Characterizing the underlying population code to understand the functional organization of the hippocampus and the lateral hypothalamus

表征潜在的群体代码以了解海马和下丘脑外侧的功能组织

• 批准号: 10371262
• 负责人: Horng-An Edward Nieh
• 金额: $ 15.36万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10371262
• 关键词: Affect; Algorithms; Anatomy; Animal Behavior; Animals; Appetitive Behavior; Area; Behavior; Behavioral; Brain; Calcium; Cells; Characteristics; Chronic; Cocaine; Code; Complex; Computer Models; Computing Methodologies; Data; Decision Making; Detection; Dimensions; Dorsal; Drug Addiction; Drug Modelings; Exhibits; Female; Food; Goals; Heterogeneity; Hippocampus (Brain); Hypothalamic structure; Image; Imaging technology; Implant; Individual; Indwelling Catheter; Injections; Intervention; Lateral; Learning; Left; Light; Location; Measures; Methods; Modeling; Mus; Nature; Neurons; Noise; Optics; Pattern; Pharmaceutical Preparations; Phase; Population; Research; Resolution; Rewards; Rodent; Role; Self Administration; Sex Behavior; Sex Differences; Social Interaction; Substance Use Disorder; Technical Expertise; Techniques; Testing; Therapeutic Intervention; Training; cell type; drinking; drug seeking behavior; experimental study; feeding; high dimensionality; instrumentation; interest; lens; male; microscopic imaging; neuromechanism; novel; optogenetics; place fields; programs; relating to nervous system; reward processing; sex; side effect; skills; targeted treatment; tool; two-photon; virtual reality

Project Summary/Abstract Recent advancements in neural recording/imaging technologies and computational methods have generated a renewed interest in studying coordinated population activity. Understanding the population code can help us better understand the complex mechanisms behind substance use disorders (SUD). One leading idea is that high-dimensional neural activity, such as simultaneous recordings from hundreds to thousands of cells, can lie on low-dimensional manifolds, such that a handful of latent variables can accurately describe the activity of all recorded neurons. The lateral hypothalamus (LH) is a brain area well-known for its functional diversity – individual cells respond with great heterogeneity to a wide range of appetitive behaviors, and LH stimulation can evoke a variety of actions ranging from feeding to social interaction. This project proposes to use the latest nonlinear dimensionality reduction techniques to extract the low-dimensional manifolds representing population activity patterns that geometrically organize the heterogeneous single neuron activity. These manifolds can then be used to achieve this project’s main goal – differentiating LH neural population encoding of natural reward-seeking behaviors and maladaptive drug-seeking behaviors. In addition, novel computational modeling methods will be used to perform unsupervised detection of internal neural states that guide animals switching between these two reward-seeking behaviors. Finally, state-of-the-art cellular-resolution simultaneous stimulation and imaging microscopy will be used to casually perturb animal behavior and/or neural activity patterns by activating sequences of neurons along trajectories on the low-dimensional manifolds. Importantly, Aims 1 and 2 support these goals by offering training in the use of the necessary computational and instrumentation techniques. Ultimately, results obtained from this project will advance our understanding of the neural mechanisms separating harmful drug-seeking behaviors and useful natural reward-seeking behaviors, such that SUD treatments with more precise targets can be developed that minimize unwanted side effects.

项目摘要/摘要 神经记录/成像技术和计算方法的最新进展已产生 对研究协调人口活动的新兴趣。了解人口代码可以帮助我们 更好地了解药物使用障碍背后的复杂机制（SUD）。一个主要想法是 高维神经活动，例如数百到数千个细胞的简单记录 在低维歧管上，少数潜在变量可以准确地描述所有的活性 记录的神经元。下丘脑（LH）是一个以其功能多样性而闻名的大脑区域 - 个体 细胞以极大的异质性对各种食用行为做出反应，而LH刺激可以引起A 从喂养到社交互动的各种行动。该项目提出使用最新的非线性 降低降低技术以提取代表种群活动的低维歧管 几何组织异质单神经元活性的模式。然后可以使用这些歧管 为了实现该项目的主要目标 - 区分自然奖励编码的LH神经体形 行为和不良适应性毒品的行为。此外，新颖的计算建模方法将是 用于执行无监督的检测内部神经状态，这些神经状态引导动物在这两个之间切换 寻求奖励的行为。最后，最先进的细胞分辨率同时刺激和成像 显微镜将通过激活来随便扰动动物行为和/或神经活动模式 在低维歧管上沿轨迹的神经元序列。重要的是，目标1和2支持 这些目标通过在使用必要的计算和仪器技术方面提供培训。 最终，从该项目获得的结果将提高我们对神经机制的理解 分离有害的毒品寻求行为和有用的自然寻求奖励行为，以使SUD 可以开发具有更精确的目标的治疗方法，以最大程度地减少不需要的副作用。