Paper Info
Title
Spikes and Nets (S&N): A New Fast, Parallel Computing, Point Process Software for Multineuronal Discharge and Connectivity Analysis
Abstract
S&N is a new multi-platform software for neuronal spike train analysis which offers a comprehensive set of methods to efficiently handle large numbers of neuronal spike train files with a user-friendly interface and automatic results archiving. Selection, grouping, archiving and results matching of point process sequential analysis of neuronal files is a complex and time-consuming task especially for multiple electrode array recordings. Relevant and useful software packages for spike train analysis are already available; however, the aim of this work was to develop an easy to use, fast, short learning curve, multi-platform and parallel computing software able to manage a large number of neuronal spike train files to detect discharge patterns, connectivity, and time-dependent changes. A set of the most used spike train methods to perform single and multi-neuronal discharge pattern recognition and functional connectivity analysis were implemented in an easy-to-use, standalone, Matlab-based software toolbox: spikes and nets (S&N). The methods included for single and multi-neuronal discharge pattern analysis are raster plot, interspike intervals distribution, multiparametric burst, auto-correlation, auto-spectral, fractal, poincare, and phases. For functional connectivity analysis, cross-correlation and joint interval scatter diagram were implemented. Additionally, time segmentation analysis is available to detect temporal changes for all methods. S&N efficiently handles large numbers of neuronal discharge files at once with fast and automatic archiving of both analytical and graphical results which makes it suitable for multi-electrode array data. S&N applies up to 11 different analytical methods, including automatic file segmentation for time-dependent changes detection, and generates publication quality graphs. The developed toolbox is multi-platform and reads universal spike train files with any temporal resolution, able to process also ECG, EEG or similar data files.
Year
DOI
Venue
2020
10.1007/s11063-020-10242-7
NEURAL PROCESSING LETTERS
Keywords
DocType
Volume
Spike trains,Discharge patterns,Functional connectivity,Spectral analysis,Large neuronal data files,Multi-electrode arrays,Time-dependent changes
Journal
52.0
Issue
ISSN
Citations 
SP1.0
1370-4621
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Carlos Valle100.34
Maria Rodriguez-Fernandez216211.05
Antonio Eblen-Zajjur300.34