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Figure 7. Comparison of the response-gain data with model predictions and behavior. A, The gain Equation 3 of the high-pass filter generated by adaptation (solid line) as a function of stimulus frequency. The averaged value of the measured effective adaptation time constants sets the cutoff frequency fcutoff of the gain function to 23 Hz (vertical line in all panels). Chirps are high-frequency signals (gray area) that are transmitted with a high gain. B, The response gain Equation 6 as a function of positive beat frequencies 
f estimated from the high-pass filter shown in A. The dashed line is the response gain for 14-ms-wide chirps that generate a phase shift of 1, and the gray area is for phase shifts ranging from 0.25 to 1.5. C, The response gain from B (dashed line and gray area) explains the decay of the observed response gain only qualitatively (filled circles; median with 2nd and 3rd quartiles). For the 18 cells in which f-I curves were measured, we computed the response gains as predicted by the models. Using the adaptation model Equation 1, thus taking the saturating f-I curves into account, does not improve the match (squares). However, the additional low-pass filter properties introduced by spikes, modeled using the perfect integrator Equation 4, reduce the predicted response gains significantly (triangles), resulting in a much better match to the actually observed data. The variability of the response-gain data can be mainly attributed to the different sized chirps (compare error bars to the width of the gray area). D, The probability of a male fish emitting chirps as a function of beat frequency as reported by Bastian et al. (2001), their Fig. 3A.
