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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结/文摘