期刊
PATTERN RECOGNITION
卷 94, 期 -, 页码 87-95出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2019.05.015
关键词
Spiking neural networks; Deep architecture; Digit recognition; STDP; Reward-modulated STDP; Latency coding
资金
- Iran National Science Foundation: INSF [96005286]
- European Research Council under the European Union [323711]
- Center for International Scientific Studies & Collaboration (CISSC)
- French Embassy in Iran
- NVIDIA GPU Grant Program
- European Research Council (ERC) [323711] Funding Source: European Research Council (ERC)
The primate visual system has inspired the development of deep artificial neural networks, which have revolutionized the computer vision domain. Yet these networks are much less energy-efficient than their biological counterparts, and they are typically trained with backpropagation, which is extremely data-hungry. To address these limitations, we used a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme. We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lower layers and reward-modulated STDP (R-STDP) for the higher ones. In short, with R-STDP a correct (resp. incorrect) decision leads to STDP (resp. anti-STDP). This approach led to an accuracy of 97.2% on MNIST, without requiring an external classifier. In addition, we demonstrated that R-STDP extracts features that are diagnostic for the task at hand, and discards the other ones, whereas STDP extracts any feature that repeats. Finally, our approach is biologically plausible, hardware friendly, and energy-efficient. (C) 2019 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据