CASIA OpenIR  > 类脑智能研究中心  > 神经计算及脑机交互
HMSNN: Hippocampus inspired Memory Spiking Neural Network
Zhang TL(张铁林)1; Ceng Y(曾毅)1; Zhao DC(赵东城)1; Wang LW(王立伟)2; Zhao YX(赵宇轩)1; Xu B(徐波)1; Tielin Zhang, Yi Zeng
2016
会议名称Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)
会议日期October 9-12, 2016
会议地点Budapest, Hungary
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.
关键词Hippocampus Spiking Neural Network Classification Task
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/22084
专题类脑智能研究中心_神经计算及脑机交互
通讯作者Tielin Zhang, Yi Zeng
作者单位1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.School of Software and Microelectronics, Peking University, Beijing, China
推荐引用方式
GB/T 7714
Zhang TL,Ceng Y,Zhao DC,et al. HMSNN: Hippocampus inspired Memory Spiking Neural Network[C],2016.
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