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.

钙成像允许在单一宽视场内记录100个神经元，从而产生