A linear-nonlinear (LN) cascade model reveals that central vestibular neurons respond nonlinearly to broadband noise stimulation.

<p>(A) Schematic showing the LN model's assumptions. The stimulus (left) is convolved with a filter <i>H(t)</i> that is given by the inverse Fourier transform of the transfer function in order to generate the linear predicted firing rate (middle). This linear prediction is then passed through a static function f (which can be linear or nonlinear) to give rise to the predicted output firing rate (right). (B) Population-averaged function f for afferents. Also shown is the best-fit line (<i>R</i>² = 0.998±0.001, <i>n</i> = 15) (red) whose slope did not significantly differ from unity (<i>p</i> = 0.99, <i>n</i> = 15, pairwise <i>t</i> test). Inset: population-averaged filter <i>H(t)</i> for afferents. (C) Population-averaged function f for central VO neurons. Also shown are the best-fit straight lines for the intervals (0–80 Hz) and (80–160 Hz) (red) whose slopes were significantly different from one another (<i>p</i> = 0.0014, <i>n</i> = 13, pairwise <i>t</i> test). Inset: population-averaged filter <i>H(t)</i> for central VO neurons.