Tools to decipher neuronal signalling and computation

破译神经信号和计算的工具

• 批准号: RGPIN-2020-06361
• 负责人: DeKoninck, Yves
• 金额: $ 3.64万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763634
• 关键词: Tools; decipher; neuronal; signalling; computation

The overall objective of my NSE program is to advance our ability to understand the structural and functional substrates of how information is encoded, in brain circuits, from synapses assemblies to small and long-range networks. These circuits have evolved to solve tasks far beyond the reach of today's most powerful computers while using only the energy budget of a light bulb. Understanding how this is achieved is particularly challenging given the complexity of brain structures and the degree of multidirectional, multiscale interactions within neuronal circuits. Being able to probe the brain across a wide range of scales, from its most intricate components to large neuronal ensembles, in action, is necessary to provide an appropriate appreciation of the computational capabilities of the brain. To undertake this challenge, my NSERC program has focused on developing novel approaches and analytical tools to probe and manipulate live nerve cells and brain circuits across scales. Toward this aim, our main focus is to develop: 1) signal analysis tools to resolve biophysical events (e.g., channel gating and molecular interactions) at the limits of what our experimental tools allow us to address; 2) high-resolution optical microscopy to resolve structural determinants underlying synaptic arrangements in live cell conditions; 3) non-imaging sensing and actuating micro-device to monitor and control neuronal activity in the intact brain, from single cells to large ensembles in freely moving animals to understand signal processing in relevant behavioural context; 4) computer-based modelling to identify how local structural elements determine information coding, from single neurons to neural circuits. We achieve this through collaborative efforts with physicists, mathematicians and engineers. Our research builds on complementary expertise in neuroscience, biophysics, material science, micro-structuring, imaging, signal analysis, circuit integration and miniaturisation to maximise the amount of information collected in the least invasive manners to bridge molecular probing with behavioural assessment. In turn, we integrate knowledge gained experimentally into models to guide further experimental probing to unravel how each component of the system shapes the computational properties of neural networks. Thus, my Discovery Grant has been centered on training research personnel with photonics, mathematics and engineering background aiming to apply their skills to resolve neurobiological questions. While the primary focus is in developing physical and computational tools, the trainees are exposed to experimental components to validate their novel optical, nanotechnological and analytical approaches and to test predictions made by modelling approaches. It also serves to provide trainees with a multidisciplinary know-how that will be critical for their academic or industrial career.

我的NSE项目的总体目标是提高我们理解信息如何在大脑电路中编码的结构和功能基础的能力，从突触组装到小型和远程网络。这些电路已经发展到解决远远超出当今最强大的计算机能力范围的任务，而只使用一个灯泡的能量预算。考虑到大脑结构的复杂性和神经元回路中多方向、多尺度的相互作用的程度，理解这一点是特别具有挑战性的。能够在广泛的范围内探测大脑，从它最复杂的组成部分到大型神经元集合，在行动中，这是必要的，以提供对大脑计算能力的适当评价。为了应对这一挑战，我的NSERC项目专注于开发新的方法和分析工具，以探测和操纵活的神经细胞和大脑回路。为此，我们的主要重点是开发：1)信号分析工具，以解决我们的实验工具所能解决的极限生物物理事件(例如，通道门控和分子相互作用)；2)高分辨率光学显微镜，以解析活细胞条件下潜在的突触安排的结构决定因素；3)非成像传感和致动微型设备，以监测和控制完整大脑中的神经元活动，从单个细胞到自由移动动物的大型群体，以了解相关行为背景下的信号处理；4)基于计算机的建模，以确定局部结构元素如何决定信息编码，从单个神经元到神经电路。我们通过与物理学家、数学家和工程师的合作努力实现了这一点。我们的研究建立在神经科学、生物物理学、材料科学、微结构、成像、信号分析、电路集成和微型化方面的互补专业知识的基础上，以最小的侵入性方式最大限度地收集信息量，从而将分子探测与行为评估联系起来。反过来，我们将从实验中获得的知识整合到模型中，以指导进一步的实验探索，以揭示系统的每个组件如何塑造神经网络的计算特性。因此，我的发现基金一直专注于培训具有光子学、数学和工程背景的研究人员，旨在应用他们的技能来解决神经生物学问题。虽然主要的重点是开发物理和计算工具，但受训者将接触到实验部件，以验证他们的新光学、纳米技术和分析方法，并测试通过建模方法作出的预测。它还为学员提供了对他们的学术或工业生涯至关重要的多学科知识。