strong ethological and modelling history, which led neuroscientists and computational scientists to investigate the 'navigational brain' as a dynamical system viewed through ecologically relevant goggles. From this perspective, behaviour is not a direct consequence of brain activity, but emerge from complex interactions between the brain, body and environment.

The authors demonstrate why such considerations are key to understand the mechanisms of behaviour.

The final chapter of this issue focuses on a stunning animal. Dragonflies are not only fascinating because they belong to the most ancient insect families on our planet and have large compound eyes that are equipped with up to 30,000 ommatidia but also because of their prey-catching behaviour.

However, what makes dragonflies such efficient predators? What kind of strategy do dragonflies employ to catch their prey and how is the neural substrate adapted to reliably track a prey during flight? Lancer et al. summarize our recent knowledge on the adaptation of the visual system and the behavioural strategies that allows these animals to catch their prey so accurately. Based on recent studies, adaptations of the dragonfly's compound eyes and the underlying neuronal network in the brain, combined with the ability to execute formidable manoeuvres during flight allows these animals to reach extremely high capture rates within complex environments [Olberg et al. 2017].

We are very grateful to all authors and reviewers for their contribution to this special issue. We hope it will inspire future works and young scientists in undertaking an approach that combine neuroscience with ecology to shed light on the richness of animal behaviours.