Here we describe how to optimize the acquired video image for an olfactory magnetic-tether (OMT) apparatus. We also describe two sample experimental protocols for studying visuo-olfactory fusion.
Flying insects use visual cues to stabilize their heading in a wind stream. Many animals additionally track odors carried in the wind. As such, visual stabilization of upwind tracking directly aids in odor tracking. But do olfactory signals directly influence visual tracking behavior independently from wind cues? Additionally, recent advances in olfactory molecular genetics and neurophysiology have motivated novel quantitative behavioral analyses to assess the behavioral influence of (e.g.) genetically inactivating specific olfactory activation circuits. We modified a magnetic tether system originally devised for vision experiments by equipping the arena with narrow laminar flow odor plumes. Here we focus on experiments that can be performed after a fly is tethered and is able to navigate in the magnetic arena. We show how to acquire video images optimized for measuring body angle, how to judge stable odor tracking, and we illustrate two experiments to examine the influence of visual cues on odor tracking.
The OMT is an adaptation of a magnetic tether system 1 designed to incorporate a virtual plume simulator 2. The following protocols will explain how to optimize the acquired video images (Part 1) and run two types of basic visuo-olfactory experiments (Part 2 & 3).
Part 1: Video Acquisition
Optimizing the video image is crucial for the quality of the acquired data. Follow these steps for cleaner images.
Part 2: The fly finding the odor plume
One simple experiment in the OMT is observing a hungry (starved) fly locating and then actively tracking a plume of attractive food odor. Manipulating the visual surroundings influences the fly’s ability to robustly track an appetitive food odor. Note that experiments are typically run in a random block format to minimize any bias in experimental order. All external hardware (gas multiplexers, LED arena, video acquisition) are controlled by custom software routines written in Matlab (Mathworks).
Part 3: The fly remaining within the odor plume
A second basic experiment in the OMT is to visually drag a fly into an odor plume, instantaneously change the visual conditions, and then measure its ability to remain in the plume. Again, note that all experiments are done in a random block format to minimize any bias in experimental order.
Representative Results:
Figure 1
Part 1 explains how to optimize the OMT video images. The video image should have a clear view of the illuminated fly on a dark black background. Overly bright images can be improved by reducing background interference, reducing the intensity of the IR LEDs, or by turning off the room lights. If the fly is improperly illuminated, adjusting the focus of the IR LEDs or modulating the intensity of the IR LEDs may fix the problem. Parts 2 & 3 describe simple experiments to conduct to verify the proper operation of an OMT. In all cases, a fly should be visually “forced” (via optomotor responses) to rotate several revolutions in the arena to ensure a smooth and complete range of motion. In Part 2 a fly is challenged to find an odor plume on its own. To keep the fly engaged in the experiment, to reduce the effect of residual odor release, and to keep flies from maintaining a single heading throughout the experiment, it is useful to visually rotate the fly for a few seconds between trials. A fly should never localize a water vapor plume to a significant degree, and should always localize an odor plume in the presence of rich panoramic visual cues. In Part 3 a fly is visually dragged directly into an odor plume and challenged to maintain its heading within the plume against a variety of stationary visual backgrounds. If the fly is dragged into a water vapor plume, it should quickly turn away. If the fly is dragged into an odor plume and there are sufficient visual cues to mediate robust tracking, the fly will remain in the plume.
The image acquisition for this system utilizes an inexpensive firewire board camera and software written in Matlab. Clear images are crucial for obtaining accurate data traces. The strategies described above are useful for cleaning up images in this system. Other video tracking procedures for this system have been described 1. Furthermore, this system could easily be modified to include real-time video tracking. The example experimental protocols described here meant to be a starting point to ensure the proper operation of an OMT. It is possible to experiment with different visual displays and even different odors.
Funded by a grant from the National Science Foundation to MF
Material Name | Typ | Company | Catalogue Number | Comment |
---|---|---|---|---|
Firewire camera | 1394store.com | Fire-I board camera BW | 4.3mm lens no IR coating | |
IR LEDs | Small Parts | |||
Black spray paint | Rustoleum | Flat black | ||
Black flock paper | Edmund Scientific | |||
Panel system | Caltech | 3 | ||
Matlab 2006a | Mathworks | Image acquisition toolbox |