Machine Learning for Bird Song Learning

鸣禽，包括我们熟悉的物种，如苍头燕雀和大山雀，都和我们有一种不寻常的能力：声乐学习。像我们一样，鸟类需要倾听和模仿其他动物，以发展它们的声音交流信号。大多数哺乳动物和脊椎动物都不能做到这一点，包括除人类以外的所有其他灵长类动物。近年来，对鸣声学习的发展、神经生物学和遗传学的研究揭示了人类语言和鸟类鸣声之间更深层次的联系——以至于鸟类鸣声目前代表了我们理解语言生物学的最佳动物模型。为了研究鸟类的鸣叫，研究人员需要准确地测量不同鸣叫之间的差异。需要这些测量来评估一只鸟是否真的模仿另一只鸟，以及它们模仿的精确程度。然而，开发计算机算法来进行这种测量是困难的，其原因与语音识别对计算机来说是一项困难的任务的许多原因相同。在这项拨款中，我们将使用一种新的方法来解决这个问题-受到语音识别发展的启发。首先，我们将训练鸟类啄食按钮以获得鸟类喂食器的食物奖励，然后进一步训练它们区分鸟叫声中的不同“音符”。然后，我们将训练“机器学习”计算机算法来复制鸟类的决策。因此，我们将开发一种计算机算法，我们可以用一种经过生物学验证的方式来比较鸟类的叫声。然后，我们将使用我们的算法来研究鸟类是如何学习它们的歌曲的。为了做到这一点，我们将利用数据集，在这些数据集中，研究人员简单地记录了种群中不同鸟类所唱的不同歌曲。这些数据包含了鸟类如何学习它们的歌曲的特征，就像我们的基因组包含了我们进化历史的特征一样。我们将利用统计技术结合模拟模型来推断鸟类是如何学习鸣叫的：由于错误或创新，它们产生新歌类型的频率；他们喜欢向谁学习；以及他们更喜欢学哪首歌。我们将对15个不同的物种和种群做这个实验，让我们第一次比较不同的群体是如何学习他们的歌曲的。

Songbirds, including familiar species like chaffinches and great tits, share an unusual ability with us: vocal learning. Like us, birds need to hear and imitate others in order to develop their vocal communication signals. Most mammal and vertebrate species cannot do this, including all other primate species apart from us. In recent years, research into the development, neurobiology, and genetics of song learning have revealed ever deeper links between human speech and bird song - so much so that bird song currently represents the best animal model we have for understanding the biology of speech. In order to study bird song, researchers need to accurately measure how different songs are from each other. These measures are needed to assess whether one bird really did imitate another, and how precisely they did so. Developing computer algorithms to make such measurements is difficult, however, for many of the same reasons that speech recognition is a difficult task for computers. In this grant, we will use a new approach to solve this problem - inspired by developments in speech recognition. First we will train birds to peck on buttons to get a food reward from a bird feeder, and then train them further to discriminate between different "notes" within bird songs. Then we will train "machine learning" computer algorithms to replicate the birds' decisions. We will thus develop a computer algorithm that we can use to compare bird songs in a way that is biologically validated.We will then use our algorithm to investigate how birds learn their songs. To do this, we will make use of data-sets where researchers have simply recorded the different songs sung by birds within the population. This data contains a signature of how the birds actually learned their songs in much the same way that our genomes contain signatures of our evolutionary history. We will exploit this by using a statistical technique in combination with simulation models to infer how birds learn their songs: how frequently they generate new song types due to errors or innovations; who they prefer to learn from; and which songs they prefer to learn. We will do this for 15 different species and populations, allowing us to compare how different groups learn their songs for the first time.