Information theoretic methods are now widely used for the analysis of spike train data. However, developing robust implementations of these methods can be tedious and time-consuming. In order to facilitate further adoption of these methods, the Spike Train Analysis Toolkit implements several information-theoretic spike train analysis techniques.

This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C and optimized for efficiency.
The capabilities of the toolkit are distinguished between information methods and entropy methods.
Information methods are those methods which estimate the mutual information between an ensemble of spike trains and some other experimental variable. We distinguish between formal and attribute-specific information, as proposed by Reich et al. (2001)
Entropy methods are those methods that estimates entropy from a discrete histogram, a computation common to many information-theoretic methods.
Read more and download the toolkit here.

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