• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons
【24h】

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons

机译：DL-ReSuMe：刺激神经元的基于延迟学习的远程监督方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：最近的研究表明，增强神经网络（SNN）可以对大脑中的复杂信息处理进行建模。有生物学证据证明使用尖峰的精确定时进行信息编码。然而，训练神经元在精确时间发射的精确学习机制仍然是一个悬而未决的问题。现有的大多数SNN学习方法都是基于权重调整的。但是，也有生物学证据表明突触延迟不是恒定的。本文提出了一种学习神经突刺的学习方法，称为延迟学习远程监督方法（DL-ReSuMe），将延迟移位方法与基于ReSuMe的权重调整相结合以提高学习性能。 DL-ReSuMe具有更多生物学上合理的属性，例如延迟学习，并且与ReSuMe相比，需要更少的体重调节。仿真结果表明，与ReSuMe相比，所提出的DL-ReSuMe方法可以提高学习准确性和学习速度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号