September 7, 2017
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.
Kretschmer F, Kretschmer V, Kunze VP, Kretzberg J (2013) OMR-Arena: Automated Measurement and Stimulation System to Determine Mouse Visual Thresholds Based on Optomotor Responses. PLoS ONE 8(11): e78058. https://doi.org/10.1371/journal.pone.0078058
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.
June 2, 2017
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.
February 12, 2017
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.
Puścian, A., Łęski, S., Kasprowicz, G., Winiarski, M., Borowska, J., Nikolaev, T., … Knapska, E. (2016). Eco-HAB as a fully automated and ecologically relevant assessment of social impairments in mouse models of autism. eLife, 5, e19532. https://doi.org/10.7554/eLife.19532
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 buillt several devices for conducting operant conditioning and monitoring enviornmental 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.
- We provide a low cost alternative to commercially available nose poke system.
- Our custom made apparatus is open source and TTL compatible.
- We validate our system with optogenetic self-stimulation of dopamine neurons in mice.
ArduiPod Box is a simple, comprehensive touchscreen-based operant conditioning chamber that utilizes an iPod Touch in conjunction with an Arduino microcontroller to present visual and auditory stimuli, record behavior in the form of nose-pokes or screen touches, and deliver liquid reward. In his 2014 paper, Oskar Pineño introduces ArduinoPod Box and demonstrates the use of the device in a visual discrimination task.
ArduiPod Box relies on an open-source iOS app named Shaping that can be downloaded for free at the iTunes store, as well as, on Dr. Pineno’s website. Detailed instructions for assembly of the ArduiPod Box are also detailed on the website. In addition, video demonstrating of ArduiPod can be found here.
The Rodent Operant Bucket (ROBucket), designed by Dr. Alexxai Kravitz and Kavya Devarakonda of the Eating and Addiction Section, Diabetes Endocrinology and Obesity Branch, NIDDK, is an inexpensive and easily assembled open-source operant chamber, based on the Arduino microcontroller platform, that can be used to train mice to respond for a reward.
The apparatus contains two nose pokes, a drinking well, and a solenoid-controlled sucrose delivery system. The chamber can easily run magazine training, fixed ratio and progressive ratio training schedules, and can be programmed to run more complicated behavioral paradigms.
In their 2016 paper, “ROBucket: A low cost operant chamber based on the Arduino microcontroller,” Kavya Devarakonda, Katrina P. Nguyen, and Alexxai V. Kravitz validate ROBucket by demonstrating its application in an operant conditioning paradigm, as well as, detail the hardware comprising ROBucket, and the flexible software controlling it.
Further documentation of this device can be found on the NIDDK website, where Dr. Kravitz and his lab share ROBucket construction instructions, ROBucket design files, ROBucket source code, and 3D printing design files.