pillow lab @ princeton

neural coding & computation group

info-theoretic spike-triggered average and covariance (iSTAC) estimator

description: MATLAB code - estimates a set of linear filters (or "receptive fields") using an information-theoretic objective that optimally combines information from 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 single-spike information captured by each additional filter.

download: iSTAC-master.zip
clone: github page
more info: README.html

relevant publication:
Pillow, JW and Simoncelli, EP. (2006). Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis. Journal of Vision, 6(4):414-428 (pdf)

Please report any bugs to pillow@princeton.edu