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High-Fidelity Coding with Correlated Neurons
Figure 2
Positive correlation can dramatically suppress the error.
A. Probability of discrimination error for a 2-Pool model of a neural population, as a function of the number of neurons, 
, for independent (dashed; all 
) and correlated (circles) populations; parameters are 
, 
for both, and 
, 
in the correlated case. Numerical (circles) and analytic (solid line) results are compared. B. Suppression factor due to correlation, defined as the ratio between the error probability of independent and correlated populations, as a function of the number of neurons, 
; numeric (circles) and analytic (solid line) results. C. Error probability as a function of the cross-pool correlation, 
, for independent (dashed line) and correlated (circles, 
) populations; analytic results for correlated population (solid line). 
. D. Error probability as a function of the correlation within Pool 1, 
, for independent (dashed line) and correlated (circles, 
, 
) populations; analytic results for correlated population (solid line). 
. E. Probability contours for three examples of neural populations; independent (green cross, 
, 
, 
), near lock-in correlation (pink dot, 
, 
), and uneven correlation (blue diamond, 
, 
, 
). Colored symbols correspond to points on plots in previous panels.
doi: https://doi.org/10.1371/journal.pcbi.1003970.g002