Long-duration animal tracking in difficult lighting conditions

A simplified courtship conditioning protocol to test learning and memory in Drosophila

In Drosophila, a male that has previously been sexually rejected reduces its courtship behavior when confronted again with an unreceptive female. This reduced courting time reflects a memory formation process. Here, we describe a simplified protocol to perform the courtship conditioning assay for assessing the reduced courting time, using regular lab equipment and handmade tools. Every step of the procedure, from raising flies and training to testing and quantification of this memory-related behavior, can be implemented in any practice laboratory.

Automatic counting of birds in a bird deterrence field trial

Decreasing costs in high-quality digital cameras, image processing, and digital storage allow researchers to generate and store massive amounts of digital imagery. The time needed to manually analyze these images will always be a limiting factor for experimental design and analysis. Implementation of computer vision algorithms for automating the detection and counting of animals reduces the manpower needed to analyze field images.For this paper, we assess the ability of computer vision to detect and count birds in images from a field test that was not designed for computer vision. Using video stills from the field test and Matlab's Computer Vision Toolbox, we designed and evaluated a cascade object detection method employing Haar and Local Binary Pattern feature types.Without editing the images, we found that the Haar feature can have a recall over 0.5 with an Intersection over Union threshold of 0.5. However, using this feature, 86% of the frames without birds had false-positive bird detections. Reducing the false positives could lead to these detection methods being implemented into a fully automated system for detecting and counting birds.Accurately detecting and counting birds using computer vision will reduce manpower for field experiments, both in experimental design and data analysis. Improvements in automated detection and counting will allow researchers to design extended trials without the added step of optimizing the experimental setup and/or captured images for computer vision.

Learning a Spatial Task by Trial and Error in Drosophila

The ability to use memory to return to specific locations for foraging is advantageous for survival. Although recent reports have demonstrated that the fruit flies Drosophila melanogaster are capable of visual cue-driven place learning and idiothetic path integration [1-4], the depth and flexibility of Drosophila's ability to solve spatial tasks and the underlying neural substrate and genetic basis have not been extensively explored. Here, we show that Drosophila can remember a reward-baited location through reinforcement learning and do so quickly and without requiring vision. After gaining genetic access to neurons (through 0273-GAL4) with properties reminiscent of the vertebrate medial forebrain bundle (MFB) and developing a high-throughput closed-loop stimulation system, we found that both sighted and blind flies can learn-by trial and error-to repeatedly return to an unmarked location (in a rectangularly shaped arena) where a brief stimulation of the 0273-GAL4 neurons was available for each visit. We found that optogenetic stimulation of these neurons enabled learning by employing both a cholinergic pathway that promoted self-stimulation and a dopaminergic pathway that likely promoted association of relevant cues with reward. Lastly, inhibiting activities of specific neurons time-locked with stimulation of 0273-GAL4 neurons showed that mushroom bodies (MB) and central complex (CX) both play a role in promoting learning of our task. Our work uncovered new depth in flies' ability to learn a spatial task and established an assay with a level of throughput that permits a systematic genetic interrogation of flies' ability to learn spatial tasks.

Sweet neurons inhibit texture discrimination by signaling TMC-expressing mechanosensitive neurons in Drosophila

Integration of stimuli of different modalities is an important but incompletely understood process during decision making. Here, we show that Drosophila are capable of integrating mechanosensory and chemosensory information of choice options when deciding where to deposit their eggs. Specifically, females switch from preferring the softer option for egg-laying when both options are sugar free to being indifferent between them when both contain sucrose. Such sucrose-induced indifference between options of different hardness requires functional sweet neurons, and, curiously, the Transmembrane Channel-like (TMC)-expressing mechanosensitive neurons that have been previously shown to promote discrimination of substrate hardness during feeding. Further, axons of sweet neurons directly contact axons of TMC-expressing neurons in the brain and stimulation of sweet neurons increases Ca²⁺ influx into axons of TMC-expressing neurons. These results uncover one mechanism by which Drosophila integrate taste and tactile information when deciding where to deposit their eggs and reveal that TMC-expressing neurons play opposing roles in hardness discrimination in two different decisions.

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

High Throughput Assay to Examine Egg-Laying Preferences of Individual Drosophila melanogaster

Recently, egg-laying preference of Drosophila has emerged as a genetically tractable model to study the neural basis of simple decision-making processes. When selecting sites to deposit their eggs, female flies are capable of ranking the relative attractiveness of their options and choosing the "greater of two goods." However, most egg-laying preference assays are not practical if one wants to take a systematic genetic screening approach to search for the circuit basis underlying this simple decision-making process, as they are population-based and laborious to set up. To increase the throughput of studying of egg-laying preferences of single females, we developed custom chambers that each can simultaneously assay egg-laying preferences of up to thirty individual flies as well as a protocol that ensures each female has a high egg-laying rate (so that their preference is readily discernable and more convincing). Our approach is simple to execute and produces very consistent results. Additionally, these chambers can be equipped with different attachments to allow video recording the egg-laying animals and to deliver light for optogenetics studies. This article provides the blueprints for fabricating these chambers and the procedure for preparing the flies to be assayed in these chambers.