A cascade model comprised of a derivative filter followed by a nonlinear sigmoidal component reproduces the input-size dependence of transfer gain in the baroreflex neural arc from baroreceptor pressure input to efferent sympathetic nerve activity (SNA). We examined whether the same model could predict the operating-point dependence of the baroreflex neural arc transfer characteristics estimated by a binary white noise input. In 8 anesthetized rabbits, we isolated bilateral carotid sinuses from the systemic circulation and controlled intra-carotid sinus pressure (CSP). We estimated the linear transfer function from CSP to SNA while varying mean CSP among 70, 100, 130, and 160 mmHg (P₇₀, P₁₀₀, P₁₃₀, and P₁₆₀, respectively). The transfer gain at 0.01 Hz was significantly smaller at P₇₀ (0.61±0.26) and P₁₆₀ (0.60±0.25) than at P₁₀₀ (1.32±0.42) and P₁₃₀ (1.36±0.45) (mean±SD, P<0.05). In contrast, transfer gain values above 0.5 Hz were similar among the protocols. As a result, the slope of increasing gain between 0.1 and 0.5 Hz was significantly steeper at P₇₀ (17.6±3.6) and P₁₆₀ (14.1±4.3) than at P₁₀₀ (8.1±4.4) and P₁₃₀ (7.4±6.6) (in dB/decade, mean±SD, P<0.05). These results were consistent with those predicted by the derivative-sigmoidal model where the deviation of mean input pressure from the center of the sigmoidal nonlinearity reduced the transfer gain mainly in the low-frequency range. The derivative-sigmoidal model functionally reproduces the dynamic SNA regulation by the arterial baroreflex over a wide operating range.