Luo Xiaoling, Qu Hong, Zhang Yun, Chen Yi
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Front Neurosci. 2019 Jun 6;13:559. doi: 10.3389/fnins.2019.00559. eCollection 2019.
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.
神经回路通过精确计时的脉冲发放对多种感觉刺激做出反应。受这一现象的启发，人们提出了基于脉冲时间的脉冲神经网络（SNN）来处理和记忆时空脉冲模式。然而，与人类大脑相比，SNN现有学习算法的响应速度和准确性仍显不足。为了进一步提高精确计时脉冲学习的性能，我们提出了一种新的权重更新机制，该机制总是在第一个错误的输出脉冲时间调整突触权重。所提出的学习算法可以准确地调整对期望和非期望发放时间的膜电位有贡献的突触权重。实验结果表明，与经典序列学习算法远程监督方法（ReSuMe）和脉冲模式关联神经元（SPAN）相比，所提出的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，配备所提出学习方法的基于SNN的计算模型在语音识别任务中比其他受生物启发的基线系统取得了更好的识别结果。
