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Intrinsic morphological diversity of thick-tufted layer 5 pyramidal neurons ensures robust and invariant properties of in silico synaptic connections

机译:厚簇状第5层锥体神经元的内在形态多样性可确保计算机突触连接的鲁棒和不变特性

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摘要

Non-technical summaryPyramidal neurons are output neurons of the neocortex. The thick-tufted layer 5 (TTL5) pyramidal neurons are one of the most extensively studied neocortical cell types and are characterized by an exquisite morphological structure. Despite their characteristic morphology, TTL5 neurons in the neocortical microcircuit display an intrinsic diversity that renders each neuron morphologically unique. In order to investigate the functional significance of this intrinsic morphological diversity, we reconstructed networks of TTL5 neurons through a detailed computer model and compared the properties of modelled synaptic connections against experimental data. We found that the average synaptic properties of modelled connections between TTL5 neurons closely matched experimental observations and remained unaltered by changes to several parameters at the local network level. These results show that the intrinsic morphological diversity of TTL5 neurons is a mechanism to ensure that the average synaptic properties are robust to changes at the local network level.
机译:非技术摘要金字塔形神经元是新皮层的输出神经元。丛集厚的第5层(TTL5)锥体神经元是研究最广泛的新皮层细胞类型之一,其特征是具有精美的形态结构。尽管它们具有独特的形态,新皮层微电路中的TTL5神经元仍具有内在的多样性,从而使每个神经元在形态上都独一无二。为了研究这种内在形态多样性的功能意义,我们通过详细的计算机模型重建了TTL5神经元的网络,并将建模的突触连接的特性与实验数据进行了比较。我们发现,TTL5神经元之间的建模连接的平均突触特性与实验观察值非常匹配,并且在局部网络级别上对多个参数的更改保持不变。这些结果表明,TTL5神经元的固有形态多样性是一种机制,可确保平均突触特性对本地网络级别的变化具有鲁棒性。

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