Chronux: A platform for analyzing neural signals

doi:10.1016/j.jneumeth.2010.06.020

Journal of Neuroscience Methods

Volume 192, Issue 1, 30 September 2010, Pages 146-151

https://doi.org/10.1016/j.jneumeth.2010.06.020 Get rights and content

Abstract

Chronux is an open-source software package developed for the analysis of neural data. The current version of Chronux includes software for signal processing of neural time-series data including several specialized mini-packages for spike-sorting, local regression, audio segmentation, and other data-analysis tasks typically encountered by a neuroscientist. Chronux is freely available along with user tutorials, sample data, and extensive documentation from http://chronux.org/.

Introduction

Neuroscientists are increasingly gathering large time-series data in the form of multichannel electrophysiological recordings, EEG, MEG, fMRI, and optical image time-series. The availability of such data has brought with it new challenges for analysis, and has created a pressing need for the development of software tools for storing and analyzing neural signals. While sophisticated methods for analyzing multichannel time-series have been developed over the past several decades in statistics and signal processing, the lack of a unified user-friendly platform that implements these methods is a critical bottleneck in mining large neuroscientific data. Chronux is an open-source software project that aims to fill this void by providing a comprehensive software platform for the analysis of neural signals. It is a collaborative effort that draws on a number of previous research projects (Fee et al., 1996, Llinas et al., 1999, Mitra and Pesaran, 1999, Tchernichovski et al., 2000, Pesaran et al., 2002, Bokil et al., 2006a, Bokil et al., 2006b).

This paper provides a brief description of Chronux. We begin by discussing the architecture of Chronux and related online resources. This is followed by a description of the key routines in Chronux along with examples of their use. While the examples are drawn from our own work, we also provide extensive references to the use of Chronux in the literature. We end with a summary and a discussion of planned enhancements.

Section snippets

Software

The Chronux website at http://chronux.org/ is the central location for information about the current and all previous releases of Chronux. The home page contains links to the source code, documentation, tutorials and the discussion forum. Most of the code is written in the Matlab^® (The Mathworks, Natick, MA) scripting language, with some compiled C code integrated into Matlab^® using mex functions. Chronux has been tested in Matlab^® (releases R13 to the 2007a) under the Windows, Macintosh, and

Discussion

We have presented Chronux, a comprehensive software platform for the analysis of neural signals. The current version of Chronux includes a Matlab^® toolbox for signal processing of neural time-series data as well as several specialized mini-packages for spike-sorting, local regression, audio segmentation, and other tasks. Future developments will include improvements and extensions to the spectral analysis and spike-sorting libraries, the addition of a machine-learning toolbox and the

Disclosure statement

Dr. Mitra is on the board of directors of Medametrics, LLC.

Acknowledgements

The Chronux platform was initially supported by grant R01MH071744 from NIH to PPM. We thank David Kleinfeld, Murray Jarvis, Bijan Pesaran, Hiren Maniar, and David Thomson for contributions. We would also like to specially acknowledge Catherine Loader for her contributions to the Chronux project, and specifically for the LOCFIT package.

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View full text

Chronux: A platform for analyzing neural signals

Abstract

Introduction

Section snippets

Software

Discussion

Disclosure statement

Acknowledgements

J Neurosci Methods

Neuroimage

Biophys J

Neuroimage

Anim Behav

J Neurosci Methods

A method for detection and classification of events in neural activity

IEEE Trans Biomed Eng

Comparing spectra and coherences for groups of unequal size

J Neurosci Methods

Dynamic phenotypes

Neuroinformatics

The natural statistics of audiovisual speech

PLoS Comput Biol

Anticipatory activity of motor cortex in relation to rhythmic whisking

J Neurophysiol

Interactions between the superior temporal sulcus and auditory cortex mediate dynamic face/voice integration in rhesus monkeys

J Neurosci

Intracellular dynamics of hippocampal place cells during virtual navigation

Nature

Electrophysiological correlates of default-mode processing in macaque posterior cingulate cortex

Proc Natl Acad Sci USA