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.
Extracellular microelectrodes frequently record neural activity from more than one neuron in the vicinity of the electrode. The process of labeling each recorded spike waveform with the identity of its source neuron is called spike sorting and is often approached from an abstracted statistical perspective.
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).
morphologica is a header-only C++ library which provides simulation support facilities for simulations of dynamical systems.
It helps with:
Configuration: morphologica allows you to easily set up a simulation parameter configuration system, using the JSON reading and writing abilities of morph::Config.
NetPyNE (Networks using Python and NEURON) is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biophysical neuronal networks using the NEURON simulator.
For more details, installation instructions, documentation, tutorials, forums, videos and more, please visit: www.
Easy whole-brain modeling for computational neuroscientists 👩🏿🔬💻🧠
In its essence, neurolib is a computational framework for simulating coupled neural mass models written in Python. It helps you to easily load structural brain scan data to construct brain networks where each node is a neural mass representing a single brain area.
With YAPiC you can make your own customized filter (also called model or classifier) to enhance a certain structure of your choice with a simple Python based command line interface, installable with pip.