Jumpei Matsumoto has submitted the following to OpenBehavior regarding 3D tracker, a 3D video tracking system for animal behavior.
3DTracker-FAB is an open source software for 3D-video based markerless computerized behavioral analysis for laboratory animals (currently mice and rats). The software uses multiple depth cameras to reconstruct full 3D images of animals and fit skeletal models to the 3D image to estimate 3D pose of the animals.
More information on 3D tracker may be found on the system’s website, www.3dtracker.org
Additionally, a dynamic poster on the system will be presented on November 12th at the Society for Neuroscience annual meeting. Click here for more information.
Greg Silas, from the University of Ottawa, has kindly contributed the following to OpenBehavior.
“Silasi et al developed a low-cost system for fully autonomous training of group housed mice on a forelimb motor task. We demonstrate the feasibility of tracking both end-point as well as kinematic performance of individual mice, each performing thousands of trials over 2.5 months. The task is run and controlled by a Raspberry Pi microcomputer, which allows for cages to be monitored remotely through an active internet connection.”
The DropBox folder containing the python code may be found here.
Andreas Genewsky, from the Max-Planck Institute of Psychiatry, has generously shared the following regarding his Moving Wall Box task and associated apparatus.
“Typicallly, behavioral paradigms which aim to asses active vs. passive fear responses, involve the repeated application of noxius stimuli like electric foot shocks (step-down avoidance, step-through avoidance, shuttle-box). Alternative methods to motivate the animals and ultimately induce a conflict situation which needs to be overcome often involve food and/or water deprivation.
In order to repeatedly assess fear coping strategies in an emotional challenging situation without footshocks, food or water deprivation (comlying to the Reduce & Refine & Replace 3R principles), we devised a novel testing strategy, henceforward called the Moving Wall Box (MWB) task. In short, during the MWB task a mouse is repeatedly forced to jump over a small ice-filled box (10 trials, 1 min inter-trial intervals ITI), by slowly moving walls (2.3 mm/s, over 60 s), whereby the presence of the animal is automatically sensed via balances and analyzed by a microcontroller board which in turn controls the movements of the walls. The behavioral readouts are (1) the latency to reach the other compartment (high levels of behavioral inhibition lead to high latencies) and (2) the number of inter-trial shuttles per trial (low levels of behavioral inhibition lead to high levels of shuttles during the ITI).
The MWB offers the possibility to conduct simultaneous in vivo electrophysiological recordings, which could be later aligned to the behavioral responses (escapes). Therefore the MWB task fosters the study of activity patterns in, e.g., optogenetically identified neurons with respect to escape responses in a highly controlled setting. To our knowledge there is no other available compatible behavioral paradigm.”
Researchers at the National Eye Institute and the University of Oldenberg, Germany, have developed the OMR-arena for measuring visual acuity in mice.
The OMR-arena is an automated measurement and stimulation system that was developed to determine visual thresholds in mice. The system uses an optometer to characterize the visual performance of mice in a free moving environment. This system uses a video-tracking system to monitor the head movement of mice while presenting appropriate 360° stimuli. The head tracker is used to adjust the desired stimulus to the head position, and to automatically calculate visual acuity. This device, in addition to being open-source and affordable, offers an objective way for researchers to measure visual performance of free moving mice.
Jesús Ballesteros, Ph.D. (Learning and Memory Research Group, Department of Neurophysiology at Ruhr-University Bochum, Germany) has generously his project, called Autoreward2, with OpenBehavior. Autoreward2 is an Elegoo Uno-based system designed to detect and reward rodents in a modified T-maze task.
In designing their modified T-maze, Ballesteros found the need for an automatic reward delivery system. Using open-source resources, he aimed to create a system with the following capabilities:
Detect an animal at a certain point in a maze;
Deliver a certain amount of fluid through the desired licking port;
Provide visual cues that indicate which point in the maze has been reached;
Allow different modes to be easily selected using an interface;
Allow different working protocols (i.e., habituation, training, experimental, cleaning)
To achieve these aims, he created used an Elegoo UNO R3 board to read inexpensive infrared emitters. Breaking any infrared beam causes the board to open one or two solenoid valves connected to a fluid tank. The valve remains open for around 75 milliseconds, allowing a single drop of fluid to form at the tip of the licking port. Additionally, the bread board contains LEDS to signal to the researcher when the IR beam has been crossed.
Currently, a membrane keypad allows different protocols or modes to be selected. The system is powered through a 9V wall adapter, providing 3.3V to the LEDs and IR circuits and 9V to the solenoids.
Importantly, the entire system can be built for under 80€. In the future, Ballesteros hopes to add a screen and an SD card port, and to switch the keypad out for a wireless interface.
Andre Chagas, creator of OpenNeuroscience, has generously shared the following with OpenBehavior regarding an arduino-based, 3D-printed nose poke device:
“This nose poke device was built as “proof-of-principle”. The idea was to show that scientists too can leverage from the open source philosophy and the knowledge built by the community that is developing around open source hardware. Moreover, the bill of materials was kept simple and affordable. One device can be built for ~25 dollars and should take 2-3 hours to build, including the time to print parts.
The device is organised as follows: The 3D printed frame (which can also be built with other materials when a printer is not available) contains a hole where the animals are expected to insert their snouts. At the front part of the hole, an infrared led is aligned with an infrared detector. This forms an “infrared curtain” at the hole’s entrance. If this curtain is interrupted, a signal is sent to a microcontroller (an Arduino in this case), and it can be used to trigger other electronic components, such as a water pump, or an led indicator, or in this case a Piezo buzzer.
At the back of the hole, a white LED is placed to indicate that the system is active and ready for “nose pokes”.
The microcontroller, contains the code responsible for controlling the electronic parts, and can easily be changed, as it is written for Arduino and several code examples/tutorials (for begginners and experts) can be found online.”
Robert Sachdev, from the Neurocure Cluster of Excellence, Humboldt Universität Zu Berlin, Germany, has generously shared the following regarding automated optical tracking of animal movement:
“We have developed a method for tracking the motion of whiskers, limbs and whole animals in real-time. We show how to use a plug and play Pixy camera to monitor the real-time motion of multiple colored objects and apply the same tools for post-hoc analysis of high-speed video. Our method has major advantages over currently available methods: we can track the motion of multiple adjacent whiskers in real-time, and apply the same methods post-hoc, to “recapture” the same motion at a high temporal resolution. Our method is flexible; it can track objects that are similarly shaped like two adjacent whiskers, forepaws or even two freely moving animals. With this method it becomes possible to use the phase of movement of particular whiskers or a limb to perform closed-loop experiments.”
Figure using Pixy for two adjacent whiskers A. Setup. Head-fixed mice are acclimatized to whisker painting, and trained to use their whiskers to contact a piezo-film touch sensor. A Pixy camera is used to track whiskers in real-time (left), a high-speed color camera is used simultaneously to acquire data. B. Paradigm for whisker task. A sound-cue initiates the trial. The animal whisks one of the two painted whiskers into contact with a piezo-film sensor and if contact reaches threshold, the animal obtains a liquid reward. There is a minimum inter-trial interval of 10 seconds. C, Capturing whisker motion in real-time. The movement and location of the D1 and D2 whiskers shown at two consecutive time points (20 ms apart, left & right images). Lines corresponding to the location of the two whiskers (middle panel) acquired with Spike2 software. The waveform of whisker data reflects the spatial location and the dimensions of the tracked box around the whisker, which can both change as the whisker moves
David Barker from the National Institute on Drug Abuse Intramural Research Program has shared the following regarding the development of a device designed to allow the automatic detection of 50kHz ultrasonic vocalizations.
Ultrasonic vocalizations (USVs) have been utilized to infer animals’ affective states in multiple research paradigms including animal models of drug abuse, depression, fear or anxiety disorders, Parkinson’s disease, and in studying neural substrates of reward processing. Currently, the analysis of USV data is performed manually, and thus time consuming.
The present method was developed in order to allow for the automated detection of 50-kHz ultrasonic vocalizations using a template detection procedure. The detector runs in XBAT, an extension developed for MATLAB developed by the Bioacoustics Research Program at Cornell University. The specific template detection procedure for ultrasonic vocalizations along with a number of companion tools were developed and tested by our laboratory. Details related to the detector’s performance can be found within our published work and a detailed readme file is published along with the MATLAB package on our GitHub.
Our detector was designed to be freely shared with the USV research community with the hope that all members of the community might benefit from its use. We have included instructions for getting started with XBAT, running the detector, and developing new analysis tools. We encourage users that are familiar with MATLAB to develop and share new analysis tools. To facilitate this type of collaboration, all files have been shared as part of a GitHub repository, allowing for suggested changes or novel contribution to be made to the software package. I would happily integrate novel analysis tools created by others into future releases of the detector.
Work on a detector for 22-kHz vocalizations is ongoing; the technical challenges for detecting 22-kHz vocalizations, which are nearer to audible noise, are more difficult. Those interested in contributing to this can email me at djamesbarker@gmail-dot-com or find me on twitter (@DavidBarker_PhD).
The Feeding Experimentation Device (FED) is a free, open-source system for measuring food intake in rodents. FED uses an Arduino processor, a stepper motor, an infrared beam detector, and an SD card to record time-stamps of 20mg pellets eaten by singly housed rodents. FED is powered by a battery, which allows it to be placed in colony caging or within other experimental equipment. The battery lasts ~5 days on a charge, providing uninterrupted feeding records over this duration. The electronics for building each FED cost around $150USD, and the 3d printed parts cost between $20 and $400, depending on access to 3D printers and desired print quality.
The Kravitz lab has published a large update of their Feeding Experimentation Device (FED) to their Github site (https://github.com/KravitzLab/fed), including updated 3D design files that print more easily and updates to the code to dispense pellets more reliably. Step-by-step build instructions are available here: https://github.com/KravitzLab/fed/wiki
The Laubach Lab at American University investigates executive control and decision making, focusing on the role of the prefrontal cortex. Through their GitHub repository, these researchers provide 3D print files for many of the behavioral devices used in their lab, including a Nosepoke and a Lickometer designed from rats. The repository also includes a script that reads MedPC files into Python in a usable way.