How do backward walking ants (<i>Cataglyphis velox</i>) cope with navigational uncertainty?

Schwarz, Sebastian; Clément, Léo; Gkanias, Evripides; Wystrach, Antoine

doi:10.1101/2019.12.16.877704

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…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 66,71–73 as well as the regular need of insects to scan multiple directions 21,42,49,74,75 .…”

Section: Discussionsupporting

confidence: 65%

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

Wystrach

2023

Preprint

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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.

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Section: Discussionsupporting

confidence: 65%

“…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 46,73,78 , as well as the ants' ability to recognise views whatever their body orientation when on highly familiar regions 35 .…”

Section: Discussionmentioning

confidence: 99%

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

Wystrach

2023

Preprint

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“…Views facing the nest may as well be included during learning and categorised as left, right or both, explaining why most ants facing their goal usually choose to turn in one particular Revisiting current questions in insect and robot navigation such as early exploration, route following and homing 20,[46][47][48][49] ; the integration of aversive memories 8,24,50 , path integration and views ( [51][52][53][54] or other sensory modalities ( 55-58 as well as seeking for underlying neural correlates [5][6][7] -with such a lateralised design as a framework promises an interesting research agenda.…”

Section: Discussionmentioning

confidence: 99%

A lateralised design for the interaction of visual memories and heading representations in navigating ants

Wystrach¹,

Moël²,

Clément³

et al. 2020

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The navigational skills of ants, bees and wasps represent one of the most baffling examples of the powers of minuscule brains. Insects store long-term memories of the visual scenes they experience, and they use compass cues to build a robust representation of directions. We know reasonably well how long-term memories are formed, in a brain area called the Mushroom Bodies (MB), as well as how heading representations are formed in another brain area called the Central Complex (CX). However, how such memories and heading representations interact to produce powerful navigational behaviours remains unclear. Here we combine behavioural experiments with computational modelling that is strictly based on connectomic data to provide a new perspective on how navigation might be orchestrated in these insects. Our results reveal a lateralised design, where signals about whether to turn left or right are segregated in the left and right hemispheres, respectively. Furthermore, we show that guidance is a two-stage process: the recognition of visual memories, presumably in the MBs, does not directly drive the motor command, but instead updates a desired heading, presumably in the CX, which in turn is used to control guidance using celestial compass information. Overall, this circuit enables ants to recognise views independently of their body orientation, and combines terrestrial and celestial cues in a way that produces exceptionally robust navigation.

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“…Various insect navigational behaviour have been investigated with such an approach: path integration [14], the use of learnt terrestrial cues [15], wind and olfaction [16], [17], obstacle avoidance [18], route optimisation [19], [20], sequence learning [21], [22] or navigation backwards [23], [24]. Below, we focus on behavioural models of route following and visual homing in ants and bees, because these illustrate well the importance of considering the 'brain-body-environment' dynamics at play.…”

Section: Models From 'Behaviour' To 'Computation' (Level 3 <-> Level 2)mentioning

confidence: 99%

Towards a multi-level understanding in insect navigation

Moël

Wystrach

2020

Current Opinion in Insect Science

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 The field of insect navigation is a good example of the implementation of Marr's three levels of explanation.  It illustrates how important it is to consider an intermediary 'computational' level between neurons and behaviour.  Ethological background and computational modelling both have been key to characterize this intermediary level.  Recent descriptions of neural circuits can be well mapped to such identified computation level, rather than directly to behaviour.  This led to multi-level understandings of complex insect navigational behaviours, of which we provide examples here.

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How do backward walking ants (Cataglyphis velox) cope with navigational uncertainty?

Cited by 4 publications

References 37 publications

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

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

A lateralised design for the interaction of visual memories and heading representations in navigating ants

Towards a multi-level understanding in insect navigation

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