Schilling Achim, Gerum Richard, Metzner Claus, Maier Andreas, Krauss Patrick
Laboratory of Sensory and Cognitive Neuroscience, Aix-Marseille University, Marseille, France.
Neuroscience Lab, University Hospital Erlangen, Erlangen, Germany.
Front Neurosci. 2022 Jun 8;16:908330. doi: 10.3389/fnins.2022.908330. eCollection 2022.
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.
一般认为噪声会损害信息处理性能。然而，在随机共振的背景下，噪声已被证明能改善微弱阈下信号的信号检测能力，并且有人提出大脑可能会积极利用这一现象。特别是在听觉系统中，最近的研究表明，内在噪声在信号处理中起着关键作用，甚至可能与听力损失后在听觉脑干和皮层早期处理阶段观察到的神经元自发放电率增加相对应。在这里，我们提出了一个基于深度神经网络的听觉通路计算模型，并在语音识别上进行了训练。我们模拟了不同程度的听力损失，并研究了内在噪声的影响。值得注意的是，听力损失后的语音识别实际上会随着额外的内在噪声而提高。这一惊人结果表明，内在噪声不仅可能在人类听觉处理中起关键作用，甚至可能对当代机器学习方法有益。
