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Simple biologically-constrained CA1 pyramidal cell models using an intact whole hippocampus context

机译:使用完整的完整海马背景的简单受生物限制的CA1锥体细胞模型

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摘要

The hippocampus is a heavily studied brain structure due to its involvement in learning and memory. Detailed models of excitatory, pyramidal cells in hippocampus have been developed using a range of experimental data. These models have been used to help us understand, for example, the effects of synaptic integration and voltage gated channel densities and distributions on cellular responses. However, these cellular outputs need to be considered from the perspective of the networks in which they are embedded. Using modeling approaches, if cellular representations are too detailed, it quickly becomes computationally unwieldy to explore large network simulations. Thus, simple models are preferable, but at the same time they need to have a clear, experimental basis so as to allow physiologically based understandings to emerge. In this article, we describe the development of simple models of CA1 pyramidal cells, as derived in a well-defined experimental context of an intact, whole hippocampus preparation expressing population oscillations. These models are based on the intrinsic properties and frequency-current profiles of CA1 pyramidal cells, and can be used to build, fully examine, and analyze large networks.
机译:由于海马参与学习和记忆,因此海马已被广泛研究。使用一系列实验数据已经开发出海马兴奋性锥体细胞的详细模型。这些模型已用于帮助我们理解例如突触整合以及电压门控通道密度和分布对细胞反应的影响。然而,这些蜂窝输出需要从它们所嵌入的网络的角度来考虑。使用建模方法,如果蜂窝表示过于详细,则探索大型网络仿真的计算将很快变得不方便。因此,简单的模型是可取的,但同时它们需要有明确的实验基础,以便能够出现基于生理的理解。在本文中,我们描述了CA1锥体细胞的简单模型的发展,该模型是在完整的,表达种群振荡的完整海马体的明确实验环境中得出的。这些模型基于CA1锥体细胞的固有特性和频率-电流曲线,可用于构建,全面检查和分析大型网络。

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