Department of Mathematics, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
Department of Biological Science and Program in Neuroscience, Florida State University, Tallahassee, FL, 32306, USA.
J Comput Neurosci. 2022 May;50(2):145-159. doi: 10.1007/s10827-021-00799-0. Epub 2021 Oct 19.
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.
研究单个神经元尖峰属性的标准方法是在监测电压响应的同时施加电流阶跃。虽然这很有启发性,但施加的电流跳跃是人为的。更具生理意义的输入是施加的电流逐渐上升,反映化学感觉输入。毫不奇怪,神经元对这两种方案的反应不同,因为离子通道的激活和失活受到不同的影响。数学模型有助于理解电流斜坡的影响及其斜率的变化。然而,电流斜坡的分析技术还不够发达。在本文中,我们展示了如何在单个神经元模型中分析电流斜坡。主要问题是存在门控变量,它们在缓慢的时间尺度上激活,因此在整个斜坡过程中远远偏离平衡。适当的快慢分析技术的使用允许人们充分理解不同斜率的斜坡对神经元的响应。这项研究的动机来自嗅球多巴胺神经元的数据,其中快速斜坡(几十毫秒)和慢速斜坡(几十秒)方案都用于理解细胞的尖峰分布。慢斜坡以施加的电流为分岔参数生成实验分岔图,从而建立渐近的尖峰活动模式。更快的斜坡引起纯粹的瞬态行为,这与大多数生理输入有关,这些输入持续时间很短。这两种方案共同提供了对神经元尖峰分布的更广泛理解,以及缓慢激活离子通道可能发挥的作用。