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| Content Provider | frontiers |
|---|---|
| Author | Schilling, Achim Gerum, Richard Metzner, Claus Maier, Andreas Krauss, Patrick |
| Abstract | Noise is generally considered to harm information processing performance. However, in the context of stochastic resonance, noise has been shown to improve signal detection of weak sub- threshold signals, and it has been proposed that the brain might actively exploit this phenomenon. Especially within the auditory system, recent studies suggest that intrinsic noise plays a key role in signal processing and might even correspond to increased spontaneous neuronal firing rates observed in early processing stages of the auditory brain stem and cortex after hearing loss. Here we present a computational model of the auditory pathway based on a deep neural network, trained on speech recognition. We simulate different levels of hearing loss and investigate the effect of intrinsic noise. Remarkably, speech recognition after hearing loss actually improves with additional intrinsic noise. This surprising result indicates that intrinsic noise might not only play a crucial role in human auditory processing, but might even be beneficial for contemporary machine learning approaches. |
| ISSN | 1662453X |
| DOI | 10.3389/fnins.2022.908330 |
| Volume Number | 16 |
| Journal | Frontiers in Neuroscience |
| Language | English |
| Publisher Date | 2022-06-08 |
| Access Restriction | Open |
| Subject Keyword | Noise Tinnitus mechanisms Auditory Perception Stochastic resonance Deep artificial neural networks Hearing Loss Dorsal cochlear nucleus Zwicker tone Speech Processing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Neuroscience |
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