棘密度对伏隔中棘神经元兴奋性影响的计算方法

M. Rane, R. Manchanda
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引用次数: 1

摘要

伏隔核(NAc)是腹侧纹状体的主要分支,在奖赏通路中起重要作用。GABAergic Medium Spiny Neurons (MSNs)是NAc的主要细胞类型。这些神经元通过位于其复杂树突上的众多棘接受兴奋性突触输入。脊柱密度和形态的改变可以影响msn的综合特性。我们开发了一种生物物理上真实的、刺状的MSN模型。我们发现,在现有的棘刺模型中包含的棘改变了细胞的被动和主动特性。对spiny模型进行了调整,使其属性与早期的aspiny模型相匹配。我们发现,共192个输入从中间和远端树突需要产生膜电位的特征双峰行为。利用该模型,我们研究了脊髓丢失对细胞兴奋性的影响。我们发现,在没有脊椎损失的情况下,当激活输入的数量减少15%时,细胞的尖峰频率减少到零,使细胞完全不可兴奋。然而,15%的脊柱丢失和15%的激活突触输入减少将峰值频率降低到1.1 Hz。我们的研究结果表明,当脊髓和突触输入一起消失时,细胞的兴奋性并没有完全消失,尽管这可能发生在仅突触输入消失时。相反,在这种情况下,可以通过稍微增强输入连接来增加兴奋性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effect of Spine Density on Excitability in Accumbal Medium Spiny Neurons-A Computational Approach
The nucleus accumbens (NAc), the major subdivision of the ventral striatum plays an important role in the reward pathway. GABAergic Medium Spiny Neurons (MSNs) are the principal cell type of NAc. These neurons receive excitatory synaptic inputs over the numerous spines which are present on their complex dendritic arbours. Alterations in spine density and morphology can affect the integrative properties of MSNs. We developed a biophysically realistic, spiny model of MSN. We found that inclusion of spines in an existing aspiny model changed passive as well as active properties of the cell. The spiny model was tuned to match its properties with that of the earlier aspiny model. We found that a total of 192 inputs from middle and distal dendrites were required to generate a characteristic bimodal behaviour of the membrane potential. Using this model, we investigated the effect of loss of spines on the excitability of the cell. We found that with no spine loss, when only the number of activated inputs was reduced by 15%, spike frequency of the cell reduced to zero, rendering the cell completely inexcitable. However, spine loss of 15% along with 15% reduction in activated synaptic inputs decreased the spike frequency to 1.1 Hz. Our results suggest that when spines are lost along with synaptic inputs, excitability of the cell is not abolished completely, although this might happen when only synaptic inputs are lost. Instead, in such a case the excitability can be increased by slightly enhancing the input connections.
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