Description:
My laboratory is interested in the neural basis of complex sound perception, including vocalizations. Our neural studies have focused on the high-level auditory system of songbirds where neurons respond preferentially to birdsong. We quantified the selectivity of these auditory neurons by recording their responses to degraded versions of song. To do so, we added noise to this natural stimulus, affecting the time-frequency structure of the sound at different scales. We found that at particular time-frequency scales the noise has little effect on the response of the neurons, while at other scales it greatly reduces the neural response. We correlated the tuning of the neurons with the statistical structure in the birdsongs that allows the discrimination of songs produced by different birds. We found a good match between the optimal scale of the neurons sensitivity curve and the scale that best captures the variance in the acoustical structure of an ensemble of songs. We have done a similar analysis with speech, where the neural tuning curve in songbirds is replaced by a human perceptual tuning curve based on speech intelligibility. As in songbirds, the scale for optimal speech perception is also best for capturing the statistical structure in the speech signal.