A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing and diffusion-weighted imaging data. Created for modeling brain dynamics using TheVirtualBrain (thevirtualbrain.org) platform. A detailed description and example usage can be found in the paper here: https://www.
The large diversity of cell-types of the brain, provides the means by which circuits perform complex operations. Understanding such diversity is one of the key challenges of modern neuroscience. Neurons have many unique electrophysiological and behavioral features from which parallel cell-type classification can be inferred.
BrainGlobe is a suite of Python-based computational neuroanatomy software tools. We provide software packages for the analysis and visualisation of neuroanatomical data, particularly from whole-brain microscopy. In addition, we provide tools for working with brain atlases, to simplify development of new tools and aid collaboration and cooperation by adopting common standards.
Kilosort is a software package for identifying neurons and their spikes in extracellular electrophysiology, a process known as “spike sorting”. Kilosort has been primarily developed and tested on the Neuropixels 1.
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.
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.
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.
MNE-Python is an open-source Python module for neuroscience data analysis. It implements many neuroscience-specific algorithms and statistical tools; has rich visualization capabilities that are both interactive and fully scriptable (for reproducibility); and integrates easily with general-purpose libraries like SciPy, Scikit-learn, and TensorFlow.
Napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy).