Fig 6. The effect of phasic dynamics are robust to noise statistics and linearly related to $g_{K_{LT}}$ expression.

(A—D) The effect of varying signal-to-noise ratios (SNR) on the outcome of the total entropy (A), noise entropy (B), MI (C), and selectivity (D) analyses. As the amplitude of the noise current increases relative to the amplitude of the stimulus-driven current (decreasing SNR), noise increases and MI and selectivity decrease, but phasic models continue to outperform tonic models. The red lines shows the means of phasic models, and the blue line shows the means of tonic models. Bars show standard error. The gray boxes indicate the parameter values used in this study. (E—H) The effect of noise shapes on total entropy (E), noise entropy (F), MI (G), and selectivity (H). Adding white noise erases the advantage of phasic neurons, but white noise is biologically unrealistic and ineffective at driving significant amounts of variability (F). More biologically realistic noise shapes with greater power at low frequencies, such as pink (1/f) and red (1/f²), produce comparable results when SNR is adjusted to match variability. Mean and standard error shown. White noise shown at SNR 0.5, pink noise at SNR 2, and red noise at SNR 4. (I–L) The effect of $g_{K_{LT}}$ conductance on total entropy (I), noise entropy (J), MI (K), and selectivity (L) for the phasic models.