Data-driven, biologically constrained biophysical computational model of the hippocampal network at full scale

数据驱动、生物约束的全尺寸海马网络生物物理计算模型

• 批准号: 1811597
• 负责人: Ivan Soltesz
• 金额: $ 0.25万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811597&HistoricalAwards=false
• 关键词: Data; driven; biologically; constrained; biophysical

This project aims to fundamentally improve our understanding of the brain. In particular, this project will construct a detailed, biologically realistic, computational model of the brain hippocampus. The hippocampus is a major component of the brain that plays an important role in memory and other major human functions. This detailed computational model of the hippocampus will allow researchers to understand the origins of specific behavioral level features of the brain, as well as treatment effectiveness for conditions such as epilepsy. In addition, the project aims to investigate brain changes under different radiation exposure regimes. Furthermore, the software infrastructure developed in the project, as well as simulation results, will be publicly released to the wider neuroscience community.The overarching goal of the research projects is the construction of realistic and biophysically detailed computational models of the three major neuronal circuit layers in the hippocampus: the dentate gyrus (DG), CA3, and CA1. These three regions of the hippocampus are interconnected, and therefore the computational aspects of this research require building a detailed data-driven, full-scale computational model of the entire hippocampal formation and its inputs from the septum and the entorhinal cortex. Information processing in the brain is organized and facilitated by the complex interactions of intrinsic biophysical properties of distinct neuronal types, neuronal morphology, and network connection topology. These properties give rise to specific types of network oscillations and other dynamic processes that govern neural information encoding and exchange. The hypotheses in this proposal are designed to create a detailed picture at unprecedented scale of how the intrinsic properties of hippocampal principal neurons and interneurons define the network activity under normal conditions, and how pathological changes in those properties under epileptic conditions disrupt hippocampal function. The project has made public releases of the CA1 model code and simulation management tool (SimTracker), and have also published the CA1 simulation datasets that accompany the publication describing the main results of the CA1 work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个项目旨在从根本上提高我们对大脑的理解。特别是，这个项目将构建一个详细的、生物上真实的大脑海马体计算模型。海马体是大脑的主要组成部分，在记忆和其他主要人类功能方面发挥着重要作用。这个海马体的详细计算模型将使研究人员能够了解大脑特定行为水平特征的起源，以及对癫痫等疾病的治疗效果。此外，该项目旨在研究不同辐射暴露制度下的大脑变化。此外，项目中开发的软件基础设施以及模拟结果将向更广泛的神经科学界公开发布。研究项目的总体目标是构建海马区三个主要神经元电路层的真实和生物物理详细的计算模型：齿状回(DG)、CA3和CA1。海马体的这三个区域是相互联系的，因此本研究的计算方面需要建立一个详细的数据驱动的、完整的计算模型，以了解整个海马体结构及其来自隔膜和内嗅觉皮质的输入。大脑中的信息处理是由不同神经元类型、神经元形态和网络连接拓扑的内在生物物理属性的复杂相互作用来组织和促进的。这些特性导致了特定类型的网络振荡和管理神经信息编码和交换的其他动态过程。这项提议中的假设旨在以前所未有的规模创建一幅详细的图景，展示正常情况下海马主神经元和中间神经元的内在属性如何定义网络活动，以及在癫痫条件下这些属性的病理变化如何破坏海马体功能。该项目已公开发布了CA1模型代码和模拟管理工具(SimTracker)，并随出版物发布了描述CA1工作主要结果的CA1模拟数据集。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Offline memory replay in recurrent neuronal networks emerges from constraints on online dynamics

循环神经网络中的离线记忆重放源于在线动态的限制

• DOI: 10.1113/jp283216
• 发表时间: 2023
• 期刊: The Journal of Physiology
• 作者: Milstein, Aaron D.;Tran, Sarah;Ng, Grace;Soltesz, Ivan
• 通讯作者: Soltesz, Ivan