FED3 is an open-source battery-powered device for home-cage training of mice in operant tasks. FED3 can be 3D printed and the control code is open-source and can be modified. The code is written in the Arduino language and is run on an Adafruit Feather M0 Adalogger microcontroller inside of FED3.
The advent of genetically encoded calcium indicators, along with surgical preparations such as thinned skulls or refractive index matched skulls, have enabled mesoscale cortical activity imaging in head-fixed mice. Such imaging studies have revealed complex patterns of coordinated activity across the cortex during spontaneous behaviors, goal-directed behavior, locomotion, motor learning,and perceptual decision making.
Two-photon (2P) microscopy is a cornerstone technique in neuroscience research. However, combining 2P imaging with spectrally arbitrary light stimulation can be challenging due to crosstalk between stimulation light and fluorescence detection.
Suite2P is a very modular imaging processing pipeline written in Python which allows you to perform registration of raw data movies, automatic cell detection, extraction of calcium traces and infers spike times.
Modern Biology methods require a large number of high quality experiments to be conducted, which requires a high degree of automation. Our solution is an open-source hardware that allows for automatic high-throughput generation of large amounts of cell biology data.
The BigPint package can help examine any large multivariate dataset. However, we note that the example datasets and example code in this package consider RNA-sequencing datasets. If you are using this software for RNA-sequencing data, then it can help you confirm that the variability between your treatment groups is larger than that between your replicates and determine how various normalization techniques in popular RNA-sequencing analysis packages (such as edgeR, DESeq2, and limma) affect your dataset.
COINSTAC provides a platform to analyze data stored locally across multiple organizations without the need for pooling the data at any point during the analysis. It is intended to be an ultimate one-stop shop by which researchers can build any statistical or machine learning model collaboratively in a decentralized fashion.
When performing canine operant conditioning studies, the delivery of the reward can be a limiting factor of the study. While there are a few commercially available options for automatically delivering rewards, they generally require manual input, such as using a remote control, in accordance with the experiment script.
This workflow is used to analyze large-scale, multi-round, high-resolution image data acquired using EASI-FISH (Expansion-Assisted Iterative Fluorescence In Situ Hybridization). It takes advantage of the n5 filesystem to allow for rapid and parallel data reading and writing.
Experiments aiming to understand sensory-motor systems, cognition and behavior necessitate training animals to perform complex tasks. Traditional training protocols require lab personnel to move the animals between home cages and training chambers, to start and end training sessions, and in some cases, to hand-control each training trial.