Abstract
The auditory system converts the physical properties of a sound waveform to neural activities and processes them for recognition. During the process, the tuning to amplitude modulation (AM) is successively transformed by a cascade of brain regions. To test the functional significance of the AM tuning, we conducted single-unit recording in a deep neural network (DNN) trained for natural sound recognition. We calculated the AM representation in the DNN and quantitatively compared it with those reported in previous neurophysiological studies. We found that an auditory-system-like AM tuning emerges in the optimized DNN. Better-recognizing models showed greater similarity to the auditory system. We isolated the factors forming the AM representation in the different brain regions. Because the model was not designed to reproduce any anatomical or physiological properties of the auditory system other than the cascading architecture, the observed similarity suggests that the AM tuning in the auditory system might also be an emergent property for natural sound recognition during evolution and development.SIGNIFICANCE STATEMENT This study suggests that neural tuning to amplitude modulation may be a consequence of the auditory system evolving for natural sound recognition. We modeled the function of the entire auditory system; that is, recognizing sounds from raw waveforms with as few anatomical or physiological assumptions as possible. We analyzed the model using single-unit recording, which enabled a fair comparison with neurophysiological data with as few methodological biases as possible. Interestingly, our results imply that frequency decomposition in the inner ear might not be necessary for processing amplitude modulation. This implication could not have been obtained if we had used a model that assumes frequency decomposition.
Highlights
Natural sounds such as speech and environmental sounds exhibit rich patterns of amplitude modulation (AM) (Fig. 1a) (Varnet et al, 2017)
Functional model of the auditory system The deep neural network (DNN) was trained to classify raw sound data of nonhuman natural sounds consisting of animal vocalizations and environmental sounds
We found that a DNN optimized for natural sound recognition exhibits an AM tuning similar to that of the auditory system throughout the entire cascade of layers
Summary
The auditory system converts the physical properties of a sound waveform to neural activities and processes them for recognition. To test the functional significance of the AM tuning, we conducted single-unit recording in a deep neural network (DNN) trained for natural sound recognition. Because the model was not designed to reproduce any anatomical or physiological properties of the auditory system other than the cascading architecture, the observed similarity suggests that the AM tuning in the auditory system might be an emergent property for natural sound recognition during evolution and development. Significance Statement This study suggests that neural tuning to amplitude modulation may be a consequence of the auditory system evolving for natural sound recognition. Our results imply that frequency decomposition in the inner ear might not be necessary for processing amplitude modulation This implication could not have been obtained if we had used a model that assumes frequency decomposition
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