Tools to decipher neuronal signaling and compoutation

破译神经元信号和计算的工具

• 批准号: RGPIN-2015-04499
• 负责人: DeKoninck, Yves
• 金额: $ 7.43万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648825
• 关键词: Tools; decipher; neuronal; signaling; compoutation

The objective of my NSERC program is to understand the biophysical determinants of neural signalling and computation. That is, to identify the substrates of cell to cell signalling and of signal integration that underlie information processing in neural circuits. In this context, my research has focused on 1) how the structural and functional organizations of synapses affects information transfer between cells and 2) how synaptic mechanisms interact with the intrinsic properties of nerve cells to shape information coding. To achieve this, I continuously seek to develop novel, enabling approaches and analytical tools. We develop: 1) signal analysis tools to resolve biophysical events at the limits of what our experimental tools allow us to address, 2) novel, high-resolution microscopy and nanotechnology tools to resolve structural determinants underlying synaptic arrangements, and 3) computer-based modeling to identify how synaptic mechanisms determine information coding by individual neurons and small neural circuits.***1) Properties of receptor/channels at functional synapses***Modification of the strength of synaptic transmission involve several mechanisms (receptor numbers, properties, positioning, etc.); to understand the basics of synaptic plasticity, we develop approaches to resolve channel numbers at live synapses: Two approaches will be pursued:***1a) A modification of our noise analysis algorithm***1b) An adaptation of our SpIDA image analysis approach to structurally resolve receptor numbers at synapses.***2) High-resolution live cell imaging and micromanipulation***Activity-dependent spine reshaping are an important determinants of synaptic funciton. To follow the structural dynamics of submicron-scale cellular compartments, we develop high-resolution approaches:***2a) We exploit Atomic Force Microscopy as ultimate micropositionners and micromanipulators i) to study forces underlying ligand-receptor interaction and ii) to artificially engineer network topologies in neuronal cultures.***2b) We developed high-resolution SLAM Microscopy and will continue to explore resolution enhancement using other beam shaping and polarization approaches.***3- Cl- dynamics and information processing***We study the multiple impacts of altered Cl- dynamics on neural computation using different, complementary computational approaches:***3a) We will explore how Cl- dynamics affect information transfer combining electrodiffusion modeling and a low dimensional generic model.***3b) Cost of inhibition: we will investigate the advantage of maintaining hyperpolarizing inhibition given its energy cost.******   The research is relevant to the training of personnel with background in mathematics, computer sciences and physics (photonics and nanotechnology) who have an interest in applying their expertise in computational approaches to the understanding of neurobiological mechanisms.**

我的NSERC项目的目标是了解神经信号和计算的生物物理决定因素。也就是说，识别细胞到细胞的信号传导和信号整合的基底，这些基底是神经回路中信息处理的基础。在这种情况下，我的研究集中在1）突触的结构和功能组织如何影响细胞之间的信息传递和2）突触机制如何与神经细胞的内在属性相互作用，以塑造信息编码。为了实现这一目标，我不断寻求开发新的，使能的方法和分析工具。 我们开发：1）信号分析工具，以解决我们的实验工具允许我们解决的极限生物物理事件，2）新颖的，高分辨率的显微镜和纳米技术工具，以解决突触排列的结构决定因素，以及3）基于计算机的建模，以确定突触机制如何确定单个神经元和小神经回路的信息编码。1)功能性突触处受体/通道的性质 * 突触传递强度的改变涉及几种机制（受体数量、性质、定位等）;为了了解突触可塑性的基础知识，我们开发了解析活突触处通道数量的方法：将采用两种方法：*1a）对我们的噪声分析算法的修改 *1b）对我们的SpIDA图像分析方法的调整，以在结构上解析突触处的受体数量。* 2)高分辨率活细胞成像和显微操作 * 活动依赖性脊柱重塑是突触功能的重要决定因素。为了跟踪亚微米尺度细胞区室的结构动力学，我们开发了高分辨率方法：*2a）我们利用原子力显微镜作为最终的显微定位器和显微操纵器i）研究配体-受体相互作用的基础力量，ii）人工设计神经元培养物中的网络拓扑结构。2b）我们开发了高分辨率SLAM显微镜，并将继续探索使用其他光束整形和偏振方法来提高分辨率。3-Cl-动力学和信息处理 * 我们使用不同的互补计算方法研究改变的Cl-动力学对神经计算的多重影响：*3a）我们将结合电扩散模型和低维通用模型探索Cl-动力学如何影响信息传递。3b）抑制的成本：我们将研究在考虑到能量成本的情况下维持超极化抑制的优势。 该研究与培训具有数学、计算机科学和物理学（光子学和纳米技术）背景的人员有关，他们有兴趣将其在计算方法方面的专业知识应用于理解神经生物学机制。**