Encoding information with precise spike timings using spike-coded neurons has been shown to be more computationally powerful than rate-coded approaches. However, most existing supervised learning algorithms for spiking neurons are complicated and offer poor time complexity. To address these limitations, we propose a supervised multi-spike learning algorithm which reduces the required number of training iterations. We achieve this by formulating a large number of weight updates as a linear constraint satisfaction problem, which can be solved efficiently. Experimental results show this method offers better efficiency compared to existing algorithms on the MNIST dataset. Additionally, we provide experimental results on the classification capacity of the LIF neuron model, relative to several parameters of the system.

翻译：使用峰值编码神经元进行精确的峰值计时信息编码,其计算能力比速率编码方法要强。然而,大多数现有的神经元跳跃的受监督学习算法都复杂,而且时间复杂度低。为解决这些限制,我们建议采用受监督的多spike学习算法,以减少所需培训迭代次数。我们通过将大量重量更新作为线性约束满意度问题来实现这一目标,这个问题可以有效解决。实验结果表明,这种方法比MNIST数据集的现有算法效率更高。此外,我们提供了相对于该系统若干参数的LIF神经模型分类能力的实验结果。