Janata P
Department of Organismal Biology and Anatomy, University of Chicago, Illinois 60637, USA.
J Acoust Soc Am. 2001 Nov;110(5 Pt 1):2593-603. doi: 10.1121/1.1412446.
To understand the mechanisms of song learning by songbirds it is necessary to have in hand tools for extracting, describing, and quantifying features of the developing vocalizations. The extremely large number of vocalizations produced by juvenile zebra finches and the variability in these vocalizations during the sensorimotor learning period preclude manual scoring methods. Here we describe an approach for classification of vocalizations produced during sensorimotor learning based on self-organizing neural networks. This approach allowed us to construct probability distributions of spectrotemporal features recorded on each day. By training the network with samples obtained across the course of vocal development in individual birds, we observed developmental trajectories of these features. The emergence of stereotypy in sequences of song elements was captured by computing the entropy in the matrices of first- and second-order transition probabilities. Self-organizing maps may assist in classifying large libraries of zebra finch vocalizations and shedding light on mechanisms of vocal development.
为了理解鸣禽学习鸣叫的机制，有必要掌握用于提取、描述和量化发育中发声特征的工具。幼年斑胸草雀产生的发声数量极多，且在感觉运动学习期这些发声具有变异性，这使得人工评分方法不可行。在此，我们描述一种基于自组织神经网络对感觉运动学习期间产生的发声进行分类的方法。这种方法使我们能够构建每天记录的频谱时间特征的概率分布。通过用个体鸟类在发声发育过程中获得的样本训练网络，我们观察到了这些特征的发育轨迹。通过计算一阶和二阶转移概率矩阵中的熵，捕捉到了鸣叫元素序列中刻板模式的出现。自组织映射可能有助于对斑胸草雀发声的大型库进行分类，并阐明发声发育的机制。
