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!
September 26, 2018
In Computers in Biology and Medicine, Carlos Fernando Crispin Jr. and colleagues share their software EthoWatcher: a computational tool that supports video-tracking, detailed ethography, and extraction of kinematic variables from video files of laboratory animals.
The freely available EthoWatcher software has two modules: a tracking module and an ethography module. The tracking module permits the controlled separation of the target from its background, the extraction of image attributes used to calculate distances traveled, orientation, length, area and a path graph of the target. The ethography module allows recording of catalog-based behaviors from video files, the environment, or frame-by-frame. The output reports latency, frequency, and duration of each behavior as well as the sequence of events in a time-segmented format fixed by the user. EthoWatcher was validated conducting tests on the detection of the known behavioral effects of drugs and on kinematic measurements.
Read more in their paper or download the software from the EthoWatcher webpage!
Junior, C. F., Pederiva, C. N., Bose, R. C., Garcia, V. A., Lino-De-Oliveira, C., & Marino-Neto, J. (2012). ETHOWATCHER: Validation of a tool for behavioral and video-tracking analysis in laboratory animals. Computers in Biology and Medicine,42(2), 257-264. doi:10.1016/j.compbiomed.2011.12.002
September 19, 2018
In HardwareX, Brendan Drackley and colleagues share VASIC, an open source weight-bearing device for high-throughput and unbiased behavioral pain assessment in rodents.
The assessment of pain in animal models is a key component in understanding and developing treatments for chronic pain. Drackley and colleagues developed VASIC (Voluntary Access Static Incapacitance Chamber), a modified version of a weight-bearing test. A brief water deprivation encourages rats or mice to seek water in a test chamber, set up with a weighing platforms under the water spout, which can assess weight shifting to an unaffected side in animals with damage to nerves or inflammatory pain. The design incorporates a custom printed circuit board (available from the paper), infrared sensor, Arduino microcontroller, 3D printed parts, and open source software for analysis. A full parts list, links to files, and data from a validation study are available in their paper.
Read more here!
September 12, 2018
In Frontiers in Neuroinformatics, Jason Rothman and R. Angus Silver share NeuroMatic, an open-source toolkit for acquiring, analyzing and simulating electrophysiological data.
Data acquisition, analysis, and simulation are key components of understanding neural activity from electrophysiological recordings. Traditionally, these three components of ephys data have been handled by separate software tools. NeuroMatic was developed to merge these tools into a single package, capable of performing a variety of patch-clamp recordings, data analysis routines and simulations of neural activity. Additionally, due to its open-source, modular design in WaveMetrics Igor Pro, NeuroMatic allows users to develop their own analysis functions that can be easily incorporated into its framework. By integrating acquisition, analysis, and simulation together, researchers are able to conserve experimental metadata and track the analysis performed in real time, without involving separate softwares.
Read more about NeuroMatic here!
Or check out their website and GitHub.
September 5, 2018
In a recent Behavior Research Methods article, Soaleha Shams and colleagues share Argus, a data extraction and analysis tool built in the open-source R language for tracking zebrafish behavior.
Based on a formerly developed custom-software for zebrafish behavior tracking, Argus was developed with behavioral researchers in mind. It includes a new, user-friendly, and efficient graphical user interface and offers simplicity and flexibility in measuring complex zebrafish behavior through customizable parameters set by the researcher. The program is validated against two commercially available programs for zebrafish behavior analysis, and measures up in its ability to track speed, freezing, erratic movement, and interindividual distance. In summary, Argus is shown to be a novel, cost- effective, and customizable method for the analysis and quantification of both single and socially interacting zebrafish.
Read more here!
August 15, 2018
In the Journal of Neurophysiology, Sachin S. Deshmuhk and colleagues share their design for a Picamera system that allows for tracking of animals in large behavioral arenas.
Studies of spatial navigation and its neural correlates have been limited in the past by the reach of recording cables and tracking ability in small behavioral arenas. With the implementation of long-range, wireless neural recording systems, researchers are not able to expand the size of their behavioral arenas to study spatial navigation, but a way to accurately track animals in these larger arenas is necessary. The Picamera system is a low-cost, open-source scalable multi-camera tracking system that can be used to track behavior in combination with wireless recording systems. The design is comprised of 8 overhead Raspberry Pi cameras (capable of recording at a high frame rate in a large field of view) recording video independently in individual Raspberry Pi microcomputers and processed using the Picamera Python library. When compared with a commercial tracking software for the same purpose, the Picamera system reportedly performed better with improvements in inter-frame interval jitter and temporal accuracy, which improved the ability to establish relationships between recorded neural activity and video. The Picamera system is an affordable, efficient solution for tracking animals in large spaces.
Read more here!
Or check out their GitHub!
Saxena, R., Barde, W., and Deshmukh, S.S. An inexpensive, scalable camera system for tracking rats in large spaces (2018). Journal of Neurophysiology. https://doi.org/10.1152/jn.00215.2018
August 8, 2018
In HardwareX, an open access journal for designing, building and customizing opensource scientific hardware, Martin A. Raymond and colleagues share their design for a user-constructed, low-cost lickometer.
Researchers interested in ingestive behaviors of rodents commonly use licking behavior as a readout for the amount of fluid a subject consumes, as recorded by a lickometer. Commercially available lickometers are powerful tools to measure this behavior, but can be expensive and often require further customization. The authors offer their own design for an opensource lickometer that utilizes readily available or customizable components such as a PC sound card and 3D printed drinking bottle holder. The data from this device is collected by Audacity, and opensource audio program, which is then converted to a .csv format which can be analyzed using an R script made available by the authors to assess various features of licking microstructure. A full bill of materials, instructions for assembly and links to design files are available in the paper.
Check out the full publication here!
Raymond, M. A., Mast, T. G., & Breza, J. M. (2018). An open-source lickometer and microstructure analysis program. HardwareX, 4. doi:10.1016/j.ohx.2018.e00035
July 23, 2018
OpenBehavior has been covering open-source neuroscience projects for a few years, and we are always thrilled to see projects that are well documented and can be easily reproduced by others. To further this goal, we have formed a collaboration with Hackaday.io, who have provided a home for OpenBehavior on their site. This can be found at: https://hackaday.io/OpenBehavior, where we currently have 36 projects listed ranging from electrophysiology to robotics to behavior. We are excited about this collaboration because it provides a straightforward way for people to document their projects with instructions, videos, images, data, etc. Check it out, see what’s there, and if you want your project linked to the OpenBehavior page simply tag it as “OPENBEHAVIOR” or drop us a line at the Hackaday page.
Note: This collaboration between OpenBehavior and Hackaday.io is completely non-commercial, meaning that we don’t pay Hackaday.io for anything, nor do we receive any payments from them. It’s simply a way to further our goal of promoting open-source neuroscience tools and their goal of growing their science and engineering community.
July 16, 2018
In a special issue of Journal of Neural Engineering, Dominique Martinez and colleagues their share design for NeRD, an open source neural recording device for wireless transmission of local field potential (LFP) data in in freely-behaving animals.
Electrophysiological recording of local field potentials in freely-behaving animals is a prominent tool used by researchers for assessing the neural basis of behavior. When performing these recordings, cables are commonly used to transmit data to the recording equipment, which tethers the animals and can interfere with natural behavior. Wireless transmission of LFP data has the advantage of removing the cable between the animal and the recording equipment, but is hampered by the large number of data to be transmitted at a relatively high rate.
To reduce transmission bandwidth, Martinez et al. propose an encoder/decoder algorithm based on adaptive non-uniform quantization. As proof-of- concept, they developed a NeRD prototype that digitally transmits eight channels encoded at 10 kHz with 2 bits per sample. This lightweight device occupies a small volume and is powered with a small battery allowing for 2h 40min of autonomy. The power dissipation is 59.4 mW for a communication range of 8 m and transmission losses below 0.1%. The small weight and low power consumption offer the possibility of mounting the entire device on the head of a rodent without resorting to a separate head-stage and battery backpack. The use of adaptive quantization in the wireless transmitting neural implant allows for lower transmission bandwidths, preservation of high signal fidelity, and preservation of fundamental frequencies in LFPs from a compact and lightweight device.
Martinez, D., Clément, M., Messaoudi, B., Gervasoni, D., Litaudon, P., & Buonviso, N. (2018). Adaptive quantization of local field potentials for wireless implants in freely moving animals: An open-source neural recording device. Journal of Neural Engineering, 15(2), 025001. doi:10.1088/1741-2552/aaa041