Full-scale biophysical modeling of hippocampal networks during spatial navigation and memory replay

空间导航和记忆回放过程中海马网络的全面生物物理建模

• 批准号: 10202755
• 负责人: IVAN SOLTESZ
• 金额: $ 42.24万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-25 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10202755
• 关键词: Amygdaloid structure; Behavioral; Biophysics; Cells; Code; Communities; Companions; Complex; Computer Models; Data; Elements; Exhibits; Experimental Models; Generations; Goals; Hippocampus (Brain); Interneurons; Investigation; Knowledge; Location; Mediating; Memory; Modeling; Neurons; Neurosciences; Outcome; Output; Pattern; Physiological; Population; Prefrontal Cortex; Property; Recurrence; Series; Signal Transduction; Synapses; Testing; Work; biophysical model; cell type; dentate gyrus; experimental study; improved; large scale simulation; network models; neuronal circuitry; open source; response; segregation; simulation; spatial memory; way finding

Throughout our investigation of the mechanisms underlying hippocampal memory replay, we will employ a strategy of continuous interaction between experimental and modeling approaches. First, we consider conceptual models, and formulate hypotheses relevant to the initiation, generation and termination of SPW-Rs. Then, as we gather critical new experimental data to test those hypotheses in Projects 1-4, we will use that data to constrain full-scale biophysically-detailed computational models of hippocampal networks, integrating a newly developed model of CA3 with existing models of dentate gyrus (DG) and CA1. These models will then be used to predict the complex responses of the network to simulated perturbations, help interpret experimental outcomes, inform hypotheses about underlying principles, and suggest new experiments to test those hypotheses. In addition to an improved conceptual understanding of memory replay, our final product will include full-scale network models of the hippocampus with unprecedented functionality, fully open-sourced and freely available to the broader neuroscience community. !

在我们对海马记忆重放机制的研究中，我们将 采用实验和建模方法之间持续互动的策略。第一、 我们考虑概念模型，并制定相关的启动，生成和 SPW-R的终止。然后，当我们收集关键的新实验数据来测试这些假设时， 项目1-4，我们将使用这些数据来约束全尺寸生物制药学详细的计算模型 海马网络，整合新开发的CA 3模型与现有的齿状回模型， 海马回（DG）和CA 1.然后，这些模型将用于预测网络的复杂响应 模拟扰动，帮助解释实验结果，告知有关潜在的假设 原则，并提出新的实验来测试这些假设。除了改进的 我们的最终产品将包括完整的网络模型， 海马具有前所未有的功能，完全开源，免费提供给 更广泛的神经科学社区。 !