快慢分析作为一种理解神经元对电流斜坡反应的技术。

IF 2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2022-05-01 Epub Date: 2021-10-19 DOI:10.1007/s10827-021-00799-0
Kelsey Gasior, Kirill Korshunov, Paul Q Trombley, Richard Bertram
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引用次数: 1

摘要

研究单个神经元尖峰特性的标准方案是在监测电压响应的同时应用电流步长。虽然这是有用的,但应用电流的跳跃是人为的。一个更生理的输入是施加的电流增加,反映化学感觉输入。不出所料,神经元对两种方案的反应不同,因为离子通道的激活和失活受到的影响不同。数学模型有助于理解当前坡道的影响及其坡度的变化。然而,分析电流坡道的技术还不发达。在本文中,我们演示了如何在单神经元模型中分析电流斜坡。主要问题是在缓慢时间尺度上激活的门控变量的存在,因此在整个斜坡上远离平衡。使用适当的快慢分析技术,可以充分了解不同坡度坡道的神经反应。这项研究的动机是来自嗅球多巴胺神经元的数据,其中使用快速斜坡(数十毫秒)和慢斜坡(数十秒)协议来了解细胞的尖峰分布。慢速坡道以施加的电流作为分岔参数生成实验分岔图,从而建立渐近尖峰活动模式。更快的斜坡引起纯粹的瞬态行为,这与大多数持续时间较短的生理输入有关。这两种方案共同提供了对神经元尖峰谱和缓慢激活离子通道的作用的更广泛的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fast-slow analysis as a technique for understanding the neuronal response to current ramps.

Fast-slow analysis as a technique for understanding the neuronal response to current ramps.

The standard protocol for studying the spiking properties of single neurons is the application of current steps while monitoring the voltage response. Although this is informative, the jump in applied current is artificial. A more physiological input is where the applied current is ramped up, reflecting chemosensory input. Unsurprisingly, neurons can respond differently to the two protocols, since ion channel activation and inactivation are affected differently. Understanding the effects of current ramps, and changes in their slopes, is facilitated by mathematical models. However, techniques for analyzing current ramps are under-developed. In this article, we demonstrate how current ramps can be analyzed in single neuron models. The primary issue is the presence of gating variables that activate on slow time scales and are therefore far from equilibrium throughout the ramp. The use of an appropriate fast-slow analysis technique allows one to fully understand the neural response to ramps of different slopes. This study is motivated by data from olfactory bulb dopamine neurons, where both fast ramp (tens of milliseconds) and slow ramp (tens of seconds) protocols are used to understand the spiking profiles of the cells. The slow ramps generate experimental bifurcation diagrams with the applied current as a bifurcation parameter, thereby establishing asymptotic spiking activity patterns. The faster ramps elicit purely transient behavior that is of relevance to most physiological inputs, which are short in duration. The two protocols together provide a broader understanding of the neuron's spiking profile and the role that slowly activating ion channels can play.

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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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