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Spike-Timing-Based Computation in Sound Localization

Figure 8

Estimation results in the approximate model.

(A) Comparison of a gammatone-filtered HRIR (blue) and an approximate filter (green; gammatone with best delay and gain). (B, C) Activation of neural assemblies for two particular source locations, as in Figure 6. (C) shows a mistake of the model. (D) Spatial receptive field of a particular neural assembly, as in Figure 6. (E) Preferred interaural delay vs. preferred frequency for neurons in two assemblies tuned to locations differing only by a front-back reversion. (F) Interaural gain difference vs. preferred frequency for the same assemblies. (G–I) Performance of the model, as in Figure 6. (J–L) Estimation results as a function of the number of frequency channels used. Simulations were all performed using 240 channels. To obtain estimates of the error using a smaller number of channels while keeping the same frequency range, a randomly chosen subset of the 240 channels was chosen. Error estimates are averaged over many such random choices. (J) Mean error in azimuth estimates for white noise (red), vowel-consonant-vowel sounds (blue), instruments (green) and pure tones (magenta). (K) Mean error in elevation estimates. (L) Categorization performance discriminating left and right (solid), front and back (dashed) and up and down (dotted). For all classes of sounds except the pure tones, the left/right categorization performance is 100% for all points.

Figure 8