Neuron-astrocyte mechanisms of norepinephrine in goal-directed learning

去甲肾上腺素在目标导向学习中的神经元星形胶质细胞机制

• 批准号: 10651486
• 负责人: MRIGANKA SUR
• 金额: $ 64.34万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651486
• 关键词: Affect; Astrocytes; Behavior; Behavioral; Biological; Brain; Brain Diseases; Calcium; Calcium Signaling; Complex; Computer Analysis; Computer Models; Corpus striatum structure; Data; Decision Making; Dependence; Development; Dopamine; Electrophysiology (science); Environment; Equilibrium; Exhibits; Functional disorder; Genetic; Goals; Image; Learning; Linear Models; Measures; Mediating; Memory; Methods; Modeling; Motor Cortex; Mus; Neural Network Simulation; Neuroglia; Neuromodulator; Neurons; Norepinephrine; Outcome; Performance; Phase; Policies; Population; Population Analysis; Population Dynamics; Prefrontal Cortex; Psychological reinforcement; Punishment; Rewards; Role; Signal Transduction; Stimulus; Synapses; System; Task Performances; Uncertainty; Update; Work; behavior measurement; behavioral outcome; cell type; density; designer receptors exclusively activated by designer drugs; dimensional analysis; functional outcomes; genetic manipulation; in vivo; innovation; insight; learned behavior; learning algorithm; locus ceruleus structure; millisecond; neural; neuronal circuitry; neuroregulation; neurotransmission; novel; optogenetics; recurrent neural network; response; spatiotemporal; success; theories; two-photon

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.

通过强化学习（RL）为目标指导行动的决策是一个基本组成部分 复杂的行为。 RL理论的核心是探索和剥削之间的平衡，这使得 代理使用反复试验来解释环境，以学习最大程度地提高奖励的最佳策略。 确定何时在RL模型中探索/开发状态切换的最佳参数 很难，因此需要新的生物学见解。我们实验室的最新工作牵涉到基因座 信号探索和剥削状态中的去甲肾上腺素释放（LC-NE）。 LC-NE神经元表现出阶段 当呈现不确定的刺激证据以促进任务执行/勘探时，在RL任务中的活动， 在收到令人惊讶的强化后，以促进下一个试验的任务优化/开发。如何 这些不同的音阶LC-NE信号集成在目标区域中，以调节行为的不同方面IS 未知。一种可能性是通过星形胶质细胞的时空整合，对NE的反应高度响应， 已知与学习和记忆有关，并且可以调节在审判内和 审判时间尺度。在这里，我们建议在RL任务期间释放LC-NE会导致皮质变化 通过星形胶质细胞信号促进的网络动力学，可以实现任务执行和优化。我们将 使用创新的LC-NE和星形胶质细胞对神经元种群动态的影响以及RL的影响 在额叶/前额叶皮层中组合星形胶质细胞和神经元的双2光子成像，高密度的方法 神经和星形胶质细胞的神经记录，光学遗传学和化学遗传操作以及计算 定义LC-NE和星形胶质细胞对神经元种群和任务编码的影响的方法。最后， 我们将在学习期间开发出星形胶质细胞 - 神经元相互作用的生物学知识计算模型 行为。在AIM 1中，我们将记录执行RL任务的小鼠皮质星形胶质细胞和神经元。我们将使用 高密度单单元记录和人口分析，以确定种群动态如何发展 不同的任务时期。使用此信息，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AIM 2中，我们将使用化学发生和光学遗传操作 星形胶质细胞钙确定星形胶质细胞动力学如何促进RL行为，以及该活动如何影响 神经元种群动态。在AIM 3中，我们将研究以下假设：通过NE-扩展RL算法 星形胶质细胞信号可以解释低刺激证据的探索，以及跨越的腹腔细胞相互作用 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 是否将NE-腹腔神经元的相互作用纳入复发性神经网络模型都可以提供丰富 行为模型并确定对我们观察到的行为结果至关重要的电路基序。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，并指出了 它们的功能障碍可以在脑部疾病和疾病中得到改善。