Towards a multi-level understanding in insect navigation

Moël, Florent Le; Wystrach, Antoine

doi:10.1016/j.cois.2020.10.006

Cited by 14 publications

(16 citation statements)

References 86 publications

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“…Our understanding of the neural underpinnings of insect navigation has tremendously increased in the last decade 52 , and notably, regarding how views are memorised and associated to specific valence in the insects' mushroom body 53 – 58 . It is therefore possible and useful to see whether and how our current behavioural results can be interpreted in the light of this neural framework.…”

Section: Discussionmentioning

confidence: 99%

Aversive view memories and risk perception in navigating ants

Freas

Wystrach

Schwarz

2022

Sci Rep

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Many ants establish foraging routes through learning views of the visual panorama. Route models have focused primarily on attractive view use, which experienced foragers orient towards to return to known sites. However, aversive views have recently been uncovered as a key component of route learning. Here, Cataglyphis velox rapidly learned aversive views, when associated with a negative outcome, a period of captivity in vegetation, triggering increases in hesitation behavior. These memories were based on the accumulation of experiences over multiple trips with each new experience regulating forager hesitancy. Foragers were also sensitive to captivity time differences, suggesting they possess some mechanism to quantify duration. Finally, we analyzed foragers' perception of risky (i.e. variable) versus stable aversive outcomes by associating two sites along the route with distinct captivity schedules, a fixed or variable duration, with the same mean across training. Foragers exhibited fewer hesitations in response to risky outcomes compared to fixed ones, indicating they perceived risky outcomes as less severe. Results align with a logarithmic relationship between captivity duration and hesitations, suggesting that aversive stimulus perception is a logarithm of its actual value. We discuss how aversive view learning could be executed within the mushroom bodies circuitry following a prediction error rule.

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Section: Discussionmentioning

confidence: 99%

Aversive view memories and risk perception in navigating ants

Freas

Wystrach

Schwarz

2022

Sci Rep

Self Cite

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“…Adaptive behavior is an interesting phenomenon for not only biologists but also engineering researchers, and they have formulated methods to elucidate the information-processing mechanisms that may help reconstruct adaptive behavior that can be used to control a robot. (1,2) In particular, researchers have actively investigated adaptive behavior in localization, (3,4) which is a fundamental behavior in animals.…”

Section: Introductionmentioning

confidence: 99%

Animal-in-the-loop System with Multimodal Virtual Reality to Elicit Natural Olfactory Localization Behavior

Shigaki¹,

Minakawa²,

Yamada³

et al. 2021

Sensors and Materials

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Herein, we proposed an "animal-in-the-loop" (AIL) system by introducing a robot and a virtual reality (VR) technology in a conventional insect behavior experiment. The setup provided sensory inputs to an insect, which mimic its natural environment and simultaneously measured the behavioral output. The proposed AIL system consisted of a multimodal VR device and a ground-running robot, both of which were connected wirelessly. The insect behavior was measured using a multimodal VR device, and the behavioral changes were transmitted to the robot as control inputs. Specifically, the multimodal VR device was equipped with three types of sensory stimulators, odor, wind, and vision, and each stimulator was controlled by the value of the corresponding sensor on the robot. The surrounding environment was observed using multiple sensors mounted on the robot, and the information was transmitted to the VR device to provide sensory stimuli to the insect. This system allowed the insect on the VR device to remotely control the robot and perform localization virtually. The localization trajectories of the proposed AIL system were similar to those of the free-walking experiment, and the tendency of the change in the heading angle during localization was also similar. Therefore, we found that using the AIL system enabled us to measure behavioral changes upon providing sensory stimuli to insects. These VR stimuli were similar to those encountered by the insect in free-walking experiments.

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“…Such learning and optimization based models are normative in the sense that they can prescribe how a neural system should behave, rather than describing how it has been observed to behave. As neuroscience moves towards studying increasingly naturalistic behaviors [Gomez-Marin and Ghazanfar, 2019;Huk et al, 2018;Nastase et al, 2020;Sonkusare et al, 2019], such normative approaches are gaining traction as tools to gain insight, rapidly explore hypotheses, and generate ideas for theoretical development [Ahrens, 2019;Banino et al, 2018;Colabrese et al, 2017;Le Moël and Wystrach, 2020;Merel et al, 2019;Richards et al, 2019;Verma et al, 2018].…”

Section: Introductionmentioning

confidence: 99%

Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes

Singh¹,

Breugel²,

N.³

et al. 2021

Preprint

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Tracking a turbulent plume to locate its source is a complex control problem because it requires multi-sensory integration and must be robust to intermittent odors, changing wind direction, and variable plume statistics. This task is routinely performed by flying insects, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behavior have been studied in detail in many experimental studies. Here, we take a complementary in silico approach, using artificial agents trained with reinforcement learning to develop an integrated understanding of the behaviors and neural computations that support plume tracking. Specifically, we use deep reinforcement learning (DRL) to train recurrent neural network (RNN) agents to locate the source of simulated turbulent plumes. Interestingly, the agents' emergent behaviors resemble those of flying insects, and the RNNs learn to represent task-relevant variables, such as head direction and time since last odor encounter. Our analyses suggest an intriguing experimentally testable hypothesis for tracking plumes in changing wind direction-that agents follow local plume shape rather than the current wind direction. While reflexive short-memory behaviors are sufficient for tracking plumes in constant wind, longer timescales of memory are essential for tracking plumes that switch direction. At the level of neural dynamics, the RNNs' population activity is low-dimensional and organized into distinct dynamical structures, with some correspondence to behavioral modules. Our in silico approach provides key intuitions for turbulent plume tracking strategies and motivates future targeted experimental and theoretical developments.

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Towards a multi-level understanding in insect navigation

Cited by 14 publications

References 86 publications

Aversive view memories and risk perception in navigating ants

Aversive view memories and risk perception in navigating ants

Animal-in-the-loop System with Multimodal Virtual Reality to Elicit Natural Olfactory Localization Behavior

Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes

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