Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States.
MIT Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, United States.
Elife. 2021 Nov 18;10:e62889. doi: 10.7554/eLife.62889.
To adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in . By imaging ensemble-level neural activity in freely moving animals, we identify stereotyped changes in circuit activity corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the neural activity patterns observed in each state. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.
为了适应环境,动物必须产生与快速变化的感觉世界紧密匹配的行为。然而,觅食或求偶等行为状态通常会持续很长时间尺度,以确保正确执行。目前尚不清楚神经回路如何在保持对感觉环境变化时选择替代状态的灵活性的同时,产生持久的行为状态。在这里,我们阐明了控制漫游和停留状态之间选择的神经回路的功能结构,这是觅食过程中探索和利用的基础。通过对自由移动动物的整体水平神经活动进行成像,我们确定了与每种行为状态相对应的刻板的电路活动变化。通过全电路成像与遗传分析相结合,我们发现两种拮抗的神经调质系统之间的相互抑制是观察到的每种状态下神经活动模式的持久性和互斥性的基础。通过机器学习分析和电路干扰,我们鉴定出一个感觉处理神经元,它可以将食物气味的信息传递给漫游和停留电路,并根据不同的感觉环境将动物偏向不同的状态,从而产生适当的状态转换。我们的发现揭示了一种潜在的通用电路结构,它能够灵活地、受感觉驱动地控制持久的行为状态。