基于电压的事件计时依赖性可塑性规则解释了 CA1 锥体神经元树突尖峰阈下和阈上的 LTP。

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2024-05-01 Epub Date: 2024-03-12 DOI:10.1007/s10827-024-00868-0
Matus Tomko, Lubica Benuskova, Peter Jedlicka
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引用次数: 0

摘要

长期电位(LTP)是一种参与学习和记忆的突触机制。实验表明,CA1 锥体细胞远端顶端树突的 LTP 需要树突钠尖峰(Na-dSpikes)。另一方面,突触输入模式既可以是阈下也可以是阈上Na-dSpikes,从而诱导周边树突的LTP。目前还不清楚这些结果是否可以用一种统一的可塑性机制来解释。在这里,我们在 CA1 锥体细胞的生物物理和形态学逼真的分区模型中表明,这些形式的 LTP 可由一个简单的可塑性规则完全解释。我们称之为基于电压的事件计时可塑性(ETDP)规则。突触前事件是突触前棘波或谷氨酸的释放。突触后事件是局部去极化超过一定的可塑性阈值。我们的模型重现了实验观察到的各种方案中的 LTP,包括河豚毒素(TTX)对树突尖峰的局部药理抑制。总之,我们对基于电压的 ETDP 进行了验证,表明这一简单的可塑性规则甚至可以用来模拟神经元树突中长期突触可塑性的复杂时空模式。
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A voltage-based Event-Timing-Dependent Plasticity rule accounts for LTP subthreshold and suprathreshold for dendritic spikes in CA1 pyramidal neurons.

Long-term potentiation (LTP) is a synaptic mechanism involved in learning and memory. Experiments have shown that dendritic sodium spikes (Na-dSpikes) are required for LTP in the distal apical dendrites of CA1 pyramidal cells. On the other hand, LTP in perisomatic dendrites can be induced by synaptic input patterns that can be both subthreshold and suprathreshold for Na-dSpikes. It is unclear whether these results can be explained by one unifying plasticity mechanism. Here, we show in biophysically and morphologically realistic compartmental models of the CA1 pyramidal cell that these forms of LTP can be fully accounted for by a simple plasticity rule. We call it the voltage-based Event-Timing-Dependent Plasticity (ETDP) rule. The presynaptic event is the presynaptic spike or release of glutamate. The postsynaptic event is the local depolarization that exceeds a certain plasticity threshold. Our model reproduced the experimentally observed LTP in a variety of protocols, including local pharmacological inhibition of dendritic spikes by tetrodotoxin (TTX). In summary, we have provided a validation of the voltage-based ETDP, suggesting that this simple plasticity rule can be used to model even complex spatiotemporal patterns of long-term synaptic plasticity in neuronal dendrites.

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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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