德国柏林洪堡大学Matthew Evan Larkum团队揭示人类2/3层皮质神经元中的树突动作电位和计算过程。该研究于2020年1月3日发表于国际一流学术期刊《科学》。

研究人员研究了离体人类大脑皮层的第2层和第3层（L2/3）锥体神经元的树突。在这些神经元中，研究人员发现了一类钙介导的树突动作电位（dCaAP），其波形和对神经元输出的影响以前没有被描述过。与典型的全有或全无动作电位相反，dCaAP可被分级。对于阈值水平的刺激，它们的振幅是最大的，但是对于较强的刺激，它们的振幅是衰减的。这些dCaAP使人类新皮质锥体神经元的树突能够对线性不可分离的输入进行分类，而这种计算过程通常被认为需要多层网络。

据悉，树突的主动电学特性影响神经元的输入和输出，并且是脑功能的基础。但是，我们对活性树突的了解几乎完全来自啮齿动物的研究。

附：英文原文

Title: Dendritic action potentials and computation in human layer 2/3 cortical neurons

Author: Albert Gidon, Timothy Adam Zolnik, Pawel Fidzinski, Felix Bolduan, Athanasia Papoutsi, Panayiota Poirazi, Martin Holtkamp, Imre Vida, Matthew Evan Larkum

Issue&Volume: 2020/01/03

Abstract: The active electrical properties of dendrites shape neuronal input and output and are fundamental to brain function. However, our knowledge of active dendrites has been almost entirely acquired from studies of rodents. In this work, we investigated the dendrites of layer 2 and 3 (L2/3) pyramidal neurons of the human cerebral cortex ex vivo. In these neurons, we discovered a class of calcium-mediated dendritic action potentials (dCaAPs) whose waveform and effects on neuronal output have not been previously described. In contrast to typical all-or-none action potentials, dCaAPs were graded; their amplitudes were maximal for threshold-level stimuli but dampened for stronger stimuli. These dCaAPs enabled the dendrites of individual human neocortical pyramidal neurons to classify linearly nonseparable inputs—a computation conventionally thought to require multilayered networks.

DOI: 10.1126/science.aax6239

Source: https://science.sciencemag.org/content/367/6473/83