基于脉冲序列核的脉冲神经元监督学习算法

A New Supervised Learning Algorithm for Spiking Neurons Based on Spike Train Kernels

脉冲神经元应用脉冲时间编码神经信息,监督学习的目标是对于给定的突触输入产生任意的期望脉冲序列.但由于神经元脉冲发放过程的不连续性,构建高效的脉冲神经元监督学习算法非常困难,同时也是该研究领域的重要问题.基于脉冲序列的核函数定义,提出了一种新的脉冲神经元监督学习算法,特点是应用脉冲序列核构造多脉冲误差函数和对应的突触学习规则,并通过神经元的实际脉冲发放频率自适应地调整学习率.将该算法用于脉冲序列的学习任务,期望脉冲序列采用Poisson过程或线性方法编码,并分析了不同的核函数对算法学习性能的影响.实验结果表明该算法具有较高的学习精度和良好的适应能力,在处理复杂的时空脉冲模式学习问题时十分有效.

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit arbitrary spike trains in response to given synaptic inputs. However, due to the discontinuity in the spike process, the formula- tion of efficient supervised learning algorithms for spiking neurons is difficult and remains an important problem in the re- search area. Based on the definition of kernel functions for spike trains,this paper proposes a new supervised learning algo- rithm for spiking neurons with temporal encoding. The learning rule for synapses is developed by constructing the multiple spikes error function using spike train kernels, and its learning rate is adaptively adjusted according to the actual firing rate of spiking neurons during learning. The proposed algorithm is successfully applied to various spike trains learning tasks, in which the desired spike trains are encoded by Poisson process or linear method. Furthermore, the effect of different kernels on the performance of the learning algorithm is also analyzed. The experiment results show that our proposed method has higher learning accuracy and flexibility than the existing learning methods, so it is effective for solving complex spatio-tem- poral spike pattern learning problems.