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.

动作电位触发的发射器释放构成了神经元通信的标志。这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，概率囊泡释放，并随机补充囊泡池。这些噪声机制如何结合影响神经元交流的忠诚是一个有趣的基本问题。钥匙该项目的重点是使用随机混合系统（SHS）的数学形式主义将连续动力学与离散的随机事件结合起来，用于建模突触传输。这基于SHS的形式主义将用于得出将突触噪声机制连接到的分析结果神经递质水平的随机性及其对响应时间的时间精度的影响突触后神经元。该项目还将开发出新的推理方法急性脑切片中的全细胞贴片钳记录的神经传递参数老鼠。将数学模型与持久高频激活的实验数据集成输入神经元将用于表征各种听觉和非审计的神经传递突触类型。这种跨学科的方法与遗传和药理学操纵结合神经递质释放，再摄取和囊泡补充 - 将系统地揭示这些在单细胞级别的信息处理中的过程以及听觉脑干如何突触在长时间的刺激过程中，实现了精美的高保真度。总共，该项目将揭示听觉突触的非凡能力，因此为更好地理解中央而构成了基础听觉处理障碍。相关性（请参阅说明）：听力障碍是最普遍的感觉赤字，具有重大的社会经济影响。为了了解听力是如何发生的，我们必须获得有关神经元的全面知识中央听觉系统中的信息处理。该项目将彻底解决突触通过将经验工作与计算建模相结合，涉及声音本地化的过程，我们将对我们的目标实现迄今未达到的协同作用。