Rowcliffe Phill, Feng Jianfeng
Department of Informatics, School of Science and Technology, University of Sussex, Brighton, East Sussex BN1 9QH, UK.
IEEE Trans Neural Netw. 2008 Sep;19(9):1626-40. doi: 10.1109/TNN.2008.2000999.
In this paper, spiking neuronal models employing means, variances, and correlations for computation are introduced. We present two approaches in the design of spiking neuronal networks, both of which are applied to engineering tasks. In exploring the input-output relationship of integrate-and-fire (IF) neurons with Poisson inputs, we are able to define mathematically robust learning rules, which can be applied to multilayer and time-series networks. We show through experimental applications that it is possible to train spike-rate networks on function approximation problems and on the dynamic task of robot arm control.
本文介绍了采用均值、方差和相关性进行计算的脉冲神经元模型。我们提出了两种设计脉冲神经网络的方法，这两种方法都应用于工程任务。在探索具有泊松输入的积分发放（IF）神经元的输入-输出关系时，我们能够定义数学上稳健的学习规则，这些规则可应用于多层和时间序列网络。我们通过实验应用表明，在函数逼近问题和机器人手臂控制的动态任务上训练脉冲率网络是可行的。
