First Error-Based Supervised Learning Algorithm for Spiking Neural Networks
First Error-Based Supervised Learning Algorithm for Spiking Neural Networks
复制标题
第一个基于误差的尖峰神经网络监督学习算法
DOI：
10.3389/fnins.2019.00559
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发表时间：
2019-06-06
影响因子：
4.3
通讯作者：
Chen, Yi
中科院分区：
文献类型：
--
作者：
Luo, Xiaoling;Qu, Hong;Chen, Yi
神经回路通过发出精确定时的尖峰来响应多种感觉刺激。受这种现象的启发，基于尖峰时序的尖峰神经网络（SNN）被提出来处理和记忆时空尖峰模式。然而，现有的SNN学习算法的响应速度和准确性与人脑相比仍然存在不足。为了进一步提高学习精确定时尖峰的性能，我们提出了一种新的权重更新机制，该机制始终在第一个错误的输出尖峰时间调整突触权重。所提出的学习算法可以准确地调整有助于期望和非期望放电时间的膜电位的突触权重。实验结果表明，与经典的序列学习算法远程监督方法（ReSuMe）和尖峰模式关联神经元（SPAN）相比，该算法具有更高的准确性、更好的鲁棒性和更少的计算资源。此外，与其他仿生基线系统相比，配备所提出的学习方法的基于 SNN 的计算模型在语音识别任务中取得了更好的识别结果。
Neural circuits respond to multiple sensory stimuli by firing precisely timed spikes. Inspired by this phenomenon, the spike timing-based spiking neural networks (SNNs) are proposed to process and memorize the spatiotemporal spike patterns. However, the response speed and accuracy of the existing learning algorithms of SNNs are still lacking compared to the human brain. To further improve the performance of learning precisely timed spikes, we propose a new weight updating mechanism which always adjusts the synaptic weights at the first wrong output spike time. The proposed learning algorithm can accurately adjust the synaptic weights that contribute to the membrane potential of desired and non-desired firing time. Experimental results demonstrate that the proposed algorithm shows higher accuracy, better robustness, and less computational resources compared with the remote supervised method (ReSuMe) and the spike pattern association neuron (SPAN), which are classic sequence learning algorithms. In addition, the SNN-based computational model equipped with the proposed learning method achieves better recognition results in speech recognition task compared with other bio-inspired baseline systems.