Spikes and Nets (S&N): A New Fast, Parallel Computing, Point Process Software for Multineuronal Discharge and Connectivity Analysis

摘要

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, poincaré, 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.