美国普林斯顿大学Mala Murthy课题组发现塑造自然行为的内部状态的无监督识别。相关论文于11月25日在线发表在《自然—神经科学》上。

内部状态决定着刺激应答和决策制定，但是缺乏鉴定它们的方法。为了弥补这一空缺，研究人员开发了一种无监督的方法来从行为数据中鉴定内部状态，并将其应用于动态的社会互动中。

在求爱过程中，雄性黑腹果蝇使用来自伴侣的反馈提示来模仿他它们的歌声。他们的模型揭示了此行为的三个潜在状态，并能够预测歌曲模式决策中的瞬间变化。这些状态对应于不同的感觉运动策略，每种策略都具有从反馈提示到歌曲模式的不同映射。

他们证明，以前被认为是歌曲产生的命令神经元的一对神经元足以驱动状态之间的切换。他们的结果揭示了动物如何根据先前无法确定的内部状态来构成行为，这是定量描述将环境线索、内部需求、神经元活动和动力输出联系起来的动物行为的必要步骤。

附：英文原文

Title: Unsupervised identification of the internal states that shape natural behavior

Author: Adam J. Calhoun, Jonathan W. Pillow, Mala Murthy

Issue&Volume: 2019-11-25

Abstract: Internal states shape stimulus responses and decision-making, but we lack methods to identify them. To address this gap, we developed an unsupervised method to identify internal states from behavioral data and applied it to a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using feedback cues from their partner. Our model uncovers three latent states underlying this behavior and is able to predict moment-to-moment variation in song-patterning decisions. These states correspond to different sensorimotor strategies, each of which is characterized by different mappings from feedback cues to song modes. We show that a pair of neurons previously thought to be command neurons for song production are sufficient to drive switching between states. Our results reveal how animals compose behavior from previously unidentified internal states, which is a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity and motor outputs.

DOI: 10.1038/s41593-019-0533-x

Source: https://www.nature.com/articles/s41593-019-0533-x