Giter Site home page Giter Site logo

wehr-do / mountainlab Goto Github PK

View Code? Open in Web Editor NEW

This project forked from magland/mountainlab

0.0 1.0 0.0 12.27 MB

spike sorting software

JavaScript 9.49% CSS 0.09% HTML 1.68% C++ 77.73% QMake 1.02% C 2.15% Shell 0.31% Python 0.32% MATLAB 7.10% M 0.01% Prolog 0.09%

mountainlab's Introduction

MountainLab Spike Sorting Software

About

MountainSort (a component of MountainLab) is spike sorting software developed by Jeremy Magland, Alex Barnett, and Leslie Greengard at the Center for Computational Biology, Flatiron Institute in close collaboration with Jason Chung and Loren Frank at UCSF department of Physiology. It is part of MountainLab, a general framework for data analysis and visualization.

MountainLab software is being developed by Jeremy Magland and Witold Wysota.

The software comprises tools for processing electrophysiological recordings and for visualizing and validating the results.

Contact the authors for information on the slack team for users and developers.

Installation

Installation instructions

How to run spike sorting

The first sort

Working branches

  • ms3 - development branch with the ms3 processing pipeline (preferred)

  • 2017_06 branch - snapshot with only critical bug fix updates

Some demo videos

Tests

Repo of unit (and not so unit) tests

Data formats used in MountainLab

The .mda file format

Data management

The .prv data management system

Automated curation/annotation

Documentation on using annotation scripts will be forthcoming.

Because one of the goals of mountainsort is to enable reproducible spike sorting, we strongly advise against manual modifications that go beyond merging bursting clusters and perhaps rejecting certain noise clusters. Instead, we suggest that you export the cluster metrics along with the sorted clusters and then set cutoffs for inclusion of data in analyses based on those metrics. This will make it easy to describe your subsequent analyses and easy to determine how those analyses do or do not depend on cluster quality.

The isolation and noise overlap metrics we provide work well for the situations we have focused on, but they can be supplemented or replaced by other objective metrics as needed. Such metric processors may be included in the pipeline as post-processing plugins as C++, matlab, or python modules. Contact us if you you would like to contribute additional cluster metrics, or need help with integration.

Other documentation

A guide to using MountainSort with snippets, rather than continuous data acquisition

An old guide: Cluster metrics and automated curation

Acknowledgements

Thanks to all the users on the slack team for ongoing testing and feedback.

References

Barnett, Alex H., Jeremy F. Magland, and Leslie F. Greengard. "Validation of Neural Spike Sorting Algorithms without Ground-truth Information." Journal of Neuroscience Methods 264 (2016): 65-77. Link to arXiv

Magland, Jeremy F., and Alex H. Barnett. Unimodal clustering using isotonic regression: ISO-SPLIT. Link to arXiv

mountainlab's People

Contributors

magland avatar wysota avatar ahbarnett avatar sebsgit avatar jasonechung avatar tjd2002 avatar alexmorley avatar mmyros avatar droumis avatar

Watchers

rig3 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.