Understanding Multimodal Interactions in Neuronal Networks

了解神经网络中的多模式交互

• 批准号: 1029388
• 负责人: Michal Zochowski
• 金额: $ 34万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029388&HistoricalAwards=false
• 关键词: Understanding; Multimodal; Interactions; Neuronal; Networks

The goal of this work is to elucidate evolving multimodal interactions between structural, dynamical, and functional network properties based on the interdependencies observed between neuronal and astrocytic networks acting on diverse spatial and temporal scales. Specifically we will investigate how dynamical and structural network characteristics interact on these different time scales to form evolving, functional neural ensembles. To achieve this goal we plan to combine computational modeling with experimental approaches that include calcium optical imaging, multi-electrode recordings, and structural labeling studies in primary neuronal cultures. This will allow for monitoring of multi-scale, simultaneous dynamical and structural changes in networks under different conditions. In particular we want to address the following questions: What properties of spatio-temporal patterning are mediated through fast and/or slow network interactions? How does network connectivity influence multimodal network activity? Whether and how do local network changes modify local patterns of fast and slow dynamics? Finally, we want to understand how the functions of these dynamical modes evolve during network development.Networks of interacting elements are ubiquitous in nature and man-made systems. The interactions in these networks happen on different spatial and temporal scales. Neural networks are a prime example of such a complex system. While interconnected neurons establish fast modes of communication, the slower astrocytes can modulate overall activity in the network that in turn will affect its connectivity structure and function. It becomes then pertinent to understand the interactions between these two modes of transmission and what their differential roles in network function are. This interdisciplinary project connects neurobiology with dynamical systems approaches to tackle these problems and can potentially have important impact on diverse fields. Identifying dynamical mechanisms underlying these interactions will allow for a better understanding of the network processes that underlie both cognitive tasks and brain pathologies: for example, on one hand structural network reconfiguration is crucial for memory formation but at the same time may drive formation of epileptic seizures. At the same time, the use of these dynamical modes for controlled network development may also lead to formulation of new strategies for network repair after injury. Finally, the insight obtained from this work could be generalized and applied to man-made networks that employ multi-scale temporal dynamics and could undergo functional reorganization online.This project will also provide opportunity for graduate and undergraduate students to experience interdisciplinary research connecting biology, physics and dynamical systems theory.

这项工作的目标是阐明不断发展的多模态之间的相互作用的结构，动力学和功能网络特性的基础上观察到的相互依赖性神经元和星形胶质细胞网络作用在不同的空间和时间尺度。具体来说，我们将研究动态和结构网络特征如何在这些不同的时间尺度上相互作用，形成不断发展的功能神经合奏。为了实现这一目标，我们计划结合联合收割机计算建模与实验方法，包括钙光学成像，多电极记录，和结构标记的研究，在原代神经元培养。这将允许监测不同条件下网络的多尺度、同时动态和结构变化。特别是，我们要解决以下问题：时空模式的哪些属性是通过快速和/或缓慢的网络相互作用介导的？网络连通性如何影响多模态网络活动？局部网络的变化是否以及如何改变局部的快速和缓慢动态模式？最后，我们希望了解这些动力学模式的功能在网络发展过程中是如何演变的。这些网络中的相互作用发生在不同的时空尺度上。神经网络就是这种复杂系统的一个典型例子。虽然相互连接的神经元建立了快速的通信模式，但较慢的星形胶质细胞可以调节网络中的整体活动，从而影响其连接结构和功能。因此，理解这两种传播模式之间的相互作用以及它们在网络功能中的不同作用就变得至关重要了。这个跨学科项目将神经生物学与动力系统方法联系起来，以解决这些问题，并可能对不同领域产生重要影响。识别这些相互作用背后的动力学机制将有助于更好地理解认知任务和大脑病理的网络过程：例如，一方面，结构网络重构对记忆形成至关重要，但同时可能会驱动癫痫发作的形成。与此同时，使用这些动力学模式控制网络的发展也可能导致制定新的策略，损伤后的网络修复。最后，从这项工作中获得的见解可以推广和应用到人造网络，采用多尺度时间动力学，并可以进行功能重组在线。这个项目也将提供机会，研究生和本科生体验跨学科研究连接生物学，物理学和动力系统理论。