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information-theoretic Spike-Triggered Average and Covariance (iSTAC) estimator for neural receptive fields

Description: Estimates a set of linear filters that best capture a neuron's input-output properties, using an information-theoretic objective that optimally combines information from the spike-triggered average and spike-triggered covariance. The filters can be considered as the first stage in a linear-nonlinear-Poisson (LNP) model of the neuron's response. They are sorted by informativeness, providing an estimate of the mutual information gained by the inclusion of each filter.

Relevant publication: Pillow & Simoncelli, Journal of Vision 2006. [abs, pdf]


  • Command line: clone the repository from github (e.g., git clone )
  • Browser: download zipped archive:


  • Launch matlab and cd into the directory containing the code (e.g. cd code/iSTAC/).

  • Examine the script test_iSTAC_script.m for a line-by-line tutorial on how to use the code contained in this package, which goes through several simulated examples.

  • The primary function used for estimating the filters is compiSTAC.m