CASIA OpenIR  > 类脑智能研究中心
A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations
Zhao, Feifei1,3; Zeng, Yi1,2,3; Wang, Guixiang1; Bai, Jun1; Xu, Bo1,2,3
2018-04-01
发表期刊COGNITIVE COMPUTATION
卷号10期号:2页码:296-306
文章类型Article
摘要Decision making is a fundamental ability for intelligent agents (e.g., humanoid robots and unmanned aerial vehicles). During decision making process, agents can improve the strategy for interacting with the dynamic environment through reinforcement learning. Many state-of-the-art reinforcement learning models deal with relatively smaller number of state-action pairs, and the states are preferably discrete, such as Q-learning and Actor-Critic algorithms. While in practice, in many scenario, the states are continuous and hard to be properly discretized. Better autonomous decision making methods need to be proposed to handle these problems. Inspired by the mechanism of decision making in human brain, we propose a general computational model, named as prefrontal cortex-basal ganglia (PFC-BG) algorithm. The proposed model is inspired by the biological reinforcement learning pathway and mechanisms from the following perspectives: (1) Dopamine signals continuously update reward-relevant information for both basal ganglia and working memory in prefrontal cortex. (2) We maintain the contextual reward information in working memory. This has a top-down biasing effect on reinforcement learning in basal ganglia. The proposed model separates the continuous states into smaller distinguishable states, and introduces continuous reward function for each state to obtain reward information at different time. To verify the performance of our model, we apply it to many UAV decision making experiments, such as avoiding obstacles and flying through window and door, and the experiments support the effectiveness of the model. Compared with traditional Q-learning and Actor-Critic algorithms, the proposed model is more biologically inspired, and more accurate and faster to make decision.
关键词Prefrontal Cortex Working Memory Basal Ganglia Dopamine System Brain-inspired Decision Making Model
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1007/s12559-017-9511-3
关键词[WOS]COMPUTATIONAL MODEL ; CONNECTIONS ; CIRCUITS ; ORGANIZATION ; STRIATUM
收录类别SCI
语种英语
项目资助者Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060007) ; Beijing Municipal Commission of Science and Technology(Z161100000216124)
WOS研究方向Computer Science ; Neurosciences & Neurology
WOS类目Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号WOS:000430190600010
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/22014
专题类脑智能研究中心
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Zhao, Feifei,Zeng, Yi,Wang, Guixiang,et al. A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations[J]. COGNITIVE COMPUTATION,2018,10(2):296-306.
APA Zhao, Feifei,Zeng, Yi,Wang, Guixiang,Bai, Jun,&Xu, Bo.(2018).A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations.COGNITIVE COMPUTATION,10(2),296-306.
MLA Zhao, Feifei,et al."A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations".COGNITIVE COMPUTATION 10.2(2018):296-306.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhao, Feifei]的文章
[Zeng, Yi]的文章
[Wang, Guixiang]的文章
百度学术
百度学术中相似的文章
[Zhao, Feifei]的文章
[Zeng, Yi]的文章
[Wang, Guixiang]的文章
必应学术
必应学术中相似的文章
[Zhao, Feifei]的文章
[Zeng, Yi]的文章
[Wang, Guixiang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。