IEEE Trans Neural Netw Learn Syst. 2015 Dec;26(12):3137-49. doi: 10.1109/TNNLS.2015.2404938. Epub 2015 Mar 18.
Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.
最近的研究表明，尖峰神经网络（SNN）具有模拟大脑中复杂信息处理的潜力。有生物学证据证明，利用尖峰的精确时间进行信息编码。然而，神经元被训练在精确时间发射的确切学习机制仍然是一个悬而未决的问题。大多数现有的 SNN 学习方法都是基于权重调整的。然而，也有生物学证据表明突触延迟不是恒定的。在本文中，提出了一种用于尖峰神经元的学习方法，称为延迟学习远程监督方法（DL-ReSuMe），该方法将延迟移位方法和基于 ReSuMe 的权重调整相结合，以提高学习性能。DL-ReSuMe 使用了更具生物学合理性的特性，如延迟学习，并且比 ReSuMe 需要更少的权重调整。仿真结果表明，与 ReSuMe 相比，所提出的 DL-ReSuMe 方法在学习准确性和学习速度方面都有所提高。
