Model ELL-EGp network captures correlation shaping between local and global stimulation.

<p><b>A</b>, Stimulus gain of superficial BP neurons in the model (compare to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002667#pcbi-1002667-g002" target="_blank">Figure 2B</a>). Our analytical theory (solid) matches the simulation results from the ELL-EGp network (dots). <b>B</b>, Correlation between superficial BP neuron pairs during local and global stimulation of the model (compare to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002667#pcbi-1002667-g001" target="_blank">Figure 1C</a>). Since our theory predicts a linear relationship between output correlation and input correlation, the output is shown in units of input correlation in the local state , which was 0.1 in simulations. <b>C</b>, Idealized schematic illustrating the effect of feedback on shared fluctuations. Left: local inputs fail to recruit EGp feedback via deep population (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002667#pcbi-1002667-g004" target="_blank">Figure 4</a>), so common input arises purely through feedforward stimulus drive. Center: Low frequency global input recruits a negative image of the stimulus, which cancels the common input to the pair of superficial pyramidal neurons. Right: The cancellation signal is weak for high frequency global inputs due to the low-pass nature of the feedback. Hence, the common fluctuations are not cancelled.