Keren Naomi, Peled Noam, Korngreen Alon
Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
J Neurophysiol. 2005 Dec;94(6):3730-42. doi: 10.1152/jn.00408.2005. Epub 2005 Aug 10.
Compartmental models with many nonlinearly and nonhomogeneous distributions of voltage-gated conductances are routinely used to investigate the physiology of complex neurons. However, the number of loosely constrained parameters makes manually constructing the desired model a daunting if not impossible task. Recently, progress has been made using automated parameter search methods, such as genetic algorithms (GAs). However, these methods have been applied to somatically recorded action potentials using relatively simple target functions. Using a genetic minimization algorithm and a reduced compartmental model based on a previously published model of layer 5 neocortical pyramidal neurons we compared the efficacy of five cost functions (based on the waveform of the membrane potential, the interspike interval, trajectory density, and their combinations) to constrain the model. When the model was constrained using somatic recordings only, a combined cost function was found to be the most effective. This combined cost function was then applied to investigate the contribution of dendritic and axonal recordings to the ability of the GA to constrain the model. The more recording locations from the dendrite and the axon that were added to the data set the better was the genetic minimization algorithm able to constrain the compartmental model. Based on these simulations we propose an experimental scheme that, in combination with a genetic minimization algorithm, may be used to constrain compartmental models of neurons.
具有许多电压门控电导非线性和非均匀分布的房室模型通常用于研究复杂神经元的生理学。然而，松散约束参数的数量使得手动构建所需模型即使不是不可能也是一项艰巨的任务。最近，使用遗传算法（GAs）等自动参数搜索方法取得了进展。然而，这些方法已应用于使用相对简单目标函数的体细胞记录动作电位。我们使用遗传最小化算法和基于先前发表的第5层新皮质锥体神经元模型的简化房室模型，比较了五种成本函数（基于膜电位波形、峰间间隔、轨迹密度及其组合）对模型的约束效果。当仅使用体细胞记录来约束模型时，发现组合成本函数最有效。然后应用此组合成本函数来研究树突和轴突记录对遗传算法约束模型能力的贡献。添加到数据集中的来自树突和轴突的记录位置越多，遗传最小化算法就越能有效地约束房室模型。基于这些模拟，我们提出了一种实验方案，该方案与遗传最小化算法相结合，可用于约束神经元的房室模型。
