Large neuronal networks: from structure to function, and back

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结构和功能之间的复杂关系：细胞、种群和系统。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。简而言之，如果神经元或群落的改变导致网络的全球活动发生戏剧性变化，并导致功能丧失，那么它是至关重要的。因此，第一个项目涉及该结构对其功能的影响。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交互作用。对于神经元网络，因果关系与网络结构密切相关，因为只有当前者与后者相连时，一个神经元才可能影响另一个神经元。因此，我们希望利用神经网络的功能来预测其结构。三是把结构和功能结合起来。我们的目标是开发新的方法来预测神经网络的弹性，在这个网络中，结构和功能都通过突触可塑性不断地相互影响。我们将确定结构(例如，低互易性)和适应(例如，快速突触增强)的关键特征，使整个网络不太容易受到扰动(例如，连接丢失、突触增强)的影响。 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度的方法，这些方法仅适用于过于简单化的网络结构(例如，随机连通性)和生物基础较低的神经元动力学(例如，泄漏积分和火灾模型)。这就是为什么将借用统计物理、非线性动力学和光谱分析的几个工具。只有具有明确生物物理解释的神经元模型，如HodgkinHuxley模型，将被考虑。在5年期结束时，我们将有一个独特的理论和计算框架，使我们能够分析结构-功能关系，并设计有针对性的干预措施，旨在恢复神经网络的良好功能。

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.