Machine Learning for Analyzing State Dependent Neuronal Network Dynamics

用于分析状态相关神经网络动力学的机器学习

• 批准号: 10825302
• 负责人: Daniel David Carbonero
• 金额: $ 3.98万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10825302
• 关键词: Academic Training; Affect; Anesthesia procedures; Behavioral; Boston; Calcium; Cells; Communication; Complex; Computational Science; Coupled; Data; Development; Dimensions; Engineering; Ensure; Equilibrium; Etiology; Event; Faculty; Frequencies; Grouping; Hippocampus; Image; Individual; Interdisciplinary Study; Isoflurane; Machine Learning; Mathematics; Memory; Mentorship; Methods; Modeling; Mus; Neurons; Neurosciences; Optics; Output; Pattern; Problem Solving; Research; Scientist; Series; Signal Transduction; Somatosensory Cortex; Stimulus; Structure; System; Technical Expertise; Time; Universities; Work; awake; cell type; collaborative environment; conditioned fear; data reduction; discounting; excitatory neuron; experimental study; fear memory; individual response; memory encoding; memory recall; multidimensional data; neural; neural network; optogenetics; response; statistics; two-photon

Calcium imaging allows recording from 100s of neurons in a single wide field of view, giving rise to extremely high dimensionality data. Current analysis standards employ descriptive statistics that summarize neuronal responses into single quantitative metrics, discounting the temporal dynamics of individual cells and local networks. In contrast, machine learning, especially dimensionality reduction models, provide more nuanced analysis that considers the temporal patterns and groupings among cells. While previous work has attempted to reduce the neuronal activity to very low dimensional manifolds, these methods result in outputs that are difficult to understand. In this work, we adapt Non-Negative Matrix Factorization (NMF), an easily interpretable dimensionality reduction method to analyze shifts in neuronal network dynamics that arise as a function of different experimental contexts. We will apply our framework to study the neuronal network dynamics of two different contexts: 1) the primary somatosensory cortex (S1) under increasing concentrations of anesthesia, and 2) the hippocampus during optogenetic stimulation of memory-encoding ensembles of neurons. We have successfully adapted and characterized a series of dimensionality reduction methods and have demonstrated NMF is a superior method to extract underlying structure from calcium recordings. Initial analyses have extracted ordered, low-dimensional, internal structure not detectable with traditional statistics. This research will be conducted at Boston University, taking advantage of the numerous multidisciplinary research centers (Center for Systems Neuroscience, Neurophotonics Center, Rafik B. Hariri Institute for Computing and Computational Science & Engineering). These institutes, consisting of highly diverse and renowned groups of faculty, create a highly collaborative environment for interdisciplinary research, allowing scientists to pursue interesting questions not directly in their expertise. Further, a combination of academic training, development of technical skills, analytical problem solving, scientific communication, professional development, and consistent mentorship will ensure the project has the highest potential to succeed possible.

钙成像允许在单个宽视野中记录数百个神经元，从而产生 极高维数据。当前的分析标准采用描述性统计 将神经元反应总结为单个定量指标，忽略时间 单个细胞和局部网络的动态。相比之下，机器学习，尤其是 降维模型，提供更细致的分析，考虑时间 细胞之间的模式和分组。虽然之前的工作试图减少神经元 活动到非常低维流形，这些方法导致输出难以 理解。在这项工作中，我们采用了非负矩阵分解（NMF），一种简单的方法 可解释的降维方法来分析神经网络动力学的变化 作为不同实验背景的函数而出现。我们将应用我们的框架来研究 两种不同环境下的神经元网络动力学：1）初级体感皮层 (S1) 在麻醉浓度增加的情况下，2) 光遗传学过程中的海马 刺激记忆编码神经元群。我们已经成功适应并 表征了一系列降维方法，并证明了 NMF 是一种 从钙记录中提取基础结构的优越方法。初步分析有 提取有序、低维、传统统计无法检测的内部结构。 这项研究将在波士顿大学进行，利用大量的 多学科研究中心（系统神经科学中心、神经光子学中心、 Rafik B. Hariri 计算和计算科学与工程研究所）。这些 研究所由高度多样化和知名的教师群体组成，创建了一个高度 跨学科研究的协作环境，让科学家能够追求有趣的 问题不直接涉及他们的专业知识。此外，结合学术培训， 发展技术技能、分析问题解决、科学交流、 专业发展和持续的指导将确保该项目具有最高的 成功的潜力成为可能。