Distinct current modules shape cellular dynamics in model neurons.
Distinct current modules shape cellular dynamics in model neurons.
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DOI：
10.1016/j.neuroscience.2016.08.016
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发表时间：
2016-10-15
期刊：
影响因子：
3.3
通讯作者：
Nair, Satish S.
中科院分区：
文献类型：
--
作者：
Alturki, Adel;Feng, Feng;Nair, Ajay;Guntu, Vinay;Nair, Satish S.
已知许多内在电流共同塑造神经元膜电位动力学或神经元特征。虽然电流集如何形成特定的特征，如尖峰特征或振荡已经被单独研究，但尚不清楚神经元的电流集如何共同形成其整个特征集。基于生物物理电导的神经元模型是解决这一重要问题的可行工具。我们假设电流被分组为不同的模块，这些模块形成特定的神经元特征或签名，例如静息电位、阈下振荡和尖峰波形，用于几类神经元。为了进行这种分组，一个模块内的电流应该对属于其他模块的电流具有最小的功能干扰。如果同一模块中的电流的门控函数在电压轴上被分组在一起，则满足该条件;相反，对于属于不同模块的电流，这些函数沿电压轴被沿着隔离。我们使用四个已发表的示例案例模型测试了这一假设，发现它对这些神经元类别是有效的。对电流的神经生物学组织的这种洞察还提出了一种直观、系统和稳健的方法来开发具有多种生物学特征的生物物理单细胞模型，其适用于手动和自动调谐方法。我们说明了使用两个例子的情况下啮齿动物的锥体神经元，从外侧杏仁核和海马的方法。该方法还有助于揭示单个核心隔室模型可以捕获多种神经元特性。这种生物物理单室模型有可能提高大型网络模型的保真度。
Numerous intrinsic currents are known to collectively shape neuronal membrane potential dynamics, or neuronal signatures. Although how sets of currents shape specific signatures such as spiking characteristics or oscillations has been studied individually, it is less clear how a neuron’s suite of currents jointly shape its entire set of signatures. Biophysical conductance based models of neurons represent a viable tool to address this important question. We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. This insight into the neurobiological organization of currents also suggests an intuitive, systematic, and robust methodology to develop biophysical single cell models with multiple biological characteristics applicable for both hand- and automated- tuning approaches. We illustrate the methodology using two example case rodent pyramidal neurons, from the lateral amygdala and the hippocampus. The methodology also helped reveal that a single core compartment model could capture multiple neuronal properties. Such biophysical single compartment models have potential to improve the fidelity of large network models.
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