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. 2014 Apr 3;9(4):e93062. doi: 10.1371/journal.pone.0093062

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© 2014 Meyer et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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(A) Predictive power for the different methods in terms of cross-validated mutual information (MI) between stimulus and response, showing mean and standard error. CbRF, MID and GLM perform almost identically with no significant difference between the methods. Linear estimators (STA, NRC, ridge regression) show significantly lower predictive power. denotes statistical significance (paired Wilcoxon test; ). (B) Same experiment as panel A, but for 38 IC responses to continuously starting FM sweep complexes recorded in a separate neural subpopulation. (C) Predictive power of the CbRF method for single units compared to ridge regression, MID and GLM. Shown are mean and standard deviation across five cross-validation folds for the 38 IC units in panel A.

Inline graphic — (A) Predictive power for the different methods in terms of cross-validated mutual information (MI) between stimulus and response, showing mean and standard error. CbRF, MID and GLM perform almost identically with no significant difference between the methods. Linear estimators (STA, NRC, ridge regression) show significantly lower predictive power. denotes statistical significance (paired Wilcoxon test; ). (B) Same experiment as panel A, but for 38 IC responses to continuously starting FM sweep complexes recorded in a separate neural subpopulation. (C) Predictive power of the CbRF method for single units compared to ridge regression, MID and GLM. Shown are mean and standard deviation across five cross-validation folds for the 38 IC units in panel A.