Neurometric Function Analysis for Neural Population Codes
Description
This package contains MATLAB functions to replicate the results presented in the papers
P. Berens,
A. S. Ecker,
S. Gerwinn,
A. S. Tolias, and
M. Bethge
Reassessing optimal neural population codes with neurometric functions
Proceedings of the National Academy of Sciences of the United States of America, 108(11), 4423-4428, 2011
#fisher information, #population coding, #mean squared error, #discrimination error, #neurometric function
Code, URL, PDF, BibTex
Reassessing optimal neural population codes with neurometric functions
Proceedings of the National Academy of Sciences of the United States of America, 108(11), 4423-4428, 2011
#fisher information, #population coding, #mean squared error, #discrimination error, #neurometric function
Code, URL, PDF, BibTex
P. Berens,
S. Gerwinn,
A. S. Ecker, and
M. Bethge
Neurometric function analysis of population codes
Advances in Neural Information Processing Systems 22, 2009
#population coding, #neurometric function
Code, PDF, BibTex
Neurometric function analysis of population codes
Advances in Neural Information Processing Systems 22, 2009
#population coding, #neurometric function
Code, PDF, BibTex
The package contains functions for numerically computing the MDE, the IMDE and the MMSE for a homogenous population with responding to an angular variable with a Gaussian Poisson-like noise model. Fisher information and the MASE are also computed. It also contains a function to estimate the MDE of an independent population with discrete Poisson distributed spike counts. The package contains a demo.m file which explains how to use the functions. In addition, all functions contain comments, which might be helpful for understanding the code.
It should be easy to adapt the code for specific other uses, since it is fairly modular. The core routines are estimateMMSE.m and estimateMDE.m, where the error measures for a specific population are computed. If you require other tuning function shapes or covariance structures, you need to change
genPop.m, genMean.m and genCov.m.If you discover problems or - worse, but inevitable - a bug, please send an to Philipp Berens.
