Figure 3.

a–c, Schematic illustration of the framework of the present study, consisting of three stages. Humans have evolved and developed the ability to precisely recognize natural sounds (a). We realized a computational simulation of this process by optimizing a model for natural sound recognition. Specifically, we used a deep NN that takes a sound waveform as input and estimates its category. We froze the learned parameters and measured the AM sensitivity in the NN by using the same procedure as in human psychophysical experiments (b). A TMTF was computed for each layer. It was compared with previously reported human AM-sensitivity data in an attempt to answer why AM sensitivity has emerged in humans in its current form. We measured neurophysiological AM tuning in the units in the NN by using the same procedure as in animal neurophysiological experiments (c). On the basis of the similarity of the AM tuning with the auditory brain regions and the results of the psychophysical experiments, we could infer possible neural mechanisms underlying behavioral AM sensitivity.