March 21, 2019
Victor Wumbor-Apin Kumbol and colleagues have developed and shared Actifield, an automated open-source actimeter for rodents, in a recent HardwareX publication.
Measuring locomotor activity can be a useful readout for understanding effects of a number of experimental manipulations related to neuroscience research. Commercially available locomotor activity recording devices can be cost-prohibitive and often lack the ability to be customized to fit a specific lab’s needs. Kumbol et al. offer an open-source alternative that utilizes infrared motion detection and an arduino to record activity in a variety of chamber set ups. A full list of build materials, links to 3D-print and laser-cut files, and assembly instructions are available in their publication.
Read more from HardwareX!
January 16, 2019
In the Journal of Neurophysiology, Brice Williams and colleagues have shared their design for a novel dual-port lick detector. This device can be used for both real-time measurement and manipulation of licking behavior in head-fixed mice.
Measuring licking behavior in mice provides a valuable metric of sensory-motor processing and can be nicely paired with simultaneous neural recordings. Williams and colleagues have developed their own device for precise measuring of licking behavior as well as for manipulating this behavior in real time. To address limitations of many available lick sensors, the authors designed their device to be smaller (appropriate for mice), contactless (to diminish electric artifacts for neural recording), and precise to a submillisecond timescale. This dual-port detector can be implemented to detect directional licking behavior during sensory tasks and can be used in combination with neural recording. Further, given the submillisecond precision of this device, it can be used in a closed-loop system to perturb licking behaviors via neural inhibition. Overall, this dual-port lick detector is a cost-effective, replicable solution that can be used in a variety of applications.
Learn how to build your own here!
And be sure to check out their Github.
December 19, 2018
In 2007, Adam Hoffman and colleagues shared their design for an Electric Operant Testing Apparatus (ELOPTA) in Behavior Research Methods.
Operant behavior is commonly studied in behavioral neuroscience, therefore there is a need for devices to train and collect data from animals in operant procedures. Commercially available systems often require training to program and use and can be expensive. Hoffman and colleagues developed a system that can automatically control operant procedures and record behavioral outputs. This system is intended to be easy to use because it is easily programmable, portable and durable.
Read more here!
Hoffman, A.M., Song, J. & Tuttle, E.M. Behavior Research Methods (2007) 39: 776. https://doi.org/10.3758/BF03192968
December 12, 2018
Vladislav Voziyanov and colleagues have developed and shared the TRIO Platform, a low-profile in vivo imaging support and restraint system for mice.
In vivo optical imaging methods are common tools for understanding neural function in mice. This technique is often performed in head-fixed, anesthetized animals, which requires monitoring of anesthesia level and body temperature while stabilizing the head. Fitting each of the components necessary for these experiments on a standard microscope stage can be rather difficult. Voziyanov and colleagues have shared their design for the TRIO (Three-In-One) Platform. This system is compact and provides sturdy head fixation, a gas anesthesia mask, and warm water bed. While the design is compact enough to work with a variety of microscope stages, the use of 3D printed components makes this design customizable.
Read more about the TRIO Platform in Frontiers in Neuroscience!
The design files and list of commercially available build components are provided here.
Voziyanov, V., Kemp, B. S., Dressel, C. A., Ponder, K., & Murray, T. A. (2016). TRIO Platform: A Novel Low Profile In vivo Imaging Support and Restraint System for Mice. Frontiers in Neuroscience, 10. doi:10.3389/fnins.2016.00169
November 30, 2018
Nikolas Francis and Patrick Kanold of the University of Maryland share their design for Psibox, a platform for automated operant conditioning in the mouse home cage, in Frontiers in Neural Circuits.
The ability to collect behavioral data from large populations of subjects is advantageous for advancing behavioral neuroscience research. However, few cost-effective options are available for collecting large sums of data especially for operant behaviors. Francis and Kanold have developed and shared Psibox, an automated operant conditioning system. It incorporates three modules for central control , water delivery, and home cage interface, all of which can be customized with different parts. The system was validated for training mice in a positive reinforcement auditory task and can be customized for other tasks as well. The full, low-cost system allows for quick training of groups of mice in an operant task with little day-to-day experimenter involvement.
Learn how to set up your own Psibox system here!
Francis, NA., Kanold, PO., (2017). Automated operant conditioning in the mouse home cage. Front. Neural Circuits.
November 14, 2018
John Stowers and colleagues from the Straw Lab at the University of Frieburg have developed and shared FreemoVR, a virtual reality set-up for unrestrained animals.
Virtual reality (VR) systems can help to mimic nature in behavioral paradigms, which help us to understand behavior and brain function. Typical VR systems require that animals are movement restricted, which limits natural responses. The FreemoVR system was developed to address these issues and allows for virtual reality to be integrated with freely moving behavior. This system can be used with a number of different species including mice, zebrafish, and Drosophila. FreemoVR has been validated to investigate several behavior in tests of height-aversion, social interaction, and visuomotor responses in unrestrained animals.
Read more on the Straw Lab site, Nature Methods paper, or access the software on Github.
Stowers, J. R., Hofbauer, M., Bastien, R., Griessner, J., Higgins, P., Farooqui, S., . . . Straw, A. D. (2017). Virtual reality for freely moving animals. Nature Methods, 14(10), 995-1002. doi:10.1038/nmeth.4399
October 17, 2018
In the journal HardwareX, Jinook Oh and colleagues share their design for OpenFeeder, an automatic feeder for animal experiments.
Automatic delivery of precisely measured food amounts is important when studying reward and feeding behavior. Commercially available devices are often designed with specific species and food types in mind, limiting the ways that they can be used. This open-source automatic feeding design can easily be customized for food types from seeds to pellets to fit the needs of any species. OpenFeeder integrates plexiglass tubes, Arduino Uno, a motor driver, and piezo sensor to reliably deliver accurate amounts of food, and can also be built using 3D printed parts.
Read more from HardwareX.
Or check out the device on Open Science Framework and Github.
October 10, 2018
On Hackaday, Richard Warren of the Sawtell Lab at Columbia University has shared his design for KineMouse Wheel, a light-weight running wheel for head-fixed locomotion that allows for 3D positioning of mice with a single camera.
Locomotive behavior is a common behavioral readout used in neuroscience research, and running wheels are a great tool for assessing motor function in head-fixed mice. KineMouse Wheel takes this tool a step further. Constructed out of light-weight, transparent polycarbonate with an angled mirror mounted inside, this innovative device allows for a single camera to capture two views of locomotion simultaneously. When combined with DeepLabCut, a deep-learning tracking software, head-fixed mice locomotion can be captured in three dimensions allowing for a more complete assessment of motor behavior. This wheel can also be further customized to fit the needs of a lab by using different materials for the build. More details about the KineMouse Wheel are available at hackaday.io, in addition to a full list of parts and build instructions.
Read more about KineMouse Wheel on Hackaday,
and check out other awesome open-source tools on the OpenBehavior Hackaday list!
We are looking for your feedback to understand how we can better serve the community! We’re also interested to know if/how you’ve implemented some of the open-source tools from our site in your own research.
We would greatly appreciate it if you could fill out a short survey (~5 minutes to complete) about your experiences with OpenBehavior.
October 3, 2018
Thomas Akam and researchers from the Champalimaud Foundation and Oxford University have developed pyControl, a system that combines open-source hardware and software for control of behavioral experiments.
The ability to seamlessly control various aspects of a complex task is important for behavioral neuroscience research. pyControl, an open-source framework, combines Python scripts and a Micropython microcontroller for the control of behavioral experiments. This framework can be run through a command line interface (CLI), or in a user-friendly graphical user interface (GUI) that allows users to manage a variety of devices such as nose pokes, LED drivers, stepper motor controllers and more. The data collected using this system can then be imported easily into Python for data analysis. In addition to complete documentation on the pyControl website, users are welcome to ask questions and interact with the developers and other users via a pyControl Google group.
Read more on the pyControl website.
Purchase the pyControl breakout board at OpenEphys.
Or check out the pyControl Google group!