Neural basis of forward flight control and landing in honeybees.

The impressive repertoire of honeybee visually guided behaviors, and their ability to learn has made them an important tool for elucidating the visual basis of behavior. Like other insects, bees perform optomotor course correction to optic flow, a response that is dependent on the spatial structure of the visual environment. However, bees can also distinguish the speed of image motion during forward flight and landing, as well as estimate flight distances (odometry), irrespective of the visual scene. The neural pathways underlying these abilities are unknown. Here we report on a cluster of descending neurons (DNIIIs) that are shown to have the directional tuning properties necessary for detecting image motion during forward flight and landing on vertical surfaces. They have stable firing rates during prolonged periods of stimulation and respond to a wide range of image speeds, making them suitable to detect image flow during flight behaviors. While their responses are not strictly speed tuned, the shape and amplitudes of their speed tuning functions are resistant to large changes in spatial frequency. These cells are prime candidates not only for the control of flight speed and landing, but also the basis of a neural 'front end' of the honeybee's visual odometer.

Mimicking honeybee eyes with a 280 degrees field of view catadioptric imaging system.

We present a small single camera imaging system that provides a continuous 280 degrees field of view (FOV) inspired by the large FOV of insect eyes. This is achieved by combining a curved reflective surface that is machined into acrylic glass with lenses covering the frontal field that otherwise would have been obstructed by the mirror. Based on the work of Seidl (1982 PhD Thesis Technische Hochschule Darmstadt), we describe an extension of the 'bee eye optics simulation' (BEOS) model by Giger (1996 PhD Thesis Australian National University) to the full FOV which enables us to remap camera images according to the spatial resolution of honeybee eyes. This model is also useful for simulating the visual input of a bee-like agent in a virtual environment. The imaging system in combination with our bee eye model can serve as a tool for assessing the visual world from a bee's perspective which is particularly helpful for experimental setups. It is also well suited for mobile robots, in particular on flying vehicles that need light-weight sensors.

Rugged, obstruction-free, mirror-lens combination for panoramic imaging.

We present a new combination of lenses and reflective surfaces for obstruction-free wide-angle imaging. The panoramic imaging system consists of a reflective surface machined into solid Perspex, which together with an embedded lens, can be attached to a video camera lens. Unlike vision sensors with a single mirror mounted in front of a camera, the view in the forward direction (i.e., the direction of the optical axis) is not obstructed. Light rays contributing to the central region of the image are refracted at a centrally positioned lens and at the Perspex enclosure. For the outer image region, rays are reflected at a mirror surface of constant angular gain machined into the Perspex and coated with silver. The design produces a field of view of approximately 260 degrees with only a small separation of viewpoints. The shape of the enclosing Perspex is specifically designed in order to minimize internal reflections.

Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths.

The retinal image flow a blowfly experiences in its daily life on the wing is determined by both the structure of the environment and the animal's own movements. To understand the design of visual processing mechanisms, there is thus a need to analyse the performance of neurons under natural operating conditions. To this end, we recorded flight paths of flies outdoors and reconstructed what they had seen, by moving a panoramic camera along exactly the same paths. The reconstructed image sequences were later replayed on a fast, panoramic flight simulator to identified, motion sensitive neurons of the so-called horizontal system (HS) in the lobula plate of the blowfly, which are assumed to extract self-motion parameters from optic flow. We show that under real life conditions HS-cells not only encode information about self-rotation, but are also sensitive to translational optic flow and, thus, indirectly signal information about the depth structure of the environment. These properties do not require an elaboration of the known model of these neurons, because the natural optic flow sequences generate--at least qualitatively--the same depth-related response properties when used as input to a computational HS-cell model and to real neurons.