Neuron-astrocyte mechanisms of norepinephrine in goal-directed learning

通过强化学习（RL）进行目标导向行动的决策是 复杂的行为强化学习理论的核心是探索和利用之间的平衡，这使得 智能体使用试错法来解释环境，以学习最大化奖励的最佳策略。 确定RL模型中何时在勘探/开采状态之间切换的最佳参数， 这是很困难的，因此需要新的生物学见解。我们实验室最近的研究表明蓝斑 去甲肾上腺素释放（LC-NE）在信号探索和开发状态。LC-NE神经元表现出阶段性 当呈现不确定的刺激证据以促进任务执行/探索时， 并且在接收到令人惊讶的强化之后，以促进下一次试验中的任务优化/利用。如何 这些不同的相位LC-NE信号被整合在靶区域中以调节行为的不同方面， 未知一种可能性是通过星形胶质细胞的时空整合，星形胶质细胞对NE高度反应， 已知参与学习和记忆，并可调节试验内和试验后的神经元活动， 试验间隔时间。在这里，我们提出，LC-NE释放在RL任务引起的变化，皮层 网络动态，通过星形胶质细胞信号传导促进，使任务执行和优化。我们将 研究LC-NE和星形胶质细胞对神经元群体动力学和RL的影响， 结合额叶/前额叶皮层星形胶质细胞和神经元的双双光子成像的方法，高密度 神经记录，神经元和星形胶质细胞的光遗传学和化学遗传学操纵，以及计算 确定LC-NE和星形胶质细胞对神经元群体和任务编码的影响的方法。最后， 我们将在学习过程中开发星形胶质细胞-神经元相互作用的生物学信息计算模型， 行为在目标1中，我们将记录执行RL任务的小鼠的皮质星形胶质细胞和神经元。我们将使用 高密度的单一单位记录和人口分析，以确定人口动态如何演变， 不同的任务时期。利用这些信息，我们将确定沉默LC-NE如何影响星形胶质细胞和神经元 RL期间的计算和动力学。在目标2中，我们将使用化学遗传学和光遗传学操作， 星形胶质细胞钙，以确定星形胶质细胞动力学如何促进RL行为，以及这种活动如何影响 神经元群体动力学在目标3中，我们将研究通过NE扩展RL算法的假设， 星形胶质细胞信号可以解释在低刺激证据下的探索，并且NE-星形胶质细胞之间的相互作用 试验将反映在政策梯度学习规则，以促进剥削。最后，我们将确定 将NE-星形胶质细胞-神经元相互作用纳入递归神经网络模型是否可以提供丰富的 行为模型，并识别对我们观察到的行为结果至关重要的电路图案。这些数据将提供 NE和星形胶质细胞在关键行为功能中的作用的前所未有的观点，并指出方法， 它们的功能障碍可以在大脑障碍和疾病中得到改善。

Decision-making for goal-directed actions via reinforcement learning (RL) is a fundamental component of complex behaviors. Central to RL theory is the balance between exploration and exploitation, which enables agents to interpret the environment using trial and error to learn an optimal strategy for maximizing reward. Determining the optimal parameters for when to switch between exploration/exploitation states in RL models has been difficult, and thus requires new biological insights. Recent work from our lab implicates locus coeruleus norepinephrine release (LC-NE) in signaling exploration and exploitation states. LC-NE neurons exhibit phasic activity in an RL task when presented with uncertain stimulus evidence to facilitate task execution/exploration, and after receiving a surprising reinforcement to facilitate task optimization/exploitation on the next trial. How these different phasic LC-NE signals are integrated in target regions to modulate different aspects of behavior is unknown. One possibility is through spatiotemporal integration by astrocytes, which are highly responsive to NE, are known to be involved in learning and memory, and can modulate neuronal activity on within-trial and between-trial timescales. Here, we propose that LC-NE release during an RL task causes changes in cortical network dynamics, facilitated through astrocyte signaling, that enable task execution and optimization. We will examine the effects of LC-NE and astrocytes on neuronal population dynamics and RL using innovative approaches combining dual 2-photon imaging of astrocytes and neurons in frontal/prefrontal cortex, high density neural recordings, optogenetic and chemogenetic manipulation of neurons and astrocytes, and computational approaches to define the effects of LC-NE and astrocytes on neuronal populations and task encoding. Finally, we will develop biologically informed computational models of astrocyte-neuron interactions during learned behavior. In Aim 1, we will record cortical astrocytes and neurons in mice performing our RL task. We will use high density single-unit recordings and population analyses to determine how population dynamics evolve during different task epochs. Using this information, we will determine how silencing LC-NE affects astrocyte and neuron computations and dynamics during RL. In Aim 2, we will use chemogenetic and optogenetic manipulations of astrocyte calcium to determine how astrocyte dynamics contribute to RL behaviors, and how this activity affects neuronal population dynamics. In Aim 3, we will examine the hypothesis that extending RL algorithms via NE- astrocyte signals can explain exploration at low stimulus evidence, and that NE-astrocyte interactions across trials would be reflected in policy gradient learning rules to promote exploitation. Finally, we will determine whether incorporating NE-astrocyte-neuron interactions into a recurrent neural network model can provide a rich model for behavior and identify circuit motifs critical to our observed behavioral outcomes. These data will provide an unprecedented view of the role of NE and astrocytes in a crucial behavioral function, and point to ways by which their dysfunction can be ameliorated in brain disorders and diseases.