Research Project 1 - Developing and applying tools to probe internal state dynamics of perception and motivation

研究项目1-知觉和动机的内部状态动力学 负责人：卡尔·戴瑟斯医学博士 摘要 该项目的重点是探索外部相互作用的技术开发和应用 哺乳动物大脑内部状态的感觉输入。我们团队的独特优势是技术驱动 在定义和调整这些内部状态时，通过实验获得速度、范围和分辨率的新机制。 首先，我们将量化时变自发活动的种群动态和统计结构 横跨初级视觉皮质的体积(由3D空间中的位置和连线定义的单个细胞，以及 分子同一性)来测试自发波动对环境刺激触发的神经元的影响 活动和动物行为。接下来，我们将量化内部状态之间转换期间的大脑皮层活动 (与行为一起)，实施精确定义的集成活动，以评估状态转换对 诱发的种群动态和行为，并量化影响这些状态的内源性活动进行评估 自然机制和效果。最后，我们将把我们的生物学发现和新技术 能够通过控制多个 随着时间的推移，多个皮质区域中的单个细胞。同样的细胞被观察到是活跃的，在行为上很重要 将可用于分子表型分析，以便细胞类型学可能与特定的 细胞在电路计算和行为这一重要领域中的自然和因果作用。

Research Project 1 - Internal state dynamics of perception and motivati0n Lead: Karl Deisseroth MD PhD Summary This project is focused on development and application of technology for probing the interaction of external sensory input with internal states of the mammalian brain. The unique leverage of our team is technology-driven experimental access to new regimes of speed, scope, and resolution in defining and tuning these internal states. First, we will quantify the population dynamics and statistical structure of time-varying spontaneous activity across the volume of primary visual cortex (with individual cells defined by position and wiring in 3D space, and by molecular identity) to test impact of spontaneous fluctuations on environmental stimulus-triggered neuronal activity and animal behavior. Next, we will quantify cortical activity during transitions among internal states (along with behaviors), impose precisely-defined ensemble activity to assess impact of state transitions on evoked population dynamics and behavior, and quantify endogenous activity influencing these states to assess natural mechanisms and effects. Finally, we will integrate our biological findings and new technological capabilities to directly and precisely impose more complex temporal dynamics of interest by controlling multiple single cells in multiple cortical regions over time. The same cells observed to be active and important in behavior will be made available for molecular phenotyping analysis, so that cell typology may be linked to the specific natural and causal roles of cells in this important domain of circuit computation and behavior.