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.
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.
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.
DataJoint is an open-source library for managing and sharing scientific data pipelines in Python and Matlab.
DataJoint allows creating and sharing computational data pipelines, which are defined as databases and analysis code for executing steps of activities for data collection and analysis.
cellfinder is software from the Margrie Lab at the Sainsbury Wellcome Centre for automated 3D cell detection and registration of whole-brain images (e.g. serial two-photon or lightsheet imaging).
It’s a work in progress, but cellfinder can:
DeepLabCut™ is an efficient method for 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames).
MNE is a software package for processing electrophysiological signals primarily from magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, and more recently sEEG, ECoG and fNIRS. It provides a comprehensive solution for data preprocessing, forward modeling (with boundary element models), distributed source imaging, time–frequency analysis, non-parametric multivariate statistics, multivariate pattern analysis, and connectivity estimation.
Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.