HMSNN: Hippocampus inspired Memory Spiking Neural Network
文献类型：会议论文
| 作者 | 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
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| 出版日期 | 2016 |
| 会议日期 | October 9-12, 2016 |
| 会议地点 | Budapest, Hungary |
| 关键词 | Hippocampus Spiking Neural Network Classification Task |
| 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. |
| 源URL | [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]. 见:. Budapest, Hungary. October 9-12, 2016. |
入库方式： OAI收割
来源：自动化研究所
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