title: Spike train analysis toolkit: enabling wider application of information-theoretic techniques to neurophysiology.
abstract: Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event 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 recordings 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.
authors.affiliation: Laboratory of Neuroinformatics-D-404 and Department of Physiology, Weill Medical College of Cornell University, 1300 York Avenue, New York, NY 10065, USA.
authors.forename: David H
authors.initials: DH
authors.lastname: Goldberg
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Victor
authors.forename: Esther P
authors.initials: EP
authors.lastname: Gardner
authors.forename: Daniel
authors.initials: D
authors.lastname: Gardner
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D003198:Computer Simulation; D019992:Databases as Topic / Q000458:organization & administration / Q000639:trends; D006801:Humans; D009474:Neurons / Q000502:physiology*; D009482:Neurophysiology / Q000379:methods*; D011381:Programming Languages; D012815:Signal Processing, Computer-Assisted; D012984:Software / Q000639:trends