Large neuronal networks: from structure to function, and back

大型神经元网络：从结构到功能，然后再返回

• 批准号: RGPIN-2019-06887
• 负责人: Desrosiers, Patrick
• 金额: $ 2.04万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716513
• 关键词: Large; neuronal; networks; structure; function

The long-term goal of our research program is to develop novel theoretical and computational methods that will help neuroscientists to decipher the intricate relationship between the structure and the function of nervous systems at different levels: cell, population, and system. To do so, we will target three specific projects and then apply the methods developed in each project to real neuronal systems. First, we propose to identify neurons and communities of neurons that are critical to specific functions of the whole network. Briefly, a neuron or a community is critical if its alteration produces dramatic changes in the global activity of the network and results in a loss of function. This first project is thus related to the impact of the structure on its function. Second, we aim to develop new methods that accurately detect causal interactions from activity data. For neuronal networks, causality is closely related to network structure since a neuron may influence another neuron only if the former is connected to the latter. We thus want to use the function of a neuronal network to predict its structure. Third, we will combine structure and function. Our goal is to develop new methods that predict the resilience of neuronal networks in which both the structure and the function constantly influence each other via synaptic plasticity. We will identify the critical characteristics of both structure (e.g., low reciprocity) and adaptation (e.g., fast synaptic potentiation) that make the whole network less vulnerable to perturbations (e.g., loss of connectivity, synaptic potentiation). To reach our goals, we will need to improve the current methods used for reducing the dimension of complex dynamical systems, which work well only for over-simplistic network structure (e.g., random connectivity) and neuronal dynamics with low biological foundation (e.g., leaky integrate-and-fire model). This is why several tools will be borrowed from statistical physics, nonlinear dynamics, and spectral analysis. Only neuron models with a clear biophysical interpretation, such as the HodgkinHuxley model, will be considered. At the end of the 5-year period, we will have a unique theoretical and computational framework that will allow to analyze the structure-function relation and to design targeted interventions aimed at restoring the good function of neuronal networks submitted to perturbations.

我们研究计划的长期目标是开发新的理论和计算方法，帮助神经科学家在不同水平上破译神经系统的结构和功能之间的复杂关系：细胞，群体和系统。为此，我们将针对三个特定的项目，然后将每个项目中开发的方法应用于真实的神经元系统。 首先，我们建议识别对整个网络的特定功能至关重要的神经元和神经元社区。简单地说，如果一个神经元或一个群体的改变导致网络的整体活动发生巨大变化，并导致功能丧失，那么它就至关重要。因此，这第一个项目与该结构对其功能的影响有关。其次，我们的目标是开发新的方法，从活动数据中准确地检测因果关系。对于神经元网络，因果关系与网络结构密切相关，因为一个神经元只有在与另一个神经元相连时才能影响另一个神经元。因此，我们想利用神经网络的功能来预测它的结构。三是联合收割机结构与功能相结合。 我们的目标是开发新的方法来预测神经元网络的弹性，其中结构和功能通过突触可塑性不断相互影响。 我们将确定两种结构的关键特征（例如，低互易性）和自适应（例如，快速突触增强）使得整个网络不易受到扰动（例如，连接丧失、突触增强）。 为了达到我们的目标，我们需要改进目前用于降低复杂动力系统维数的方法，这些方法只适用于过于简单的网络结构（例如，随机连接性）和具有低生物学基础的神经元动力学（例如，漏积分-激发模型）。这就是为什么要从统计物理学、非线性动力学和谱分析中借用一些工具的原因。只有具有明确生物物理解释的神经元模型，如HodgkinHuxley模型，才会被考虑。 在5年期结束时，我们将拥有一个独特的理论和计算框架，可以分析结构-功能关系，并设计有针对性的干预措施，旨在恢复受到干扰的神经元网络的良好功能。