SpiSeMe

SpiSeMe generates surrogate spike trains for surrogate-based hypothesis testing in nonlinear analyses of point-process data such as neuronal spike trains.

Key Features:

• Multi-Language Implementation: Implementations are provided in C++, Matlab, and Python.
• Four Algorithms for Surrogate Generation: Includes four distinct algorithms to generate surrogate data from spike trains or sequences of discrete events.
• Unified Toolbox: Presents a single package that consolidates the surrogate-generation algorithms across supported languages.
• Focus on Point-Process Data: Targets analysis of point-process data, specifically spike trains produced by electrophysiological recordings.

Scientific Applications:

• Surrogate-based hypothesis testing: Generates surrogate spike trains to assess statistical significance of observed patterns in spike-train data.
• Nonlinear time-series analysis: Supports tests and analyses in nonlinear science that require surrogate data for discrete-event sequences.
• Neuroscience research: Applied to the analysis and validation of neuronal spike-train patterns and related point-process investigations.

Methodology:

Implements four algorithms that generate surrogate spike-train sequences while preserving relevant statistical properties of the original point-process data for use in hypothesis testing; implementations are available in C++, Matlab, and Python.