Multielectrode Recordings From Identified Neurons Involved in Visually Elicited Escape Behavior

Cámera, Alejandro; Belluscio, Mariano; Tomsic, Daniel

doi:10.3389/fnbeh.2020.592309

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…To determine the specific spatial location of an approaching object, MLG1s may need to inhibit each other to win a competition. Although lateral inhibitions between MLG1 neurons in crabs are not confirmed, recent research suggested that there are connections between motion-sensitive neurons in the same lobula layer [e.g., MLG2 and BLG2 (Cámera et al, 2020)]. In this study, we propose a winner-take-all (WTA) FIGURE 4 | A Winner-take-all network for looming spatial localization.…”

Section: The Looming Spatial Localization Mechanismmentioning

confidence: 92%

A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice

Luan

Zhang

et al. 2022

Front. Neurosci.

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Similar to most visual animals, the crab Neohelice granulata relies predominantly on visual information to escape from predators, to track prey and for selecting mates. It, therefore, needs specialized neurons to process visual information and determine the spatial location of looming objects. In the crab Neohelice granulata, the Monostratified Lobula Giant type1 (MLG1) neurons have been found to manifest looming sensitivity with finely tuned capabilities of encoding spatial location information. MLG1s neuronal ensemble can not only perceive the location of a looming stimulus, but are also thought to be able to influence the direction of movement continuously, for example, escaping from a threatening, looming target in relation to its position. Such specific characteristics make the MLG1s unique compared to normal looming detection neurons in invertebrates which can not localize spatial looming. Modeling the MLG1s ensemble is not only critical for elucidating the mechanisms underlying the functionality of such neural circuits, but also important for developing new autonomous, efficient, directionally reactive collision avoidance systems for robots and vehicles. However, little computational modeling has been done for implementing looming spatial localization analogous to the specific functionality of MLG1s ensemble. To bridge this gap, we propose a model of MLG1s and their pre-synaptic visual neural network to detect the spatial location of looming objects. The model consists of 16 homogeneous sectors arranged in a circular field inspired by the natural arrangement of 16 MLG1s' receptive fields to encode and convey spatial information concerning looming objects with dynamic expanding edges in different locations of the visual field. Responses of the proposed model to systematic real-world visual stimuli match many of the biological characteristics of MLG1 neurons. The systematic experiments demonstrate that our proposed MLG1s model works effectively and robustly to perceive and localize looming information, which could be a promising candidate for intelligent machines interacting within dynamic environments free of collision. This study also sheds light upon a new type of neuromorphic visual sensor strategy that can extract looming objects with locational information in a quick and reliable manner.

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Section: The Looming Spatial Localization Mechanismmentioning

confidence: 92%

A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice

Luan

Zhang

et al. 2022

Front. Neurosci.

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“…Klapoetke et al, 2017;Ache et al, 2019b) and crabs (see e.g. Cámera et al, 2020). Additionally, our visual monitor was placed in front of the animal, thus biasing our results to neurons with frontal sensitivity.…”

Section: Dual Receptive Fieldsmentioning

confidence: 99%

Dual Receptive Fields Underlying Target and Wide-Field Motion Sensitivity in Looming-Sensitive Descending Neurons

Nicholas

Ogawa

Nordström

2023

eNeuro

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Responding rapidly to visual stimuli is fundamental for many animals. For example, predatory birds and insects alike have amazing target detection abilities, with incredibly short neural and behavioral delays, enabling efficient prey capture. Similarly, looming objects need to be rapidly avoided to ensure immediate survival, as these could represent approaching predators. Male Eristalis tenax hoverflies are non-predatory, highly territorial insects, that perform high-speed pursuits of conspecifics and other territorial intruders. During the initial stages of the pursuit the retinal projection of the target is very small, but this grows to a larger object before physical interaction. Supporting such behaviors, E. tenax and other insects have both target-tuned and loom-sensitive neurons in the optic lobes and the descending pathways. We here show that these visual stimuli are not necessarily encoded in parallel. Indeed, we describe a class of descending neurons that respond to small targets, to looming and to widefield stimuli. We show that these descending neurons have two distinct receptive fields where the dorsal receptive field is sensitive to the motion of small targets and the ventral receptive field responds to larger objects or widefield stimuli. Our data suggest that the two receptive fields have different pre-synaptic input, where the inputs are not linearly summed. This novel and unique arrangement could support different behaviors, including obstacle avoidance, flower landing, target pursuit or capture.Significance StatementIf you are playing baseball, when the ball is far away, it appears as a very small object on your retina. However, as the ball gets closer, its image becomes a rapidly expanding object. Here, we show that within the hoverfly visual system, a single neuron respond to both of these images. Indeed, we found a class of descending neurons with dual sensitivity, separated into two distinct parts of the visual field. The neurons have a more dorsal receptive field that is sensitive to small targets and a more ventral receptive field that is sensitive to larger objects.

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A Novel Neural Metric Based on Deep Boltzmann Machine

Yang

Liu

2022

Neural Process Lett

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Multielectrode Recordings From Identified Neurons Involved in Visually Elicited Escape Behavior

Cited by 5 publications

References 48 publications

A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice

A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice

Dual Receptive Fields Underlying Target and Wide-Field Motion Sensitivity in Looming-Sensitive Descending Neurons

A Novel Neural Metric Based on Deep Boltzmann Machine

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