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	HMSNN: Hippocampus inspired Memory Spiking Neural Network
	Zhang, Tielin ; Zeng, Yi ; Zhao, Dongcheng ; Wang, Liwei ; Zhao, Yuxuan ; Xu, Bo
	2016
关键词	RECALL MODELS
英文摘要	Human beings receive stimulations in primary sensory cortex and transfer them to higher brain regions automatically. What happened in this procedure? In this paper, we will focus on one of these regions (hippocampus) and try to simulate its working procedure by building an HMSNN (Hippocampus inspired Memory Spiking Neural Network) model. Dentate Gyrus (DG) and Cornu Ammonis area 3 (CA3) are the main regions of hippocampus and will be simulated by feed forward Spiking Neural Network (SNN) and recurrent Hopfield-like network respectively. From the structural perspective, the computational unit and the connectivity between neurons in HMSNN are all consistent with the anatomical-experimental results in hippocampus. From the functional perspective, the multi-scale memory formation, memory abstraction and memory retention will be shown in HMSNN model. In addition, the HMSNN is tested on MNIST handwritten digit dataset (with static images) and robot walking dataset (with dynamical images). The experimental result shows that: biological neural circuit inspired HMSNN shows comparable classification performance on both datasets compared to the state-of-art convolutional neural networks (CNNs), and shows significantly better performance compared to CNN when noises are introduced to the original images.; Strategic Priority Research Program of the Chinese Academy of Sciences [XD-B02060007]; Beijing Municipal Commission of Science and Technology [ZI51100000915070, ZI61100000216124]; CPCI-S(ISTP); 2301-2306
语种	英语
出处	IEEE International Conference on Systems, Man, and Cybernetics (SMC)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470134]
专题	软件与微电子学院
推荐引用方式 GB/T 7714	Zhang, Tielin,Zeng, Yi,Zhao, Dongcheng,et al. HMSNN: Hippocampus inspired Memory Spiking Neural Network. 2016-01-01.

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