Feeding requires sophisticated orchestration of neural processes to satiate appetite in natural, capricious settings. However, the complementary roles of discrete neural populations in orchestrating distinct behaviors and motivations throughout the feeding process are largely unknown. Here, we delineate the behavioral repertoire of mice by developing a machine-learning-assisted behavior tracking system and show that feeding is fragmented and divergent motivations for food consumption or environment exploration compete throughout the feeding process. An iterative activation sequence of agouti-related peptide (AgRP)-expressing neurons in arcuate (ARC) nucleus, GABAergic neurons in the lateral hypothalamus (LH), and in dorsal raphe (DR) orchestrate the preparation, initiation, and maintenance of feeding segments, respectively, via the resolution of motivational conflicts. The iterative neural processing sequence underlying the competition of divergent motivations further suggests a general rule for optimizing goal-directed behaviors.

进食需要复杂的神经过程协调，才能在自然、多变的环境中满足食欲。然而，在整个喂养过程中，离散神经群体在协调不同行为和动机方面的互补作用在很大程度上是未知的。在这里，我们通过开发机器学习辅助行为跟踪系统来描述小鼠的行为库，并表明进食是零散的，并且在整个进食过程中竞争食物消费或环境探索的不同动机。刺豚鼠相关肽 (AgRP) 的迭代激活序列-弓状核 (ARC) 核中的神经元、下丘脑外侧 (LH) 中的 GABA 能神经元和中缝背侧 (DR) 协调喂养段的准备、启动和维持， 分别，通过解决动机冲突。不同动机竞争背后的迭代神经处理序列进一步提出了优化目标导向行为的一般规则。