A Brain-Inspired Causal Reasoning Model Based on Spiking Neural Networks | |
Fang HongJian(方宏坚)3,4; Zeng Yi(曾毅)1,2,3,4 | |
2021-09 | |
会议日期 | 18-22 July 2021 |
会议地点 | Shenzhen, China |
英文摘要 | In today's field of artificial intelligence, the plausibility of neural networks still lacks breakthrough. We believe one reason is that the current deep neural network method based on the framework of statistical learning, in essence, only uses the correlation between the data to make predictions, different from human beings who complete reasoning and decision-making by invariably induce the causality between propositions. To solve this problem, previous researchers have proposed some causal reasoning approaches based on the causal graphs. Inspired by the human brain, we propose Causal Reasoning Spiking Neural Network(CRSNN) to implement the causal reasoning with STDP learning rule and population coding mechanism. After the verification experiment in the basic case, we show the possibility of implementation causal reasoning with SNN. As far as we know, this is the first time that SNN is used to complete causal reasoning tasks, which is an essential topic both in cognitive neuroscience and artificial intelligence. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/49911] |
专题 | 类脑智能研究中心_类脑认知计算 |
通讯作者 | Zeng Yi(曾毅) |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China 3.School of Future Technology, University of Chinese Academy of Sciences, Beijing, China 4.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Fang HongJian,Zeng Yi. A Brain-Inspired Causal Reasoning Model Based on Spiking Neural Networks[C]. 见:. Shenzhen, China. 18-22 July 2021. |
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