US-French Collaboration: Mechanisms of emergent OscillaTIONs in the septo-hippocampal network-MOTION

哺乳动物的海马体是学习和记忆的重要大脑区域，也与几种主要的神经和精神疾病有关，包括癫痫、自闭症、精神分裂症和阿尔茨海默病。先前的研究表明，海马体中与学习和记忆相关的信息处理严重依赖于被称为隔膜的邻近大脑结构所激发的时钟般的、有节奏的电信号。然而，隔区和海马区紧密相连的神经元网络是如何产生振荡神经元活动的，目前还不清楚。目前的项目将在小鼠身上使用新颖的、独特的高通量实验方法，并结合创新的、生物现实的、基于大规模超级计算机的理论建模技术，以确定隔区-海马区电路中产生节律性电信号的关键机制。该项目代表着基于生物学的大脑现实计算机模型开发的一个潜在的重大里程碑，因为它将允许产生和测试与行为相关的振荡神经元活动的网络机制的假设，具有无与伦比的生物现实主义、精确度和速度。此外，实验知识库和大规模计算大脑模型将放入开放源码数据库(包括http://senselab.med.yale.edu/modeldb/，的网络模型的ModelDB和http://neuromorpho.org/)的NeuroMoro生物结构数据)，科学界可免费获取这些数据库，不受限制地使用。该项目涉及神经信息学方面的大量博士后培训。该奖项由NSF国际科学与工程主任办公室共同资助，一个配套项目正由法国国家研究机构(ANR)资助。

The hippocampus in mammals is a brain area important for learning and memory that is also implicated in several major neurological and psychiatric disorders including epilepsy, autism, schizophrenia and Alzheimer's disease. Prior research revealed that learning and memory-related information processing in the hippocampus critically depends on clock-like, rhythmic electrical signals elicited by an adjacent brain structure called the septum. However, how the oscillatory neuronal activity emerges from the closely interconnected neuronal networks of the septum and the hippocampus is not understood. The current project will employ novel, uniquely high-throughput experimental approaches in mice integrated with innovative, biologically realistic, large-scale supercomputer-based theoretical modeling techniques to identify key mechanisms underlying rhythmic electrical signal generation in septo-hippocampal circuits. The project represents a potentially major milestone in the development of biologically based realistic computer models of the brain, as it would allow the generation and testing of hypotheses concerning network mechanisms of behaviorally relevant oscillatory neuronal activity with unparalleled biological realism, precision and speed. In addition, the experimental knowledge base and the large-scale computational brain models will be placed in open-source databases (including ModelDB for network models at http://senselab.med.yale.edu/modeldb/, and Neuromorpho for biological structural data at http://neuromorpho.org/) that are freely accessible for the unrestricted use by the scientific community. This project involves extensive postdoctoral training in neuroinformatics. This award is co-funded by NSF's Office of the Director, International Science and Engineering, and a companion project is being funded by the French National Research Agency (ANR).