Modeling microcircuits of realistic hippocampal neurons

描述（申请人提供）：该项目是实验神经生理学家和应用数学家之间的合作研究计划。该项目的主要目的是开发详细的计算模型的微电路组成的主要神经元和中间神经元在CA 1区海马。特别是，这些计算模型将：1）基于从膜片钳实验中获得的最新数据，这些实验涉及离子电导、钙和双光子成像以及细胞形态。2)使用专门设计的离子通道模型，以准确再现实验确定的行为。3)使用全细胞形态;特别地，已经从其获取电生理学数据的相同的单个细胞将被数字化用于计算研究。4)开发新的自适应计算方法，可用于加速计算模拟。 该项目的一个长期目标是开发海马体中兴奋性和抑制性细胞类型的足够准确的模型，以便可以对海马体中存在的小网络进行逼真的模拟。这些计算机模拟旨在成为一种工具，以指导新的膜片钳实验，进一步探索海马区的功能行为的发展。预计由此产生的研究将最终促进对海马网络如何存储和检索记忆的更好理解。 所提出的研究和训练目标的具体益处是：1）将开发形态学重建的神经元的新模型，从该模型中已经使用多个膜片钳电极和/或钙成像获得电生理测量。2)新的离子电导模型将被构建，以准确地再现实验结果。3)将开发新的计算方法以加速房室模型模拟。4)将开发新的模型来探索海马内兴奋性和抑制性神经元之间的网络相互作用。5)从对生物学应用感兴趣的应用数学家队伍中招募的研究生和博士后研究人员将接受计算神经科学的培训。

DESCRIPTION (provided by applicant): This project is a collaborative research program between experimental a cellular neurophysiologist and applied mathematicians. The central aim of the project is to develop detailed computational models of microcircuits composed principal neurons and interneurons in the CA1 region hippocampus. In particular, these computational models will: 1) Be based on the latest data available from patch-clamp experiments dealing with ionic conductances, calcium and two-photon imaging, and cell morphology. 2) Use ionic channel models specifically designed to accurately reproduce the experimentally determined behavior. 3) Use full-cell morphologies; in particular, the same individual cells from which electrophysiology data have been taken will be digitized for the computational studies. 4) Develop new adaptive computational methods that can be used to speed up the computational simulations. A long-term goal of the project is to develop sufficiently accurate models of excitatory and inhibitory cell types in the hippocampus so that realistic simulations of small networks present in the hippocampus can be performed. These computational simulations are intended to be a tool to guide the development of new patch-clamp experiments that further explore the functional behavior of the hippocampal region. It is anticipated that the resulting studies will ultimately foster an improved understanding of how the hippocampal network stores and retrieves memories. Specific benefits of the proposed research and training objectives are: 1) New models will be developed of morphologically reconstructed neurons from which electrophysiological measurements have been obtained using multiple patch-clamp electrodes and/or calcium imaging. 2) New ionic conductance models will be constructed to accurately reproduce the experimental results. 3) New computational methods will be developed to speed up the compartmental model simulations. 4) New models will be developed to explore network interactions between excitatory and inhibitory neurons within the hippocampus. 5) Graduate students and postdoctoral researchers, recruited from the ranks of applied mathematicians interested in biological applications, will be trained in computational neuroscience.