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。RL理论的核心是勘探和开发之间的平衡，这使得 代理通过反复试验来解释环境，以学习最大化回报的最佳策略。 在RL模型中确定何时在勘探/开采状态之间切换的最佳参数 这是困难的，因此需要新的生物学见解。我们实验室的最新研究表明蓝斑 信号探索和开发状态下的去甲肾上腺素释放(LC-NE)。LC-NE神经元呈时相变化 当呈现不确定的刺激证据以促进任务执行/探索时RL任务中的活动， 并在收到令人惊讶的强化以促进下一次试验中的任务优化/利用之后。多么 这些不同相位的LC-NE信号在目标区域中被整合以调制行为的不同方面 未知。一种可能是通过星形胶质细胞的时空整合，星形胶质细胞对去甲肾上腺素高度反应， 已知与学习和记忆有关，并可以在试验内和 审判之间的时间表。在这里，我们认为在RL任务期间LC-NE的释放导致大脑皮层的变化 通过星形细胞信号促进的网络动态，支持任务执行和优化。我们会 研究LC-NE和星形胶质细胞对神经元种群动力学和RL的影响 额叶/前额叶皮质星形胶质细胞和神经元高密度双双光子成像结合方法 神经记录，神经元和星形胶质细胞的光发生和化学发生操作，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， 我们将开发学习过程中星形胶质细胞-神经元相互作用的生物信息计算模型 行为。在目标1中，我们将记录执行RL任务的小鼠的大脑皮层星形胶质细胞和神经元。我们将使用 高密度单单元记录和种群分析，以确定种群动态如何在 不同的任务时代。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和光遗传操作 星形胶质细胞钙，以确定星形胶质细胞动力学如何影响RL行为，以及这种活动如何影响 神经元种群动力学。在目标3中，我们将检验通过NE扩展RL算法的假设-- 星形胶质细胞信号可以在低刺激证据下解释探索，而且NE和星形胶质细胞的相互作用 试验将反映在政策梯度学习规则中，以促进剥削。最后，我们将确定 将NE-星形胶质细胞-神经元的相互作用纳入递归神经网络模型是否能够提供丰富的 建立行为模型，并确定对我们观察到的行为结果至关重要的回路主题。这些数据将提供 对去甲肾上腺素和星形胶质细胞在关键行为功能中的作用的前所未有的看法，并通过 他们的功能障碍可以在大脑紊乱和疾病中得到改善。

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.