CASIA OpenIR  > 类脑智能研究中心  > 神经计算及脑机交互
A Plasticity-centric Approach to Train the Non-differential Spiking Neural Networks
Zhang TL(张铁林)1,2; Ceng Y(曾毅)1,2,3; Zhao DC(赵东城)1,3; Shi MT(史梦婷)1,3; Tielin Zhang, Yi Zeng
2018-02
Conference NameThirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018)
Conference DateFebruary 2-7, 2018
Conference PlaceNew Orleans, Louisiana, USA.
AbstractMany efforts have been taken to train spiking neural networks (SNNs), but most of them still need improvements due to the discontinuous and non-differential characteristics of SNNs. While the mammalian brains solve these kinds of problems by integrating a series of biological plasticity learning rules. In this paper, we will focus on two biological plausible methodologies and try to solve these catastrophic training problems in SNNs. Firstly, the biological neural network will try to keep a balance between inputs and outputs on both the neuron and the network levels. Secondly, the biological synaptic weights will be passively updated by the changes of the membrane potentials of the neighbour-hood neurons, and the plasticity of synapses will not propagate back to other previous layers. With these biological inspirations, we propose Voltage-driven Plasticity-centric SNN (VPSNN), which includes four steps, namely: feed forward inference, unsupervised equilibrium state learning, supervised last layer learning and passively updating synaptic weights based on spiketiming dependent plasticity (STDP). Finally we get the accuracy of 98.52% on the hand-written digits classification task on MNIST. In addition, with the help of a visualization tool, we try to analyze the black box of SNN and get better understanding of what benefits have been acquired by the proposed method.
KeywordSpiking Neural Network
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22081
Collection类脑智能研究中心_神经计算及脑机交互
Corresponding AuthorTielin Zhang, Yi Zeng
Affiliation1.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.University of Chinese Academy of Sciences, Beijing, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang TL,Ceng Y,Zhao DC,et al. A Plasticity-centric Approach to Train the Non-differential Spiking Neural Networks[C],2018.
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