(A) Percent improvement in the accuracy of direction coding as a function of the
magnitude of the secondary noise, in Model I (left panel), Model II (middle
panel), and Model III (right panel), for the stimulus-dependent correlations
generated by the models (red curves), as compared to the mean-matched
stimulus-independent case (green curves). For independent neurons (blue curves),
the percent improvement is constant and vanishing.
(B) Deterioration of the coding accuracy by the secondary sources of noise, as
described by the error normalized by the error in the noiseless case with
σ1 =
σ2 =
σ3 = 0. In the presence of the
stimulus-dependent correlations generated by Model I (left panel), Model II
(middle panel), and Model III (right panel), the coding accuracy is largely
insensitive to the secondary noise (red curves), whereas in the cases of
stimulus-independent correlation (green curves) and independent neurons (blue
curves), noise is detrimental to the coding accuracy. For a sufficiently strong
non-linearity, g, input-gain modulation can improve the coding
accuracy (dashed line, middle panel).