brainrender is a python package for the visualization of three dimensional neuro-anatomical data. It can be used to render data from publicly available data set (e.g. Allen Brain atlas) as well as user generated experimental data.
JASP is a cross-platform statistical software program with a state-of-the-art graphical user interface. The JASP interface allows you to conduct statistical analyses in seconds, and without having to learn programming or risking a programming mistake.
pyControl is a system of open source hardware and software for controlling behavioural experiments, built around the Micropython microcontroller.
pyControl makes it easy to program complex behavioural tasks using a clean, intuitive, and flexible syntax for specifying tasks as state machines.
pyPhotometry is system of open source, Python based, hardware and software for neuroscience fiber photometry data acquisition, consisting of an acquisition board and graphical user interface.
pyPhotometry supports data aquisition from two analog and two digital inputs, and control of two LEDs via built in LED drivers with an adjustable 0-100mA output.
SLEAP (Social LEAP Estimates Animal Poses) is a multi-animal pose tracker based on deep learning. It is the successor of LEAP (Pereira et al., Nature Methods, 2019) and was designed to deal with the problem of tracking body landmarks of multiple freely interacting animals.
NeuroImaging Tools & Resources Collaboratory is an award-winning free web-based resource that offers comprehensive information on an ever expanding scope of neuroinformatics software and data. Since debuting in 2007, NITRC has helped the neuroscience community make further discoveries using software and data produced from research that used to end up lost or disregarded.
PiVR is a system that allows experimenters to immerse small animals into virtual realities. The system tracks the position of the animal and presents light stimulation according to predefined rules, thus creating a virtual landscape in which the animal can behave.
ReproNim’s goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research, while making the process easier and more efficient for investigators.
Several excellent computational frameworks exist that enable high-throughput and consistent tracking of freely moving unmarked animals. SimBA introduce and distribute a plug-and play pipeline that enables users to use these pose-estimation approaches in combination with behavioral annotation for the generation of supervised machine-learning behavioral predictive classifiers.
Neurodata Without Borders is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data. NWB is designed to store a variety of neurophysiology data, including data from intracellular and extracellular electrophysiology experiments, data from optical physiology experiments, and tracking and stimulus data.