Table_3_Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms.XLSX

The automatic fitting of spiking neuron models to experimental data is a challenging problem. The integrate and fire model and Hodgkin–Huxley (HH) models represent the two complexity extremes of spiking neural models. Between these two extremes lies two and three differential-equation-based models. In this work, we investigate the problem of parameter estimation of two simple neuron models with a sharp reset in order to fit the spike timing of electro-physiological recordings based on two problem formulations. Five optimization algorithms are investigated; three of them have not been used to tackle this problem before. The new algorithms show improved fitting when compared with the old ones in both problems under investigation. The improvement in fitness function is between 5 and 8%, which is achieved by using the new algorithms while also being more consistent between independent trials. Furthermore, a new problem formulation is investigated that uses a lower number of search space variables when compared to the ones reported in related literature.

将尖峰神经元模型自动拟合至实验数据是一项极具挑战性的课题。整合-发放模型（Integrate and Fire Model）与霍奇金-赫胥黎（Hodgkin-Huxley, HH）模型代表了尖峰神经元模型的两个复杂度极端。介于这两类极端模型之间的是基于2个与3个微分方程的神经元模型。本研究针对两类带尖锐重置机制的简单神经元模型，基于两种问题表述形式，开展参数估计研究以拟合电生理记录的尖峰时序。本次研究共考察了五种优化算法，其中三种此前从未被用于解决该类问题。在所考察的两类问题中，新型算法的拟合效果均优于传统算法。采用新型算法可使适应度函数提升5%至8%，且在独立重复实验中结果更具一致性。此外，本研究还提出了一种新型问题表述形式，相较于相关文献中报道的方案，该形式的搜索空间变量数量更少。