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. 2013 Dec 16;8(12):e81660. doi: 10.1371/journal.pone.0081660

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© 2013 Zohar 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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The signal, in terms of the covariance between the neural response and the stimulus is shown as a function of the cell number in a heterogeneous population based on the responses of all cells in the data set. The inset shows the distribution of the signal over the different eigenvectors of the neural spike-count correlation matrix (with uniform correlation coefficient of 0.2), as a function of the rank of their eigenvalue, in percent. Note that the first eigenvector corresponds to the uniform vector (cf Figure 13). The signal and the correlation matrix were estimated using 10,000 repetitions for every stimulus value .

Inline graphic — The signal, in terms of the covariance between the neural response and the stimulus is shown as a function of the cell number in a heterogeneous population based on the responses of all cells in the data set. The inset shows the distribution of the signal over the different eigenvectors of the neural spike-count correlation matrix (with uniform correlation coefficient of 0.2), as a function of the rank of their eigenvalue, in percent. Note that the first eigenvector corresponds to the uniform vector (cf Figure 13). The signal and the correlation matrix were estimated using 10,000 repetitions for every stimulus value .