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CRCNS: Mechanistic Modeling and Inference of Neuronal Synaptic Transmission

CRCNS：神经元突触传递的机制建模和推断

基本信息

批准号：
10426127
负责人：
Abhyudai Singh
金额：
$ 11.1万
依托单位：
UNIVERSITY OF DELAWARE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10426127
关键词：
Action Potentials Acute Address Adolescent Age Auditory Auditory system Biological Models Biological Process Brain Brain Stem Cells Central Auditory Processing Disorder Communication Computer Models Coupled Data Derivation procedure Development Docking Electrophysiology (science)Engineering Event Frequencies Genetic Goals Hearing Hippocampus (Brain)Hybrids Instruction Interneurons Investigation Joints Knowledge Literature Mathematics Membrane Potentials Methods Modeling Mus Names Neurons Neurotransmitters Noise Pharmacology Play Process Protocols documentation Recovery Research Role Sensory Shapes Slice Sound Localization Source Synapses Synaptic Transmission Synaptic Vesicles System Time Training Variant Vesicle Work base data exchange experimental study hearing impairment information processing interdisciplinary approach mathematical model neurotransmission neurotransmitter release neurotransmitter uptake novel patch clamp postsynaptic neurons presynaptic neurons response socioeconomics synaptic depression tool uptake vesicular release

项目摘要

Action potential-triggered transmitter release forms a hallmark of interneuronal communication. The release is critically impacted by diverse noise mechanisms, such as random arrival of action potentials, probabilistic vesicle release, and random replenishment of vesicle pools. How these noise mechanisms combine to impact fidelity of interneuronal communication is an intriguing fundamental problem. A key focus of this project is to use the mathematical formalism of Stochastic Hybrid Systems (SHS) that combine continuous dynamics with discrete random events for modeling synaptic transmission. The SHS-based formalism will be used to derive analytical results connecting synaptic noise mechanisms to randomness in the neurotransmitter levels, and its impact on temporal precision of the responses in the postsynaptic neuron. The project will also develop novel inference methods for inferring neurotransmission parameters from whole-cell patch-clamp recordings in acute brain slices of juvenile mice. Integration of mathematical models with experimental data on long-lasting high-frequency activation of input neurons will be used to characterize neurotransmission at various auditory and non-auditory synapse types. This interdisciplinary approach--coupled with genetic and pharmacological manipulation of neurotransmitter release, re-uptake, and vesicle replenishment--will systematically uncover the role of these processes in information processing at the single-cell level and how auditory brainstem synapses achieve exquisitely high fidelity during prolonged stimulation. Altogether, the project will reveal the extraordinary capabilities of auditory synapses and thus form a basis for a better understanding of central auditory processing disorders. RELEVANCE (See instructions): Hearing impairment is the most prevalent sensory deficit, with major socioeconomic impact. In order to understand how hearing happens, we must obtain a comprehensive knowledge about neuronal information processing in the central auditory system. The project will thoroughly address synaptic processes involved in sound localization by combining empirical work with computational modeling, and we will achieve hitherto unreached synergistic effects towards our goal.

动作电位触发的递质释放形成了神经元间通讯的标志。这个释放受到各种噪声机制的严重影响，例如动作电位的随机到达，囊泡的概率释放和囊泡池的随机补充。这些噪音机制是如何联合起来影响神经元间通讯的保真度是一个耐人寻味的基本问题。一把钥匙本项目的重点是使用随机混合系统(SHS)的数学形式将连续动力学与离散随机事件相结合来模拟突触传递。这个基于SHS的形式主义将被用来导出将突触噪声机制与神经递质水平的随机性及其对反应时间精确度的影响突触后神经元。该项目还将开发新的推理方法来进行推理幼年急性脑片全细胞膜片钳记录的神经传递参数老鼠。长时间高频激活的数学模型与实验数据的结合将用来描述不同听觉和非听觉的神经传递突触类型。这种跨学科的方法--再加上遗传和药物操作神经递质的释放、重新摄取和囊泡补充--将系统地揭示这些在单细胞水平的信息处理过程以及听觉脑干突触是如何在长时间的刺激过程中达到极高的保真度。总之，该项目将揭示听觉突触的非凡能力，从而形成了更好地理解中枢听觉处理障碍。相关性(请参阅说明)：听力障碍是最常见的感觉障碍，具有重大的社会经济影响。为了要了解听力是如何发生的，我们必须获得关于神经元的全面知识中枢听觉系统中的信息处理。该项目将彻底解决Synaptic 通过将经验工作与计算建模相结合来涉及声音定位的过程，以及我们将朝着我们的目标取得前所未有的协同效应。