Systems and Modeling, Department of Electrical Engineering and Computer Science, University of Liège , Liège, B-4000, Belgium ; Laboratory of Pharmacology and GIGA Neurosciences, University of Liège , Liège, B-4000, Belgium ; Volen Center and Biology Department, Brandeis University , Waltham, Massachussetts 02454.
Systems and Modeling, Department of Electrical Engineering and Computer Science, University of Liège , Liège, B-4000, Belgium ; Department of Engineering, University of Cambridge , Cambridge, CB2 1PZ, United Kingdom.
eNeuro. 2015 Mar 25;2(1). doi: 10.1523/ENEURO.0031-14.2015. eCollection 2015 Jan-Feb.
Assessing the role of biophysical parameter variations in neuronal activity is critical to the understanding of modulation, robustness, and homeostasis of neuronal signalling. The paper proposes that this question can be addressed through the analysis of dynamic input conductances. Those voltage-dependent curves aggregate the concomitant activity of all ion channels in distinct timescales. They are shown to shape the current-voltage dynamical relationships that determine neuronal spiking. We propose an experimental protocol to measure dynamic input conductances in neurons. In addition, we provide a computational method to extract dynamic input conductances from arbitrary conductance-based models and to analyze their sensitivity to arbitrary parameters. We illustrate the relevance of the proposed approach for modulation, compensation, and robustness studies in a published neuron model based on data of the stomatogastric ganglion of the crab Cancer borealis.
评估生物物理参数变化在神经元活动中的作用对于理解神经元信号的调制、鲁棒性和内稳态至关重要。本文提出，可以通过分析动态输入电导来解决这个问题。那些电压依赖性曲线综合了不同时间尺度上所有离子通道的伴随活动。它们被证明可以形成决定神经元放电的电流-电压动力学关系。我们提出了一种测量神经元中动态输入电导的实验方案。此外，我们还提供了一种从任意基于电导的模型中提取动态输入电导的计算方法，并分析了它们对任意参数的敏感性。我们以基于北美的螃蟹 stomatogastric 神经节数据的已发表神经元模型为例，说明了所提出方法在调制、补偿和鲁棒性研究中的相关性。
