Airtrack was developed in LARKUM Lab by Mostafa Nashaat, Hatem Oraby, Robert Sachdev, York Winter and Matthew Larkum. Alexander Schill, engineer at Charité workshop (CWW) had a significant contribution to the design of the platform and the airtrack table.
Airtrack is a head-fixed behavioral environment that uses a lightweight physical maze floating on an air table that moves around the animal’s body under the direct control of the animal itself, solving many problems associated with using virtual reality for head-fixed animals.
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.”
Annalisa Scimemi, of the Department of Biology at SUNY Albany, has shared the following Python based code to track movement of labelled paws in grooming and freely behaving mice in an article published by PLoS Computational Biology.
Traditional approaches to analyze grooming rely on manually scoring the time of onset and duration of each grooming episode. This type of analysis is time-consuming and provides limited information about finer aspects of grooming behaviors, which are important to understand bilateral coordination in mice. Currently available commercial and freeware video-tracking software allow automated tracking of the whole body of a mouse or of its head and tail, not of individual paws. M-Track is an open-source code that allows users to simultaneously track the movement of individual paws during spontaneous grooming episodes and walking in multiple freely-behaving mice/rats. This toolbox provides a simple platform to perform trajectory analysis of paw movement. M-Track provides a valuable and user-friendly interface to streamline the analysis of spontaneous grooming in biomedical research studies.
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.
Lucy Palmer and Andrew Micallef, of the Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia, have shared the following Arduino and Python based platform for Go/ No-Go tasks in an article published by Frontiers in Cellular Neuroscience.
The Go/No-Go sensory task requires an animal to report a decision in response to a stimulus. In “Go” trials, the subject must respond to a target stimulus with an action, while in “No-Go” trials, the subject withholds a response. To execute this task, a behavioral platform was created which consists of three main components: 1) a water reward delivery system, 2) a lick sensor, and 3) a sensory stimulation apparatus. The water reward is administered by a gravity flow water system, controlled by a solenoid pinch valve, while licking is monitored by a custom-made piezo-based sensor. An Arduino Uno Rev3 simultaneously controls stimulus and reward delivery. In addition, the Arduino records lick frequency and timing through the piezo sensor. A Python script, employing the pyserial library, aids communication between the Arduino and a host computer.
Mariana de Araújo has shared the following regarding OBAT, an operant box designed for auditory tasks developed at the Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaiba, Brazil.
OBAT is a low cost operant box designed to train small primates in auditory tasks. The device presents auditory stimuli via a MP3 player shield connected to an Arduino Mega 2560 through an intermediate, custom-made shield. It also controls two touch-sensitive bars and a reward delivery system. A Graphical User Interface allows the experimenter to easily set the parameters of the experimental sessions. All board schematics, source code, equipment specification and design are available at GitHub and at the publication. Despite its low cost, OBAT has a high temporal accuracy and reliably sends TTL signals to other equipment. Finally, the device was tested with one marmoset, showing that it can successfully be used to train these animals in an auditory two-choice task.
Dr. Ewelina Knapska from the Nencki Institute of Experimental Biology in Warsaw, Poland has shared the following regarding Eco-HAB, an RFID-based system for automated tracking:
Eco-HAB is an open source, RFID-based system for automated measurement and analysis of social preference and in-cohort sociability in mice. The system closely follows murine ethology. It requires no contact between a human experimenter and tested animals, overcoming the confounding factors that lead to irreproducible assessment of murine social behavior between laboratories. In Eco-HAB, group-housed animals live in a spacious, four-compartment apparatus with shadowed areas and narrow tunnels, resembling natural burrows. Eco-HAB allows for assessment of the tendency of mice to voluntarily spend time together in ethologically relevant mouse group sizes. Custom-made software for automated tracking, data extraction, and analysis enables quick evaluation of social impairments. The developed protocols and standardized behavioral measures demonstrate high replicability. Unlike classic three-chambered sociability tests, Eco-HAB provides measurements of spontaneous, ecologically relevant social behaviors in group-housed animals. Results are obtained faster, with less manpower, and without confounding factors.
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 openBehavior github repository from Hao Chen’s lab at UTHSC aims to establish a computing platform for rodent behavior research using the Raspberry Pi computer. They have built several devices for conducting operant conditioning and monitoring environmental data.
The operant licking device can be placed in a standard rat home cage and can run fixed ratio, various ratio, or progressive ratio schedules. A preprint describing this project, including data on sucrose vs water intake is available. Detailed instructions for making the device is also provided.
The environment sensor can record the temperature, humidity, barometric pressure, and illumination at fixed time intervals and automatically transfer the data to a remote server.