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）进行目标导向行动的决策是目标导向行为的基本组成部分