Analysis and simulation of conscious animal noise.

(A) Left, IOP data of an awake free-moving rat collected previously by the lab [40]. Right, histogram of IOP fluctuations. Thick line is a Gaussian fit of the probability distribution. (B) Unfiltered and 14-min lowpass filtered (thin and thick black lines) step responses of the CPg2 model for the noise record in A. Unfiltered step responses were processed by a recursive regression method based on the eye model in Fig 1 to yield fitted pressure and flow responses (red lines). Filled and unfilled arrowheads indicate settling time of the filtered (black) and fitted (red) responses for the window (19.0 and 7.0 min) and ratio (52.8 and 16.2 min) criteria, respectively. (C) Effect of filter width on average settling time of the various CP models across 12 conscious noise records for the window (thin black line) and ratio (thick black line) criteria. Thin and thick red lines indicate the mean settling time of recursively-fitted model responses for the window and ratio criteria, respectively. Shaded areas give the standard deviation of filtered and fitted response settling times using window (dark gray and dark pink) and ratio (light gray and light pink) criteria. Diamonds mark the filter width with the fastest settling time and least variability (CPg1: 10 min, CPg2: 14 min, CPp: 14 min, CPpx: 13 min).