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

哺乳动物的海马体是一个对学习和记忆很重要的大脑区域，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由相邻的大脑结构（称为隔膜）引起的时钟样、有节奏的电信号。然而，振荡神经元活动如何从隔膜和海马的紧密互连的神经元网络中出现尚不清楚。目前的项目将采用新颖的，独特的高通量实验方法，结合创新的，生物现实的，大规模的基于超级计算机的理论建模技术，以确定隔海马电路中产生节律电信号的关键机制。该项目代表了开发基于生物学的逼真大脑计算机模型的潜在重要里程碑，因为它将允许生成和测试有关行为相关振荡神经元活动网络机制的假设，具有无与伦比的生物现实主义，精度和速度。此外，实验知识库和大规模计算大脑模型将被放置在开源数据库中（包括http：//senselab.med.yale.edu/modeldb/上的网络模型ModelDB和http：//neuromorpho.org/上的生物结构数据Neuromorpho），这些数据库可免费访问，供科学界不受限制地使用。该项目涉及广泛的神经信息学博士后培训。 该奖项由NSF国际科学与工程主任办公室共同资助，法国国家研究机构（ANR）资助了一个配套项目。

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).