Spike Train Analysis Toolkit: Enabling Wider Application of Information-Theoretic Techniques to Neurophysiology

David H. Goldberg, Jonathan D. Victor, Esther P. Gardner, Daniel Gardner
2009 Neuroinformatics  
Conventional methods widely available for the analysis of spike trains and related neural data include various time-and frequency-domain analyses, such as perievent and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural
more » ... s can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://neuroanalysis.org, maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope. Introduction: Unrealized Potential of Information-Theoretic Measures for Neurophysiology Understanding how the brain represents and processes information is an extraordinarily complex problem, requiring a wide range of experimental preparations, measurement techniques, physical scales, experimental paradigms, and computational methods. Effective collaboration across each of these domains is crucial to progress in neuroscience. Computational neuroinformatics can aid many such collaborations by synthesizing computational neuroscience-analyses of neural representation and information processing-and the standards-based methods for archiving, classifying, and exchanging neuroscience data embodied in this journal's title and reviewed in its pages by Gardner et al. (2003 Gardner et al. ( , 2008a , Kennedy (2004 Kennedy ( , 2006 , and Koslow and Hirsch (2004) . The neural coding problem-how neurons represent and process information with spike trains-can be approached in a rigorous, quantitative manner. Information theory, originally developed as a means of studying modern Neuroinform (2009) 7:165-178
doi:10.1007/s12021-009-9049-y pmid:19475519 pmcid:PMC2818590 fatcat:66l2vjknprgr5bho64aobxyzay