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A Spiking Neural Network Based Autonomous Reinforcement Learning Model and Its Application in Decision Making
Guixiang Wang1; Yi Zeng1,2; Bo Xu1,2
2016-11
Conference NameBICS 2016: The 8th International Conference on Brain Inspired Cognitive Systems
Source PublicationConferences on the 8th International Conference on Brain-inspired Cognitive System
Conference Date2016年11月27-30日
Conference PlaceBeijing, China
Abstract

In this paper, we propose an autonomous spiking neural network model for decision making. The model is an expansion of the basal ganglia circuitry with automatic environment perception, which constructs environmental states automatically from image inputs. The work in this paper has the following contributions: (1) In our model, the simplified Hodgkin-Huxley computing model is developed to achieve calculation efficiency closed to the LIF model and is used to obtain and test the ionic level properties in cognition. (2) A spike based motion perception mechanism is proposed to extract key elements for learning process from raw pixels without large amount of training. We apply our model in the “flappy bird” game and it play well after dozens of trainings. The model gets similar learning performance with human at the start of training. Besides, our model simulates cognitive defects when blocking some of sodium or potassium ion channels in the Hodgkin-Huxley model and this is an exploration of cognition deep into ionic level


KeywordSpiking Neural Network Hodgkin-huxley Basal Ganglia Motion Perception
Subject Area交叉与边缘领域的力学
Indexed ByCPCI-T
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12619
Collection类脑智能研究中心
Corresponding AuthorYi Zeng
Affiliation1.Institute of Automation, Chinese Academy of Science
2.Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences,
Recommended Citation
GB/T 7714
Guixiang Wang,Yi Zeng,Bo Xu. A Spiking Neural Network Based Autonomous Reinforcement Learning Model and Its Application in Decision Making[C],2016.
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