Multielectrode Recordings From Identified Neurons Involved in Visually Elicited Escape Behavior

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

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 nonpredatory, 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 wide-field 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 wide-field stimuli. Our data suggest that the two receptive fields have different presynaptic input, where the inputs are not linearly summed. This novel and unique arrangement could support different behaviors, including obstacle avoidance, flower landing, and target pursuit or capture.

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

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.