Figure 3. Integration across frequencies.
(A) Mean error in estimating ITD for white noise using the smoothed peak (blue), hemispheric (red), and pattern match (green) decoders, as a function of the number of binaural cells. Training and testing of the decoders are both performed using white noise. (B) Mean error as a function of frequency band when decoders are trained on white noise but tested on band-pass noise centered at the given frequency. Notice the different vertical scale between (A and B). (C) Performance when cells with a frequency above the cutoff are discarded. (D) Mean error and bias to center in the decoders for guinea pig (with a maximum frequency of 1.2 kHz) when trained on white noise and tested on colored noise.
Figure 3—figure supplement 1. Comparison with standard machine learning decoders.
As for Figure 3, except that the main decoders are shown in light colors, and there are two additional decoders: ridge regression (magenta) and nearest neighbor regression (black).