Abstract In general, Spiking Neural Networks(SNNs) have a network structure with special methods applied to neuron models and information transmission to mimic humans biologically. However, the existing SNN structures have two problems, such as fixed existing Artificial Neural Network structures and difficulty in learning due to lack of spike information during information transfer. Recently, many approaches of learning SNNs have been proposed in order to alleviate those two drawbacks. However, it is very difficult to overcome the drawbacks only by the learning method without the fundamental solution for the structure. In order to solve the problem of structure and learning method, we propose a novel flexible network construction method using neurogenesis-based cell proliferation concept and Triple Simultaneous- Spike Timing Dependent Plasticity(TS-STDP) which is improved learning method through neuroplasticity-based spike timing. We build the network flexibly and automatically by employing the concept that not only one neuron exists in the neural network, but also that various cells proliferate and transform from stem cells to function. In addition, TS-STDP is designed by considering the correlation of signal response among several neurons to solve the lack of information due to spike sparsity, which is a disadvantage of STDP. In the experimental section, we demonstrate and analyze our method using Mixed National Institute of Standards and Technology image data. Our method is 2.7× better in memory efficiency and 1.7× better in computational efficiency than the existing method. In particular, the research that automatically constructs the network structure is the first to my knowledge.

中文翻译：

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N3-CPL：基于神经可塑性的神经形态网络细胞增殖学习

摘要 一般而言，尖峰神经网络（SNNs）具有网络结构，将特殊方法应用于神经元模型和信息传输，以在生物学上模仿人类。然而，现有的 SNN 结构存在两个问题，例如固定现有的人工神经网络结构和由于信息传递过程中缺乏尖峰信息而导致学习困难。最近，为了减轻这两个缺点，已经提出了许多学习 SNN 的方法。但是，如果没有结构的根本解决方案，仅通过学习方法来克服这些缺点是非常困难的。为了解决结构和学习方法的问题，我们提出了一种使用基于神经发生的细胞增殖概念和三重同步尖峰定时相关可塑性（TS-STDP）的新型灵活网络构建方法，这是通过基于神经可塑性的尖峰定时改进的学习方法。我们利用神经网络中不仅存在一个神经元，而且各种细胞增殖并从干细胞转变为功能的概念，灵活、自动地构建网络。此外，TS-STDP 的设计考虑了多个神经元之间信号响应的相关性，以解决 STDP 的一个缺点，即尖峰稀疏导致的信息不足。在实验部分，我们使用美国国家标准与技术研究院的混合图像数据来演示和分析我们的方法。我们的方法在内存效率方面提高了 2.7 倍，而 1. 计算效率比现有方法高 7 倍。尤其是自动构建网络结构的研究，在我看来是第一次。