information-theoretic Spike-Triggered Average and Covariance (iSTAC) estimator for receptive fields
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README.md
compDklgaussian.m
compNlin_1D.m
compNlin_2D.m
compiSTAC.m
demo1_iSTAC.m
demo2_fitiSTACmodel.m
expquadratic.m
fitNlin_expquad_ML.m
fitNlin_expquad_iSTACmomentbased.m
gsorth.m
logdet.m
makeStimRows.m
negKLsubspace.m
sameconv.m
simpleSTC.m

README.md

iSTAC

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]

Download

  • Command line: clone the repository from github (e.g., git clone git@github.com:pillowlab/iSTAC.git )
  • Browser: download zipped archive: iSTAC-master.zip

Usage

  • 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