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Coding Capacity of Purkinje Cells With Different Schemes of Morphological Reduction

机译:不同形态还原方案的浦肯野细胞编码能力

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摘要

The brain as a neuronal system has very complex structures with a large diversity of neuronal types. The most basic complexity is seen from the structure of neuronal morphology, which usually has a complex tree-like structure with dendritic spines distributed in branches. To simulate a large-scale network with spiking neurons, the simple point neuron, such as the integrate-and-fire neuron, is often used. However, recent experimental evidence suggests that the computational ability of a single neuron is largely enhanced by its morphological structure, in particular, by various types of dendritic dynamics. As the morphology reduction of detailed biophysical models is a classic question in systems neuroscience, much effort has been taken to simulate a neuron with a few compartments to include the interaction between the soma and dendritic spines. Yet, novel reduction methods are still needed to deal with the complex dendritic tree. Here, using 10 individual Purkinje cells of the cerebellum from three species of guinea-pig, mouse and rat, we consider four types of reduction methods and study their effects on the coding capacity of Purkinje cells in terms of firing rate, timing coding, spiking pattern, and modulated firing under different stimulation protocols. We found that there is a variation of reduction performance depending on individual cells and species, however, all reduction methods can preserve, to some degree, firing activity of the full model of Purkinje cell. Therefore, when stimulating large-scale network of neurons, one has to choose a proper type of reduced neuronal model depending on the questions addressed. Among these reduction schemes, Branch method, that preserves the geometrical volume of neurons, can achieve the best balance among different performance measures of accuracy, simplification, and computational efficiency, and reproduce various phenomena shown in the full morphology model of Purkinje cells. Altogether, these results suggest that the Branch reduction scheme seems to provide a general guideline for reducing complex morphology into a few compartments without the loss of basic characteristics of the firing properties of neurons.
机译:作为神经元系统的大脑具有非常复杂的结构,具有多种神经元类型。从神经元形态的结构可以看出最基本的复杂性,该结构通常具有复杂的树状结构,树突棘分布在分支中。为了模拟带有尖峰神经元的大规模网络,通常使用简单点神经元,例如“整合并发射”神经元。然而,最近的实验证据表明,单个神经元的计算能力在很大程度上由其形态结构,特别是由各种类型的树突动力学所增强。由于详细的生物物理模型的形态学缩减是系统神经科学中的一个经典问题,因此已经进行了很多努力来模拟具有几个部分的神经元,以包括体细胞和树突棘之间的相互作用。然而,仍需要新颖的还原方法来处理复杂的树状树。在这里,我们使用来自豚鼠,小鼠和大鼠三种动物的10个小脑的Purkinje细胞,我们考虑了四种还原方法,并研究了它们在发射速率,定时编码,加标方面对Purkinje细胞编码能力的影响。模式,以及在不同刺激方案下的调制射击。我们发现还原性能会因单个细胞和物种而异,但是,所有还原方法都可以在一定程度上保留整个Purkinje细胞模型的放电活性。因此,当刺激大规模的神经元网络时,必须根据所解决的问题选择适当类型的简化神经元模型。在这些减少方案中,保留神经元几何体积的Branch方法可以在准确性,简化性和计算效率的不同性能指标之间达到最佳平衡,并再现Purkinje细胞完整形态模型中显示的各种现象。总而言之,这些结果表明,Branch还原方案似乎为将复杂的形态还原为几个部分而又不损失神经元激发特性的基本特征提供了一般指导。

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