Tag: rat


April 17, 2019

In a recent Nature Protocol’s article, Edoardo Balzani and colleagues from Valter Tucci’s lab have developed and shared Phenopy, a Python-based open-source analytical platform for behavioral phenotyping.

Behavioral phenotyping of mice using classic methods can be a long process and is susceptible to high variability, leading to inconsistent results. To reduce variance and speed up to process of behavioral analysis, Balzani et al. developed Phenopy, an open-source software for recording and analyzing behavioral data for phenotyping. The software allows for recording components of a behavioral task in combination with electrophysiology data. It is capable of performing online analysis as well as analysis of recorded data on a large scale, all within a user-friendly interface. Information about the software is available in their publication, available from Nature Protocols.*

Check out the full article from Nature Protocols!

(*alternatively available on ResearchGate)

Telemetry System for Recording EEG

March 29, 2019

In a 2011 Journal of Neuroscience Methods article, Pishan Chang and colleagues shared their design for an open-source, novel telemetry system for recording EEG in small animals.

EEG monitoring in freely-behaving small animals is a useful technique for observing natural fluctuations in neural activity over time. Monitoring frequencies above 80 Hz continuously over a period of weeks can be a challenge. Chang et al. have shared their design for a system that combines an implantable telemetric sensor, radio-frequency transmission, and an open-source data acquisition software to collect EEG data over a span of up to 8 weeks. Various modifications to the system  have increased the longevity of the device and reduced transmission noise to provide continuous and reliable data. Schematics of the device, transmission system, and validation results in a population of epileptic rodents are available in their publication.


Learn more from the Journal of Neuroscience Methods!


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!


February 20, 2019

Francisco Romero Ferrero and colleagues have developed idtracker.ai, an algorithm and software for tracking individuals in large collectives of unmarked animals, recently described in Nature Methods.

Tracking individual animals in large collective groups can give interesting insights to behavior, but has proven to be a challenge for analysis. With advances in artificial intelligence and tracking software, it has become increasingly easier to collect such information from video data. Ferrero et al. have developed an algorithm and tracking software that features two deep networks. The first tracks animal identification and the second tracks when animals touch or cross paths in front of one another. The software has been validated to track individuals with high accuracy in cohorts of up to 100 animals with diverse species from rodents to zebrafish to ants. This software is free, fully-documented and available online with additional jupyter notebooks for data analysis.

Check out their website with full documentation, the recent Nature Methods article, BioRXiv preprint, and a great video of idtracker.ai tracking 100 zebrafish!


January 9, 2019

Kevin Coffey has shared the following about DeepSqueak, a deep learning-based system for detection and analysis of ultrasonic vocalizations, which he developed with Russell Marx.

Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs). Recording and analysis of USVs can be performed noninvasively in almost any rodent behavioral model to provide rich insights into the emotional state and motor function. Despite strong evidence that USVs serve an array of communicative functions, technical and financial limitations have inhibited widespread adoption of vocalization analysis. Manual USV analysis is slow and laborious, while existing automated analysis software are vulnerable to broad spectrum noise routinely encountered in the testing environment.

To promote accessible and accurate USV research, we present “DeepSqueak”, a fully graphical MATLAB package for high-throughput USV detection, classification, and analysis. DeepSqueak applies state-of-the-art regional object detection neural networks (Faster-RCNN) to detect USVs. This dramatically reduces the false positive rate to facilitate reliable analysis in standard experimental conditions. DeepSqueak included pre-trained detection networks for mouse USVs, and 50 kHz and 22 kHz rat USVs. After detection, USVs can be clustered by k-means models or classified by trainable neural networks.

Read more in their recent publication and check out DeepSqueak on Github!

Multi-channel Fiber Photometry

October 24, 2018

Qingchun Guo and colleagues share their cost-effective, multi-channel fiber photometry system in Biomedical Optics Express.

Fiber photometry is a viable tool for recording in vivo calcium activity in freely behaving animals. In combination with genetically encoded calcium indicators, this tool can be used to measure neuronal and population activity from a genetically defined subset of neurons. Guo and colleagues have developed a set-up to allow for recording from multiple brain regions, or multiple animals, simultaneously with the use of a galvano-mirror system. This creative and simple solution reduces the number of detectors necessary for multi-channel data collection. This expands the ability of researchers to collect calcium imaging data from many subjects in a cost-effective way.

Read more here!


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.


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.


An opensource lickometer and microstructure analysis program

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

NeRD: an open-source neural recording device

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.
Read more here!