Category: Most Recent

OpenFeeder

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.

 

KineMouse Wheel

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!


 

OpenBehavior Feedback Survey

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.

https://american.co1.qualtrics.com/jfe/form/SV_0BqSEKvXWtMagqp

Thanks!

pyControl

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!


 

EthoWatcher: a tool for behavioral and video-tracking analysis in laboratory animals

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

VASIC

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!


NeuroMatic

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.


Argus

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!


PhotometryBox

August 29, 2018

In a recent bioRxiv preprint, Scott Owen and Anatol Kreitzer share PhotometryBox, an open-source solution for electronic control of fiber-based fluorescence measurements.


Fluorescence measurements from deep-brain structures through optical fibers (fiber photometry) represent a versatile, powerful, and rapidly growing neuroscience technique. A typical fiber photometry system consists of three
parts: (1) an implant with an optical fiber that is cemented to the skull, (2) optical components for generation of fluorescence excitation light and detection of emission light, and (3) electronic components for controlling light sources and acquiring signals. Excellent technical solutions are available for implants and optical components; however, currently available electronic control systems are not optimized for these experiments. The most commonly used electronic components are either over-engineered or unnecessarily inflexible. To address these issues, Owen et al have developed an open-source, low-cost solution for the electronic components. This system is based on a programmable microcontroller (MBED LPC1768) and can be assembled in ~1 hour (less than a day for an inexperienced user with limited soldering experience). The total estimated cost is about $650, less than one tenth the price of the most commonly used commercially available systems.
The design, development and implementation of this project is described in a manuscript now available on bioRxiv, while details regarding parts, construction and use are available on Hackaday.

Read more on bioRxiv

or check out the Hackaday page.


Q&A with Dr. Mackenzie Mathis on her experience with developing DeepLabCut

August 22, 2018

Dr. Mackenzie Mathis, Principal Investigator of the Adaptive Motor Control Lab (Rowland Institute at Harvard University), has shared the following responses to a short Q&A about the inspiration behind, development of and sharing of DeepLabCut — a toolbox for animal tracking using deep-learning.


What inspired you and your colleagues to create this toolbox as opposed to using previously developed commercial software?

Alexander Mathis and I both worked on behaviors where we wanted to track particular features, and they proved to be unreliably tracked with the methods we tried. Specifically, Alexander has an odor-guided navigation task that he works on in the lab of Prof. Venkatesh Murthy at Harvard, where the mice are placed in a very large “endless” paper trail and he inkjet prints odors for them to follow to get rewards (chocolate milk). The position of the snout is very important to measure accurately, so background subtraction or other heuristics didn’t work when the nose crossed the trail and when the droplet was right in front of the snout. I worked on a skilled joystick behavior for mice, and I wanted to track joints accurately and non-invasively – a challenging problem for little hands. So, we teamed up with Prof. Matthias Bethge at the University of Tuebingen, to work on a new approach. He suggested we start looking into the rapidly advancing human pose estimation literature, and we looked at several before deciding to seriously benchmark DeeperCut, a top performing algorithm in the large MPII dataset. Those authors did something very clever, namely, they used a deep neural network (ResNet) that was pre-trained on a large image set called ImageNet. This gives the ResNet a chance to learn natural scene statistics first. Remarkably, we found that we could use only a few frames to very accurately track the snout in the odor-guided navigation task, so we next tried videos from my joystick task, and to flex DeepLabCut’s muscles, we teamed up with Kevin Cury (who, like myself was an alumni of Prof. Nao Uchida’s group) to track fruit flies in the 3D chamber. After all this benchmarking, we built a toolbox that implements a complete pipeline to extract and label frames, train and evaluate the deep neural nets, as well as analyze new experimental videos.  We call this toolbox DeepLabCut, as a nod to DeeperCut.

What was the motivation for immediately sharing your work as an open source tool, thus making it accessible to the broader neuroscience community?

Some of the options we first tried to track with were very expensive commercial systems, and they failed quite badly. On the other hand, deep learning has revolutionized computer vision in the last few years, so we were eager to try some new approaches to solve the problem. So, in addition to being advocates of open science, we really wanted to make a toolbox that someone with minimal to no coding experience could, absolutely for free, track whatever they wanted.

We also know peer review can be slow, so as soon as we had the toolbox in place, we wrote up the arxiv paper and released the code base immediately. Honestly, it has been one of my most rewarding papers – the feedback from our peers, and seeing what people have used the code for, has been a very rewarding experience. This was my first preprint, and especially for methods manuscripts, I now cannot imagine another way to share our future work too.

How do you think open source tools, such as yours, will continue to impact the progress of scientific research?

Open source code and preprints have been the norm in some fields for decades (such as math and physics), and I am really excited to see it come of age in biology and neuroscience. I am excited to see how tools will continue to improve as the community gets behind them, just as we could build on DeeperCut, which was open source. Also, at least in my experience, many individuals write their own code, which leads to a lot of duplicated efforts. Moreover, datasets are becoming increasingly more complicated and code to work with such data need to be robust shared. My expectation is that open source code will become the norm in the future, which can only help science become more robust.

Even before formal publication this week (see Nature Neuroscience), we estimate that about 100 labs are actively using DeepLabCut, so releasing the code before publication, we hope,  has really allowed for rapid progress to be made. We were also very happy that The Atlantic could highlight some of the early adopters, as it’s one thing to say you made something, but it’s another to hear others saying it is actually ‘something.’


DeepLabCut provides an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. Read more on the website, or in Nature Neuroscience.