Spiking resonances in models with the same slow resonant and fast amplifying currents but different subthreshold dynamic properties
Spiking resonances in models with the same slow resonant and fast amplifying currents but different subthreshold dynamic properties
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具有相同慢速谐振和快速放大电流但亚阈值动态特性不同的模型中的尖峰谐振
DOI：
10.1007/s10827-017-0661-9
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发表时间：
2017
影响因子：
1.2
通讯作者：
Rotstein, Horacio G.
中科院分区：
文献类型：
--
作者：
Rotstein, Horacio G.
神经元中尖峰共振的产生（对振荡输入的优选尖峰响应）需要在亚阈值电压水平下操作的固有离子电流和尖峰机制的相互作用。不同参数范围中的相同类型的离子电流的组合可引起电压方程中的不同类型的非线性（例如，抛物线和抛物线状），产生具有不同性质的阈下（膜电位）振荡模式。这些非线性在模型方程中并不明显，但可以通过在相平面图中绘制电压零倾线来发现。我们调查的尖峰谐振特性的电导为基础的模型，是生物等效的亚阈值水平（相同的离子电流），但动态不同（抛物线和抛物线状的电压零倾）。作为一个案例研究，我们考虑一个模型具有持久的钠和超极化激活（h-）电流，表现出阈下共振在θ频带。我们将尖峰共振的概念展开为诱发尖峰共振和输出尖峰共振。前者关注能够生成尖峰的输入频率，而后者关注输出尖峰频率，而不管生成这些尖峰的输入频率如何。细胞可以表现出一种或两种类型的共振。我们还测量尖峰相位，这是一个扩展的阈下相位（零相移响应振荡输入）的尖峰制度。两种类型的模型的亚阈值谐振特性被传达到尖峰制度足够低的输入幅度作为亚阈值谐振频带的电压响应上升到阈值以上。对于更高的输入振幅诱发尖峰共振不再存在于这些模型中，但输出尖峰共振主要存在于抛物线状模型中，这是由于周期跳跃机制（涉及混合模式振荡），而抛物线状模型显示出更好的1：1夹带。我们使用动力系统工具来解释的基本机制和共振类型之间的机械差异。我们的研究结果表明，有效的时间尺度，在阈下政权产生固有的阈下振荡，混合模式振荡和阈下共振不一定决定存在一个首选的尖峰响应在同一频带的振荡输入。本文中讨论的结果突出的阈值以上的振荡输入的神经元具有不同的时间尺度的共振和放大电流的响应的复杂性和参与离子电流的身份是不足以预测所产生的模式，但额外的动态信息，捕获的几何性质的相空间图，是必要的。
The generation of spiking resonances in neurons (preferred spiking responses to oscillatory inputs) requires the interplay of the intrinsic ionic currents that operate at the subthreshold voltage level and the spiking mechanisms. Combinations of the same types of ionic currents in different parameter regimes may give rise to different types of nonlinearities in the voltage equation (e.g., parabolic- and cubic-like), generating subthreshold (membrane potential) oscillations patterns with different properties. These nonlinearities are not apparent in the model equations, but can be uncovered by plotting the voltage nullclines in the phase-plane diagram. We investigate the spiking resonant properties of conductance-based models that are biophysically equivalent at the subthreshold level (same ionic currents), but dynamically different (parabolic- and cubic-like voltage nullclines). As a case study we consider a model having a persistent sodium and a hyperpolarization-activated (h-) currents, which exhibits subthreshold resonance in the theta frequency band. We unfold the concept of spiking resonance into evoked and output spiking resonance. The former focuses on the input frequencies that are able to generate spikes, while the latter focuses on the output spiking frequencies regardless of the input frequency that generated these spikes. A cell can exhibit one or both types of resonances. We also measure spiking phasonance, which is an extension of subthreshold phasonance (zero-phase-shift response to oscillatory inputs) to the spiking regime. The subthreshold resonant properties of both types of models are communicated to the spiking regime for low enough input amplitudes as the voltage response for the subthreshold resonant frequency band raises above threshold. For higher input amplitudes evoked spiking resonance is no longer present in these models, but output spiking resonance is present primarily in the parabolic-like model due to a cycle skipping mechanism (involving mixed-mode oscillations), while the cubic-like model shows a better 1:1 entrainment. We use dynamical systems tools to explain the underlying mechanisms and the mechanistic differences between the resonance types. Our results demonstrate that the effective time scales that operate at the subthreshold regime to generate intrinsic subthreshold oscillations, mixed-mode oscillations and subthreshold resonance do not necessarily determine the existence of a preferred spiking response to oscillatory inputs in the same frequency band. The results discussed in this paper highlight both the complexity of the suprathreshold responses to oscillatory inputs in neurons having resonant and amplifying currents with different time scales and the fact that the identity of the participating ionic currents is not enough to predict the resulting patterns, but additional dynamic information, captured by the geometric properties of the phase-space diagram, is needed.
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DOI：
10.1007/s00359-015-1036-1
发表时间：
2015-11-01
影响因子：
2.1
作者：
Rau, Florian;Clemens, Jan;Schreiber, Susanne
通讯作者：
Schreiber, Susanne
单神经元中多个本征振荡器动力学的理论框架
影响因子：
3.7
作者：
Michiel W. H. Remme;M. Lengyel;B. Gutkin
通讯作者：
B. Gutkin
影响因子：
2.5
作者：
Sciamanna, Giuseppe;Wilson, Charles J.
通讯作者：
Wilson, Charles J.
影响因子：
2.5
作者：
Heys, James G.;Giocomo, Lisa M.;Hasselmo, Michael E.
通讯作者：
Hasselmo, Michael E.
DOI：
10.1523/jneurosci.23-03-00943.2003
发表时间：
2003-02
期刊：
The Journal of Neuroscience
影响因子：
--
作者：
A. Prinz;Vatsala Thirumalai;E. Marder
通讯作者：
A. Prinz;Vatsala Thirumalai;E. Marder