, Volume 7, Issue 3, pp 165–178 | Cite as

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

  • David H. Goldberg
  • Jonathan D. Victor
  • Esther P. Gardner
  • Daniel Gardner


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, 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.


Computational neuroscience Information theory Neural coding Neurodatabases Data sharing 



The STAToolkit and related computational neuroscience resources are supported by the U.S. Human Brain Project/ Neuroinformatics program via MH068012 from NIMH, NINDS, NIA, NIBIB, and NSF to D. Gardner, with partial support via EY09314 from NEI to J.D. Victor. Parallel development of and related terminology, including BrainML are supported by Human Brain Project/ Neuroinformatics MH057153 from NIMH, with past support from NIMH and NINDS. Data from E.P. Gardner’s lab used in the STAToolkit tests and demonstrations reported here supported by NS011862 from NINDS and Human Brain Project/ Neuroinformatics NS044820 from NINDS, NIMH, and NIA, both to E.P. Gardner.

We thank the many developers of the information-theoretic and entropy measures we have implemented in the toolkit, and the many users of this software. In addition to those named elsewhere, the project has benefited from consultations with Sheila Nirenberg (Weill Cornell), Ron Elber and Ramin Zabih (Cornell), Simon Schultz (Imperial College London), Emery N. Brown and R. Clay Reid (Harvard Medical School), Pamela Reinagel (UCSD), Barry J. Richmond (NIMH), Partha Mitra (Cold Spring Harbor Labs), and A.B. Bonds (Vanderbilt). We are also indebted to Keith Purpura for demonstration datasets included with STAToolkit, as well as Eliza Chan, Ajit Jagdale, Adrian Robert, and Ronit Vaknin for many helpful discussions, contributions to, and testing of the software and its in-development extensions.


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Copyright information

© Humana Press Inc. 2009

Authors and Affiliations

  • David H. Goldberg
    • 1
  • Jonathan D. Victor
    • 2
  • Esther P. Gardner
    • 3
  • Daniel Gardner
    • 1
  1. 1.Laboratory of Neuroinformatics-D-404 and Department of PhysiologyWeill Medical College of Cornell UniversityNew YorkUSA
  2. 2.Department of Neurology and Neuroscience and Laboratory of NeuroinformaticsWeill Medical College of Cornell UniversityNew YorkUSA
  3. 3.Department of Physiology and NeuroscienceNYU School of MedicineNew YorkUSA

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