Multi-regional circuits underlying visually guided decision-making in Drosophila

Cheong, Han S J; Siwanowicz, Igor; Card, Gwyneth M

doi:10.1016/j.conb.2020.10.010

Cited by 26 publications

(23 citation statements)

References 107 publications

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“…The LC10 axon terminals also feature prominent retinotopic organization within the AOTU, which is quite different from the other optic glomeruli (Wu et al, 2016). Furthermore, LC10 neurons are critical for visual tracking during courtship behavior (Ribeiro et al, 2018), whereas other LCs are involved in locomotor behaviors such as walking forward (Bidaye et al, 2020), turning (Wu et al, 2016), freezing (Zacarias et al, 2018, or escape takeoff (Cheong et al, 2020). Future work should evaluate whether LC10 pathways ultimately converge with neurons downstream of the other LCs to coordinate diverse behaviors guided by these different visual pathways.…”

Section: Articlementioning

confidence: 99%

A functionally ordered visual feature map in the Drosophila brain

Klapoetke

Nern

Rogers

et al. 2022

Neuron

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Section: Articlementioning

confidence: 99%

A functionally ordered visual feature map in the Drosophila brain

Klapoetke

Nern

Rogers

et al. 2022

Neuron

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“…R. Soc. B 288: 20202375 escape responses in Drosophila [49][50][51]). Here, we might predict that mechanosensory input from the halteres can be transmitted to the legs to induce fast escapes.…”

Section: Discussionmentioning

confidence: 99%

Takeoff diversity in Diptera

et al. 2021

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The order Diptera (true flies) are named for their two wings because their hindwings have evolved into specialized mechanosensory organs called halteres. Flies use halteres to detect body rotations and maintain stability during flight and other behaviours. The most recently diverged dipteran monophyletic subsection, the Calyptratae, is highly successful, accounting for approximately 12% of dipteran diversity, and includes common families like house flies. These flies move their halteres independently from their wings and oscillate their halteres during walking. Here, we demonstrate that this subsection of flies uses their halteres to stabilize their bodies during takeoff, whereas non-Calyptratae flies do not. We find that flies of the Calyptratae are able to take off more rapidly than non-Calyptratae flies without sacrificing stability. Haltere removal decreased both velocity and stability in the takeoffs of Calyptratae, but not other flies. The loss of takeoff velocity following haltere removal in Calyptratae (but not other flies) is a direct result of a decrease in leg extension speed. A closely related non-Calyptratae species ( D. melanogaster ) also has a rapid takeoff, but takeoff duration and stability are unaffected by haltere removal. Haltere use thus allows for greater speed and stability during fast escapes, but only in the Calyptratae clade.

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“…Currier et al 48 studied the direction behavior control of the fly during ongoing behavior, and noticed that flies exhibited two behaviors. Cheong et al 49 discussed how multiregional circuits guided flies to make visual decision. They claimed that goal‐directed decisions might occur through memory, navigation and valence processing in the central complex.…”

Section: Related Research Workmentioning

confidence: 99%

Fly visual evolutionary neural network solving large‐scale global optimization

Zhang

Xiao

Qin

2021

Int J of Intelligent Sys

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Neurophysiologic achievements claimed that the fly visual system could naturally contribute to a type of artificial computation model which used motion‐sensitive neurons to detect the local movement direction changes of moving objects. It, however, still remains open how the neurons' information‐processing mechanisms and the inspirations of swarm intelligence can be integrated to serve an interdisciplinary topic between computer vision and intelligence optimization‐visual evolutionary neural networks. Hereby, a fly visual evolutionary neural network is developed to solve large‐scale global optimization (LSGO), inspired by swarm evolution and the characteristics of fly visual perception. It includes two functional modules, of which one is to generate global and local motion direction activities of visual neural nodes, and the other takes the activities as learning rates to update the nodes' states by a population‐like evolutionary strategy. Also, it is used to optimize the structure of a multilayer perceptron to acquire a sample classification model. The theoretical results indicate that the network is convergent and meanwhile the computational complexity mainly depends on the size of the input layer and the dimension of LSGO. The comparative experiments have verified that the network is an extremely competitive optimizer for LSGO problems.

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Multi-regional circuits underlying visually guided decision-making in Drosophila

Cited by 26 publications

References 107 publications

A functionally ordered visual feature map in the Drosophila brain

A functionally ordered visual feature map in the Drosophila brain

Takeoff diversity in Diptera

Fly visual evolutionary neural network solving large‐scale global optimization

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