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首页> 外文期刊>Journal of Computational Neuroscience >Efficient fitting of conductance-based model neurons from somatic current clamp
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Efficient fitting of conductance-based model neurons from somatic current clamp

机译：从体电流钳有效拟合基于电导的模型神经元

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摘要

Estimating biologically realistic model neurons from electrophysiological data is a key issue in neuroscience that is central to understanding neu-ronal function and network behavior. However, directly fitting detailed Hodgkin-Huxley type model neurons to somatic membrane potential data is a notoriously difficult optimization problem that can require hours/days of supercomputing time. Here we extend an efficient technique that indirectly matches neuronal currents derived from somatic membrane potential data to two-compartment model neurons with passive dendrites. In consequence, this approach can fit semi-realistic detailed model neurons in a few minutes. For validation, fits are obtained to model-derived data for various thalamo-cortical neuron types, including fast/regular spiking and bursting neurons. A key aspect of the validation is sensitivity testing to perturbations arising in experimental data, including sampling rates, inadequately estimated membrane dynamics/channel kinetics and intrinsic noise. We find that maximal conductance estimates and the resulting membrane potential fits diverge smoothly and monotonically from near-perfect matches when unperturbed. Curiously, some perturbations have little effect on the error because they are compensated by the fitted maximal conductances. Therefore, the extended current-based technique applies well under moderately inaccurate model assumptions, as required for application to experimental data. Furthermore, the accompanying perturbation analysis gives insights into neuronal homeostasis, whereby tuning intrinsic neuronal properties can compensate changes from development or neurodegeneration.
机译：从电生理数据估计生物学现实模型神经元是神经科学中的关键问题，这对理解神经元功能和网络行为至关重要。但是，直接将详细的霍奇金-赫克斯利（Hodgkin-Huxley）型模型神经元拟合到体细胞膜电位数据是一个众所周知的困难的优化问题，可能需要数小时/天的超级计算时间。在这里，我们扩展了一种有效的技术，该技术可将源自体细胞膜电位数据的神经元电流间接匹配到具有被动树突的两室模型神经元。因此，这种方法可以在几分钟内适应半现实的详细模型神经元。为了进行验证，获得了适合各种丘脑皮质神经元类型的模型衍生数据的拟合，包括快速/常规尖峰和爆发性神经元。验证的一个关键方面是对实验数据（包括采样率，膜动力学/通道动力学估计不足和固有噪声）产生的扰动进行敏感性测试。我们发现最大电导估计值和所产生的膜电势拟合在不受干扰时会从近乎完美的匹配中平滑单调地偏离。奇怪的是，某些微扰对误差的影响很小，因为它们被拟合的最大电导补偿。因此，扩展的基于电流的技术可以很好地适用于中等误差的模型假设，这是应用于实验数据所必需的。此外，伴随的扰动分析可深入了解神经元的动态平衡，从而调节神经元的固有特性可以补偿发育或神经退行性变的变化。

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