模拟单一场和单个神经元对海马体局部场电位的贡献

Maria Teleńczuk, B. Teleńczuk, A. Destexhe
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引用次数: 15

摘要

突触电流是脑组织局部场电位(LFP)的主要组成部分,但兴奋性突触和抑制性突触各自的作用尚不清楚。在这里,我们通过使用海马锥体神经元的计算模型来提供这种贡献的估计,受体外记录的限制。我们关注海马CA3区单个神经元产生的单一LFP (uLFP)。我们首先重现了海马体篮细胞的实验结果,特别是抑制性uLFP如何在海马体层内分布。接下来,我们使用形态重建的CA3锥体细胞计算锥体神经元产生的uLFP。模型显示,兴奋性uLFP振幅较小,小于抑制性uLFP。事实上,当两者一起模拟时,抑制性ulfp掩盖了兴奋性ulfp,这可能会造成抑制场是由锥体细胞产生的错觉。这些结果解释了兴奋性和抑制性ulfp在体内和体外具有相同极性的现象。这些结果还表明,体细胞抑制电流是LFP的主要贡献者,这是解释该信号的重要信息。最后,我们模型的结果可能构成计算LFP的简单方法的基础,该方法可以应用于每种细胞类型的点神经元,从而提供一种简单的生物学基础方法来计算神经网络中的LFP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling unitary fields and the single‐neuron contribution to local field potentials in the hippocampus
Synaptic currents represent a major contribution to the local field potential (LFP) in brain tissue, but the respective contribution of excitatory and inhibitory synapses is not known. Here, we provide estimates of this contribution by using computational models of hippocampal pyramidal neurons, constrained by in vitro recordings. We focus on the unitary LFP (uLFP) generated by single neurons in the CA3 region of the hippocampus. We first reproduce experimental results for hippocampal basket cells, and in particular how inhibitory uLFP are distributed within hippocampal layers. Next, we calculate the uLFP generated by pyramidal neurons, using morphologically-reconstructed CA3 pyramidal cells. The model shows that the excitatory uLFP is of small amplitude, smaller than inhibitory uLFPs. Indeed, when the two are simulated together, inhibitory uLFPs mask excitatory uLFPs, which might create the illusion that the inhibitory field is generated by pyramidal cells. These results provide an explanation for the observation that excitatory and inhibitory uLFPs are of the same polarity, in vivo and in vitro. These results also show that somatic inhibitory currents are large contributors of the LFP, which is important information to interpret this signal. Finally, the results of our model might form the basis of a simple method to compute the LFP, which could be applied to point neurons for each cell type, thus providing a simple biologically-grounded method to calculate LFPs from neural networks.
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