Abstract It is now known that the spiking neuron is a basic unit of spiking neural networks (SNNs). Spiking neurons modulate the nervous cells via receiving external incentives, generation of action potential and firing spikes. The SNNs usually used for pattern recognition tasks or complex computation depending on the brain-like characteristic. Although the SNNs have no advantages comparing with the deep neural networks in terms of classification accuracy, the SNNs have more characteristics of biological neurons. In this paper, a hierarchical SNN, comprising convolutional and pooling layers, is designed. The proposed SNN consists of excitatory and inhibitory neurons based on the mechanism of the primate brain. A temporal coding (rank order) manner is used to encode the input patterns. It depends on the rank of the spike arrival on post synapses to establish the priority of input spikes for a particular pattern. The spike-timing-dependent plasticity (STDP) learning rule is used in convolutional layers to extract visual features in an unsupervised learning manner. During the classification stage, a lateral inhibition mechanism is used to prevent the non-firing neurons and produce distinguishable results. In order to improve the performance of our SNN, an ensemble SNN architecture using the voting method is proposed, and transfer learning is used to avoid re-training the SNN when solving the different tasks. The hand-written digits classification task on MNIST, CIFAR-10, and BreaKHis databases are used to verify the performance of the proposed SNN. Experimental results show that by using the ensemble architecture and transfer learning, the classification accuracy of 99.27% for the MNIST database, overall accuracy is 93% for the CIFAR-10 database, and overall accuracy is 96.97% for BreaKHis database. In the meantime, this work achieves a better performance than the benchmarking approaches. Taken together, the results of our work suggest that the ensemble SNN architecture with transfer learning is key to improving the performance of the SNN.

中文翻译：

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用于目标识别的集成无监督尖峰神经网络

摘要 现在已知尖峰神经元是尖峰神经网络（SNN）的基本单元。尖峰神经元通过接收外部激励、动作电位的产生和放电尖峰来调节神经细胞。SNN 通常用于模式识别任务或复杂计算，具体取决于类脑特征。尽管 SNN 在分类精度方面与深度神经网络相比没有优势，但 SNN 具有更多生物神经元的特征。在本文中，设计了一个包含卷积层和池化层的分层 SNN。所提出的 SNN 由基于灵长类大脑机制的兴奋性和抑制性神经元组成。时间编码（排序）方式用于对输入模式进行编码。它取决于峰值到达后突触的等级，以确定特定模式的输入峰值的优先级。在卷积层中使用脉冲时间依赖可塑性 (STDP) 学习规则以无监督学习方式提取视觉特征。在分类阶段，使用侧向抑制机制来阻止非发射神经元并产生可区分的结果。为了提高我们的 SNN 的性能，提出了一种使用投票方法的集成 SNN 架构，并使用迁移学习来避免在解决不同任务时重新训练 SNN。MNIST、CIFAR-10 和 BreaKHis 数据库上的手写数字分类任务用于验证所提出的 SNN 的性能。实验结果表明，通过使用集成架构和迁移学习，MNIST数据库的分类准确率为99.27%，CIFAR-10数据库的分类准确率为93%，BreaKHis数据库的分类准确率为96.97%。同时，这项工作取得了比基准测试方法更好的性能。总之，我们的工作结果表明，具有迁移学习的集成 SNN 架构是提高 SNN 性能的关键。