A New Supervised Learning Algorithm for Spiking Neurons
A New Supervised Learning Algorithm for Spiking Neurons
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DOI：
10.1162/neco_a_00450
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发表时间：
2013-06
影响因子：
2.9
通讯作者：
Yan Xu;Xiaoqin Zeng;Shuiming Zhong
中科院分区：
文献类型：
--
作者：
Yan Xu;Xiaoqin Zeng;Shuiming Zhong
对发放神经元进行时间编码的监督学习的目的是使神经元发出由精确的发放时间编码的特定发放序列。如果只考虑神经元的运行时间，则对尖峰神经元的监督学习等价于通过调整神经元的突触权值来区分神经元运行过程中期望输出尖峰的时刻和其他时刻，这可以看作是一个分类问题。基于这一思想，本文提出了一种新的时间编码脉冲神经元的监督学习方法，该方法首先将监督学习转化为一个分类问题，然后利用感知器学习规则来解决该问题。实验结果表明，与现有的学习方法相比，该方法具有更高的学习精度和学习效率，更适合于解决复杂的实时问题。
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.