Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Nat Neurosci. 2019 Dec;22(12):2040-2049. doi: 10.1038/s41593-019-0533-x. Epub 2019 Nov 25.
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.
内部状态塑造了刺激反应和决策，但我们缺乏识别它们的方法。为了解决这一差距，我们开发了一种从行为数据中识别内部状态的无监督方法，并将其应用于动态社会互动中。在求爱过程中，黑腹果蝇雄蝇利用来自伴侣的反馈线索来调整它们的歌声。我们的模型揭示了这种行为背后的三个潜在状态，并能够预测歌声模式决策的瞬间变化。这些状态对应于不同的感觉运动策略，每个策略都有不同的从反馈线索到歌曲模式的映射。我们表明，以前被认为是歌唱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。我们的结果揭示了动物如何从以前未被识别的内部状态中组合行为，这是对动物行为进行定量描述的必要步骤，这种描述将环境线索、内部需求、神经元活动和运动输出联系起来。
