Improving learning efficiency of recurrent neural network through adjusting weights of all layers in a biologically-inspired framework
Xiao Huang; Wei Wu; Peijie Yin; Hong Qiao
2017
Conference NameInternational Joint Conference on Neural Networks (IJCNN)
Conference Date14-19 May 2017
Conference PlaceAnchorage, Alaska
AbstractBrain-inspired models have become a focus in artificial intelligence field. As a biologically plausible network, the recurrent neural network in reservoir computing framework has been proposed as a popular model of cortical computation because of its complicated dynamics and highly recurrent connections. To train this network, unlike adjusting only readout weights in liquid computing theory or changing only internal recurrent weights, inspired by global modulation of human emotions on cognition and motion control, we introduce a novel reward-modulated Hebbian learning rule to train the network by adjusting not only the internal recurrent weights but also the input connected weights and readout weights together, with solely delayed, phasic rewards. Experiment results show that the proposed method can train a recurrent neural network in near-chaotic regime to complete the motion control and workingmemory tasks with higher accuracy and learning efficiency.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20101
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Recommended Citation
GB/T 7714
Xiao Huang,Wei Wu,Peijie Yin,et al. Improving learning efficiency of recurrent neural network through adjusting weights of all layers in a biologically-inspired framework[C],2017.
Files in This Item: Download All
File Name/Size DocType Version Access License
Improving learning e(463KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xiao Huang]'s Articles
[Wei Wu]'s Articles
[Peijie Yin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xiao Huang]'s Articles
[Wei Wu]'s Articles
[Peijie Yin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xiao Huang]'s Articles
[Wei Wu]'s Articles
[Peijie Yin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Improving learning efficiency of recurrent neural network through adjusting weights of all layers in a biologically-inspired framework.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.