Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
IEEE Trans Biomed Circuits Syst. 2012 Apr;6(2):133-41. doi: 10.1109/TBCAS.2011.2182650.
Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model's output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron's parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron's output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron's parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed.
尖峰神经元模型被广泛应用于从理解最基本的神经行为到神经假肢等多种任务中。对单个神经元模型进行参数估计，以使模型的输出与生物神经元的输出相匹配，是一项极其重要的任务。手动调整参数以获得这种行为是一个困难且耗时的过程。当神经元在硅中实例化时（在这种模型中实现这些模型的有吸引力的媒介），由于制造缺陷使参数配置任务更加复杂。在本文中，我们展示了两种自动配置硅（硬件）神经元参数的方法。首先，我们展示了如何将最大似然方法应用于具有尖峰诱导电流的漏积分和触发硅神经元，以拟合神经元的输出到所需的尖峰时间。然后，我们展示了如何使用基于距离的方法来近似对数正态分布的负对数似然，也可以用于调整神经元的参数。我们得出的结论是，基于距离的方法更适合于硅神经元的参数配置，因为它具有优越的优化速度。
