Image statistics of the environment surrounding freely behaving hoverflies

Velocity coding in the central brain of bumblebees

Moving animals experience wide-field optic flow due to the displacement of the retinal image during motion. These cues provide information about self-motion and are important for flight control and stabilization, and for more complex tasks like path integration. Although in honeybees and bumblebees the use of wide-field optic flow in behavioral tasks is well investigated, little is known about the underlying neuronal processing of these cues. Furthermore, there is a discrepancy between the temporal frequency tuning observed in most motion-sensitive neurons described so far from the optic lobe of insects and the velocity tuning that has been shown for many behaviors. Here, we investigated response properties of motion-sensitive neurons in the central brain of bumblebees. Extracellular recordings allowed us to present a large number of stimuli to probe the spatiotemporal tuning of these neurons. We presented moving gratings that simulated either front-to-back or back-to-front optic flow and found three response types. Direction-selective responses of one of the groups matched those of TN-neurons, which provide optic flow information to the central complex, whereas the other groups contained neurons with purely excitatory responses that were either selective or nonselective for stimulus direction. Most recorded units showed velocity-coding properties at lower angular velocities, but showed spatial frequency-dependent responses at higher velocities. Based on behavioral data, neuronal modeling work has previously predicted the existence of nondirection-selective neurons with such properties. Our data now provide physiological evidence for these neurons and show that neurons with TN-like properties exhibit a similar velocity-dependent coding.NEW & NOTEWORTHY Using extracellular recordings, we show that neurons in the central brain and central complex of bumblebees show velocity coding properties.

Acuity and summation strategies differ in vinegar and desert fruit flies

An animal's vision depends on terrain features that limit the amount and distribution of available light. Approximately 10,000 years ago, vinegar flies (Drosophila melanogaster) transitioned from a single plant specialist into a cosmopolitan generalist. Much earlier, desert flies (D. mojavensis) colonized the New World, specializing on rotting cactuses in southwest North America. Their desert habitats are characteristically flat, bright, and barren, implying environmental differences in light availability. Here, we demonstrate differences in eye morphology and visual motion perception under three ambient light levels. Reducing ambient light from 35 to 18 cd/m2 causes sensitivity loss in desert but not vinegar flies. However, at 3 cd/m2, desert flies sacrifice spatial and temporal acuity more severely than vinegar flies to maintain contrast sensitivity. These visual differences help vinegar flies navigate under variably lit habitats around the world and desert flies brave the harsh desert while accommodating their crepuscular lifestyle.

Facilitation of neural responses to targets moving against optic flow

Target detection in visual clutter is a difficult computational task that insects, with their poor spatial resolution compound eyes and small brains, do successfully and with extremely short behavioral delays. We here show that the responses of target selective descending neurons are attenuated by background motion in the same direction as target motion but facilitated by background motion in the opposite direction. This finding is important for understanding how target pursuit can occur in tandem with gaze stabilization. Indeed, the neural facilitation would come into effect if the hoverfly is subjected to background motion in one direction but the target it is pursuing moves in the opposite direction and could therefore be used to override gaze stabilizing corrective turns.

For the human observer, it can be difficult to follow the motion of small objects, especially when they move against background clutter. In contrast, insects efficiently do this, as evidenced by their ability to capture prey, pursue conspecifics, or defend territories, even in highly textured surrounds. We here recorded from target selective descending neurons (TSDNs), which likely subserve these impressive behaviors. To simulate the type of optic flow that would be generated by the pursuer’s own movements through the world, we used the motion of a perspective corrected sparse dot field. We show that hoverfly TSDN responses to target motion are suppressed when such optic flow moves syn-directional to the target. Indeed, neural responses are strongly suppressed when targets move over either translational sideslip or rotational yaw. More strikingly, we show that TSDNs are facilitated by optic flow moving counterdirectional to the target, if the target moves horizontally. Furthermore, we show that a small, frontal spatial window of optic flow is enough to fully facilitate or suppress TSDN responses to target motion. We argue that such TSDN response facilitation could be beneficial in modulating corrective turns during target pursuit.

Using virtual worlds to understand insect navigation for bio-inspired systems

Insects perform a wide array of intricate behaviors over large spatial and temporal scales in complex natural environments. A mechanistic understanding of insect cognition has direct implications on how brains integrate multimodal information and can inspire bio-based solutions for autonomous robots. Virtual Reality (VR) offers an opportunity assess insect neuroethology while presenting complex, yet controlled, stimuli. Here, we discuss the use of insects as inspiration for artificial systems, recent advances in different VR technologies, current knowledge gaps, and the potential for application of insect VR research to bio-inspired robots. Finally, we advocate the need to diversify our model organisms, behavioral paradigms, and embrace the complexity of the natural world. This will help us to uncover the proximate and ultimate basis of brain and behavior and extract general principles for common challenging problems.