Smith Samuel S, Sollini Joseph, Akeroyd Michael A
Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
Front Neurosci. 2023 Jan 26;17:1000079. doi: 10.3389/fnins.2023.1000079. eCollection 2023.
The binaural system utilizes interaural timing cues to improve the detection of auditory signals presented in noise. In humans, the binaural mechanisms underlying this phenomenon cannot be directly measured and hence remain contentious. As an alternative, we trained modified autoencoder networks to mimic human-like behavior in a binaural detection task. The autoencoder architecture emphasizes interpretability and, hence, we "opened it up" to see if it could infer latent mechanisms underlying binaural detection. We found that the optimal networks automatically developed artificial neurons with sensitivity to timing cues and with dynamics consistent with a cross-correlation mechanism. These computations were similar to neural dynamics reported in animal models. That these computations emerged to account for human hearing attests to their generality as a solution for binaural signal detection. This study examines the utility of explanatory-driven neural network models and how they may be used to infer mechanisms of audition.
双耳系统利用双耳时间线索来提高对噪声中呈现的听觉信号的检测能力。在人类中，这种现象背后的双耳机制无法直接测量，因此仍存在争议。作为一种替代方法，我们训练了改进的自动编码器网络，使其在双耳检测任务中模仿人类行为。自动编码器架构强调可解释性，因此我们“打开它”，看看它是否能推断出双耳检测背后的潜在机制。我们发现，最优网络自动开发出了对时间线索敏感且动态与互相关机制一致的人工神经元。这些计算与动物模型中报道的神经动力学相似。这些计算的出现是为了解释人类听觉，证明了它们作为双耳信号检测解决方案的普遍性。本研究探讨了解释驱动神经网络模型的效用，以及它们如何用于推断听觉机制。
