Reid Michael S, Brown Edgar A, DeWeerth Stephen P
Laboratory for Neuroengineering, Georgia Institute of Technology, 313 Ferst Drive, Atlanta, GA 30332, USA.
Biol Cybern. 2007 Jun;96(6):625-34. doi: 10.1007/s00422-007-0156-2. Epub 2007 May 9.
We demonstrate a parameter-space search algorithm using a computational model of a single-compartment neuron with conductance-based Hodgkin-Huxley dynamics. To classify bursting (the desired behavior), we use a simple cost function whose inputs are derived from the frequency content of the neural output. Our method involves the repeated use of a stochastic gradient descent-type algorithm to locate parameter values that allow the neural model to produce bursting within a specified tolerance. We demonstrate good results, including those showing that the utility of our algorithm improves as the pre-defined allowable parameter ranges increase and that the initial approach to our method is computationally efficient.
我们展示了一种参数空间搜索算法，该算法使用具有基于电导的霍奇金-赫胥黎动力学的单室神经元计算模型。为了对爆发行为（期望的行为）进行分类，我们使用一个简单的代价函数，其输入来自神经输出的频率成分。我们的方法涉及重复使用随机梯度下降型算法来定位参数值，以使神经模型在指定容差范围内产生爆发行为。我们展示了良好的结果，包括表明随着预定义的允许参数范围增加，我们算法的效用会提高，以及我们方法的初始方法在计算上是高效的。
