Hot off the eLife press, Jeremy Magland and colleagues have shared SpikeForest, a tool for validating automated neural spike sorters.
Spike sorting is a crucial step in neural data analysis. Manual spike sorting is time consuming and sensitive to human error, so much effort has been placed into developing automated algorithms to perform this necessary step. However, even with rapid development and sharing of these tools, there is little information to guide researchers for which algorithm may best serve their needs and that it offers the accuracy needed to give a complete scope of the data. To address this, Magland and colleagues across 11 research groups have developed and contributed data for SpikeForest. This python based software suite utilizes a large database of ephys recordings featuring ground truth units (units that have spike patterns known a priori), a parallel processing pipeline to benchmark algorithm performance, and a web interface for users to explore results. This tool can be used to assess which algorithm works best to extract data from different recording and experimental methods (in vivo, ex vivo, tetrode, etc) and provides accurate evaluation metrics for comparison. Information about the spike sorting algorithms that SpikeForest can compare are available in the recent publication, as well as a preliminary comparison of these algorithms based on community provided datasets. The SpikeForest Interface also allows users to sort their own data with a few modifications to the code, which is discussed in the publication. Be sure to check it out!
David A. Bjånes and Chet T. Moritz from the University of Washington in Seattle have developed and published their device for training rats to perform a modified center out task.
As neuroscience tools for studying rodent brains have improved in the 21st century, researchers have started to utilize increasingly complex tasks to study their behavior, sometimes adapting tasks commonly used with primates. One such task used for studying motor behavior, the center-out reaching task, has been modified for use in rodents. Bjånes and Moritz have further contributed to the adaptation of this task by creating ACRoBaT, or the Automated Center-out Rodent Behavioral Trainer. This device features two custom printed PCBs, a 3D printed housing unit, an Arduino microchip, and other commercially available parts that can be mounted outside a behavioral arena. It also provides a fully automated algorithm to train rats based on behavioral feedback fed into the device through various sensors. The authors show the effectiveness of the device with data from 18 rats across different conditions to find the optimal training procedure for this task. Information for how to build the device is available in their publication, as well as on Github.
Read the full publication here, or check out the files on GitHub!
Katrin Franke, Andre Maia Chagas and colleagues have developed and shared a spatial visual stimulator with an arbitrary-spectrum of light for visual neuroscientists.
Vision research, quite obviously, relies on control of visual stimuli in an experiment. There are a great number of commercially available devices and hardware that are implemented in presenting visual stimuli to human and other species, however, these devices are predominantly developed for the visual spectrum of humans. For other species, such as drosophila, zebrafish, and rodents, their visual spectrum includes UV, and the devices used in studies sometimes fail to present this range of stimulus, and therefore often limits our understanding of the visual systems of other organisms. To address this, Franke, Chagas and colleagues developed an open source, generally low cost visual stimulator which can be customized with up to 6 chromatic channels. Given the components used to build the device, the spectrum of light can be arbitrary and customizable to be adapted to different animal models based on their visual spectrum. The details of this device, including the parts list and information for a custom python library for generating visual stimuli (QDSpy), can be found in the eLife publication. The device is tested and shown to work with stimulating the mouse retina and in vivo zebrafish studies; details on these experiments can also be found in the publication.
Jeffrey P. Gill and colleagues have developed and shared a new toolbox for synchronizing video and neural signals, cleverly named neurotic!
Collecting neural data and behavioral data are fundamental to behavioral neuroscience, and the ability to synchronize these data streams are just as important as collecting the information in the first place. To make this process a little simpler, Gill et al. developed an open-source option called neurotic, a NEUROscience Tool for Interactive Characterization. This tool is programmed in Python and includes a simple GUI, which makes it accessible for users with little coding experience. Users can read in a variety of file formats for neural data and video, which they can then process, filter, analyze, annotate and plot. To show the effectiveness across species and signal types, the authors tested the software with aplysia feeding behavior and human beam walking. Given its open-source nature and strong integration of other popular open-source packages, this software will continue to develop and improve as the community uses it.
Raffaele Mazziotti from the Istituto di Neuroscienze CNR di Pisa has generously shared the following about 3DOC, a recently developed and published project from their team.
“Operant conditioning is a classical paradigm and a standard technique used in experimental psychology in which animals learn to perform an action in order to achieve a reward. By using this paradigm, it is possible to extract learning curves and measure accurately reaction times. Both these measurements are proxy of cognitive capabilities and can be used to evaluate the effectiveness of therapeutic interventions in mouse models of disease. Recently in our Lab, we constructed a fully 3D printable chamber able to perform operant conditioning using off-the-shelf, low-cost optical and electronic components, that can be reproduced rigorously in any laboratory equipped with a 3D printer with a total cost around 160€. Requirements include a 3D printable filament ( e.g. polylactic acid, PLA), a low-cost microcontroller (e.g. Arduino UNO), and a single-board computer (e.g. Raspberry Pi). We designed the chamber entirely using 3D modelling for several reasons: first, it has a high degree of reproducibility, since the model is standardized and can be downloaded to print the same structure with the same materials throughout different laboratories. Secondly, it can be easily customized in relation to specific experimental needs. Lastly, it can be shared through online repositories (Github: https://github.com/raffaelemazziotti/oc_chamber). With these cost-efficient and accessible components, we assayed the possibility to perform two-alternative forced-choice operant conditioning using audio-visual cues while tracking in the real-time mouse position. As a proof of principle of customizability, we added a version of the OC chamber that is able to show more complex visual stimuli (e.g. Images). This version includes an edit of the frontal wall that can host a TFT monitor and code that runs on Psychopy2 on Raspberry PI. This tool can be employed to test learning and memory in models of disease. We expect that the open design of the chamber will be useful for scientific teaching and research as well as for further improvements from the open hardware community.”
Thanks to Jan Homolak from the Department of Pharmacology, University of Zagreb School of Medicine, Zagreb, Croatia for sharing the following about repurposing a digital kitchen scale for neuroscience research: a complete hardware and software cookbook for PASTA (Platform for Acoustic STArtle).
“As we were starving for a solution on how to obtain relevant and reliable information from a kitchen scale sometimes used in very creative ways in neuroscience research, we decided to cut the waiting and cook something ourselves. Here we introduce a complete hardware and software cookbook for PASTA, a guide on how to demolish your regular kitchen scale and use the parts to turn it into a beautiful multifunctional neurobehavioral platform. This project is still medium raw, as its the work in progress, however, we hope you will still find it well done.
PASTA comes in various flavors such as:
– complete hardware design for PASTA
– PASTA data acquisition software codes (C++/Arduino)
– PASTA Chef: An automatic experimental protocol execution Python script for data acquisition and storage
– ratPASTA (R-based Awesome Toolbox for PASTA): An R-package for PASTA data analysis and visualization
There are a number of open source toolboxes available for neural data analysis, especially for spike and local field potential data. With more options comes a more difficult decision when it comes to selecting the toolbox that’s right for your data. Fortunately, Valentina Unakafova and Alexander Gail have compared several toolboxes for spike and LFP analysis, connectivity analysis, dimensionality reduction, and generalized linear modeling. They discuss the major features of software available for Python and MATLAB (Octave) including Brainstorm, Chronux, Elephant, FieldTrip, gramm, Spike Viewer, and SPIKY. They include succinct tables for assessing system and program requirements, quality of documentation and support, and data types accepted by each toolbox. Using an open-access dataset, they assess the functionality of the programs and finish their comparison with highlighting advantages of each toolbox to consider when trying to find the one that works best for your data. The files they used to compare toolboxes are all available from GitHub to supplement their paper.
Alireza Azafar and colleagues at the Donders Institute for Brain, Cognition, and Behaviour at Rabound University have published an open source database of high speed videos of whisking behavior in freely moving rodents.
As responsible citizens, it’s possible you are missing lab and working from home. Maybe you have plenty to do, or maybe you’re looking for some new data to analyze to increase your knowledge of active sensing in rodents! Well, in case you don’t have that data at hand and can’t collect it yourself for a few more weeks, we have a treat for you! Azafar et al. have shared a database of 6,642 high quality videos featuring juvenile and adult male rats and adult male mice exploring stationary objects. This dataset includes a wide variety of experimental conditions including genetic, pharmacological, and sensory deprivation interventions to explore how active sensing behavior is modulated by different factors. Information about interventions and experimental conditions are available as a supplementary Excel file.
The videos are available as mp4 files as well as MATLAB matrices that can be converted into a variety of data formats. All videos underwent quality control and feature different amounts of noise and background, which makes a great tool for mastering video analysis. A toolbox is available from this group on Github for a variety of whisker analysis methods including nose and whisker tracking. This database is a great resource for studying sensorimotor computation, top-down mechanisms of sensory navigation, cross-species comparison of active sensing, and effects of pharmacological intervention of whisking behaviors.
Read more about active sensing behavior, data collection methods, and rationale here.
This week we want to talk about joy! I mean, joy-sticks. Parley Belsey, Mark Nicholas and Eric Yttri have developed and shared an open-source joystick for studying motor behavior and decision making in mice!
Mice are hopping and popping in research, and so researchers are using more creativity and innovation to understand the finite aspects of their behaviors. Recently, members of the Yttri lab at Carnegie Mellon used their skills to create an open source joystick for studying mouse motor and decision making behaviors! In their paper they describe the full behavioral set up (based on the RIVETS design from the Dudman lab), featuring a removable head fixation point, a sipping tube, and a joystick to measure reach trajectory, amplitude, speed, etc. Data is collected and devices are controlled via an Arduino, solenoid circuit, microSD card reader, and LCD readout, and data can be analyzed in real time or saved to a csv for analysis later. The Arduino can be programmed to signal reward delivery when a correct response is recorded from the joystick which streamlines outcome based reward delivery. Belsey et al. tested their device with adult mice, and the results of training can be found in the paper as well as the full build instructions and ideas for how their tool may be of interest to build and use in your lab.
Erno Kuusela and Juho Lämsä, from the University of Oulu in Finland, have shared their design for an open source, computer controlled robotic flower system for studying bumble bee behavior.
Oh.. to be a honey bee.. collecting nectar from a robotic flower.. of open source design… splendid. As with behavioral studies from species common to neuroscience (rodents to drosophila to humans or zebrafish, etc), data collection for behavioral studies in bees can be time-consuming and sensitive to human error. Thanks to the growth in the open source movement, it’s easier than ever to develop hardware and software to automate such studies, which is what Kuusela and Lämsä have demonstrated in their publication. They developed a system of robotic flowers to study bee behavior. Their design features a control unit, based on an Arduino Mega 2560, which can collect data from and send inputs to up to 32 individual robotic flowers. Each flower contains its own servo controlled refill system. The nectar cup (in this design, a phillips screw head that can hold 1.7 uL!) is attached to servomotor’s shaft via a servo horn which, when prompted by the program, dips the cup into the flower’s individual nectar reservoir. The flower is designed in a way to capture data when an animal is feeding by the placement of IR beams that are broken when engaged on the flower’s feeding mechanism and sends data to the control unit. A covering on the system can be marked with symbols to attract bees. Custom control software is available on an open source license to be used as is, or modified to fit an experimenter’s needs. While developed and tested with bumble bees, the system can also be adapted for a number of species.
Read more about specifics of this system in Kuusela & Lämsä (2016). The circuit diagrams, parts list, and control software and source code are available in the paper’s supplemental information.