COMPUTATION AND SYNAPTIC MODIFICATION IN THE DENTATE

本研究的长期目标是了解 海马体。海马体被认为在 学习和记忆功能，并参与许多主要的 神经系统疾病如果我们想了解海马体的功能， 必须了解计算和突触修饰是如何发生的， 海马体的细胞。 计算机建模是研究海马的一种新的强有力的工具 功能单个神经元的计算机模型可以用来测试 难以通过实验解决的假设，可以提供具体的 对观察到的现象的机制的预测。本研究 海马齿状颗粒细胞的高度详细的模型， 开发这些模型将包括完整的形态沿着 突触和非突触电导的数学描述， 控制细胞内钙浓度的过程。车型将 尽可能现实地与相当大的机构保持一致， 实验数据可用。 这些高度详细的模型将用于：1）评估 齿状回功能的形态结构和电紧张结构 颗粒细胞2)确定计算可能性的范围， 这些细胞由于它们的离子通道类型、分布和 密度计算的可能性是如何变化的， 参数值的变化可能会揭示某些 病理条件。3)解释钙浓度如何变化， 这种细胞可能与长时程增强的诱导有关 (LTP)长期抑郁症（LTD）如果我们理解LTP和LTD 如果发生这种情况，我们将在理解学习如何 记忆发生了。4)推导出一种包括突触修饰的方法 在与LTP的实验观察一致的模型中， 公司 这项研究将提供一个定量的理解计算和 齿状颗粒细胞突触修饰。这将告诉我们 高度详细的模型可以简化为对齿状结构的简单描述， 颗粒细胞保留了这些细胞的基本计算特征， 细胞这些简单的描述可以用于复杂的网络 研究海马功能的模型。

The long-term goal of this research is to understand the function of the hippocampus. The hippocampus is thought to play an important role in learning and memory functions and is involved in a number of major neurological disorders. If we are to understand hippocampal function, we must understand how computation and synaptic modification take place in the cells of the hippocampus. Computer modeling is a new and powerful tool for studying hippocampal function. Computer models of individual neurons can he used to test hypotheses difficult to address experimentally and can provide specific predictions about the mechanisms of observed phenomena. In this research highly detailed models of hippocampal dentate granule cells will be developed. These models will include complete morphology along with mathematical descriptions of synaptic and non-synaptic conductances and of processes that control calcium concentration in the cell. Models will be made as realistic as possible to be consistent with the considerable body of experimental data available. These highly detailed models will be used to: 1) Assess the importance of the morphological and electrotonic structure for the function of dentate granule cells. 2) Determine the range of computational possibilities of these cells given their complement of ion channel types, distributions and densities. How the computational possibilities change when certain parameter values change might reveal phenomena seen with certain pathological conditions. 3) Explain how calcium concentration changes in the cell might be correlated with the induction of long-term potentiation (LTP) or long-term depression (LTD). If we understand how LTP and LTD occur, we will have made great strides towards understanding how learning and memory take place. 4) Deduce a means to include synaptic modification in models that is consistent with experimental observations of LTP and LTD. This research will provide a quantitative understanding of computation and synaptic modification in dentate granule cells. This will tell us how highly detailed models might be reduced to simple descriptions of dentate granule cells that retain the essential computational features of these cells. These simple descriptions can then be used in complex network models to investigate hippocampal function.