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树突就是树突?超越“天线”模型的树突信号整合。

A dendrite is a dendrite is a dendrite? Dendritic signal integration beyond the "antenna" model.

作者信息

Stingl Moritz, Draguhn Andreas, Both Martin

机构信息

Institute of Physiology and Pathophysiology, Medical Faculty, Heidelberg University, 69120, Heidelberg, Germany.

Department of Physiology, University of California, San Francisco, San Francisco, CA, USA.

出版信息

Pflugers Arch. 2025 Jan;477(1):9-16. doi: 10.1007/s00424-024-03004-0. Epub 2024 Aug 9.

Abstract

Neurons in central nervous systems receive multiple synaptic inputs and transform them into a largely standardized output to their target cells-the action potential. A simplified model posits that synaptic signals are integrated by linear summation and passive propagation towards the axon initial segment, where the threshold for spike generation is either crossed or not. However, multiple lines of research during past decades have shown that signal integration in individual neurons is much more complex, with important functional consequences at the cellular, network, and behavioral-cognitive level. The interplay between concomitant excitatory and inhibitory postsynaptic potentials depends strongly on the relative timing and localization of the respective synapses. In addition, dendrites contain multiple voltage-dependent conductances, which allow scaling of postsynaptic potentials, non-linear input processing, and compartmentalization of signals. Together, these features enable a rich variety of single-neuron computations, including non-linear operations and synaptic plasticity. Hence, we have to revise over-simplified messages from textbooks and use simplified computational models like integrate-and-fire neurons with some caution. This concept article summarizes the most important mechanisms of dendritic integration and highlights some recent developments in the field.

摘要

中枢神经系统中的神经元接收多个突触输入,并将它们转化为向其靶细胞输出的大致标准化信号——动作电位。一个简化模型假定,突触信号通过线性总和以及向轴突起始段的被动传播进行整合,在轴突起始段,要么达到产生峰电位的阈值,要么未达到。然而,过去几十年的多项研究表明,单个神经元中的信号整合要复杂得多,在细胞、网络和行为认知层面都有重要的功能影响。伴随的兴奋性和抑制性突触后电位之间的相互作用在很大程度上取决于各个突触的相对时间和定位。此外,树突含有多种电压依赖性电导,这允许对突触后电位进行缩放、非线性输入处理以及信号的分隔。这些特征共同促成了丰富多样的单神经元计算,包括非线性运算和突触可塑性。因此,我们必须修正教科书上过于简化的内容,并谨慎使用诸如积分发放神经元这样的简化计算模型。这篇概念文章总结了树突整合的最重要机制,并突出了该领域的一些最新进展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bb0/11711151/a468454f6f1e/424_2024_3004_Fig1_HTML.jpg

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