DiTullio Ronald W, Parthiban Chetan, Piasini Eugenio, Chaudhari Pratik, Balasubramanian Vijay, Cohen Yale E
David Rittenhouse Laboratory, Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, United States.
Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA, United States.
Front Comput Neurosci. 2023 May 4;17:1150300. doi: 10.3389/fncom.2023.1150300. eCollection 2023.
Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or "objects," that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than conventional feature-selection algorithms, i.e., principal component analysis and independent component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes and that the auditory brain could utilize these slowly changing temporal features.
感觉系统似乎学会将传入的感觉信息转化为感知表征或“对象”，这些表征或“对象”能够在最少明确监督的情况下为行为提供信息并加以引导。在此，我们提出听觉系统可以通过将时间用作监督者来实现这一目标，也就是说，通过学习具有时间规律性的刺激特征。我们将证明，这一过程会生成一个足以支持听觉感知基本计算的特征空间。具体而言，我们考虑区分一类典型自然听觉对象（即恒河猴发声）实例的问题。我们在两个与行为学相关的任务中测试辨别能力：在杂乱声学背景下的辨别以及对新范例进行辨别的泛化能力。我们表明，一种学习这些时间规律性特征的算法比传统特征选择算法（即主成分分析和独立成分分析）具有更好或相当的辨别和泛化能力。我们的研究结果表明，听觉刺激的缓慢时间特征可能足以解析听觉场景，并且听觉大脑可以利用这些缓慢变化的时间特征。
