This paper studies synchronization phenomena of spike-trains and approximation of target spike-trains in a simple network of digital spiking neurons. Repeating integrate-and-fire behavior between a periodic base signal and constant firing threshold, the neurons can generate various spike-trains. Connecting multiple neurons by cross-firing with delay, the network is constructed. The network can exhibit multi-phase synchronization of various spike-trains. Stability of the synchronization phenomena can be guaranteed theoretically. Applying a simple winner-take-all switching, the network can approximate target spike-trains automatically. In order to evaluate the approximation performance, we present two metrics: spike-position error and spike missing rate. Using the metrics, approximation capability of the network is investigated in typical target signals. Presenting an FPGA based hardware prototype, typical synchronization phenomenon and spike-train approximation are confirmed experimentally.

中文翻译：

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一个简单的数字尖峰神经网络：同步和峰值列车逼近

本文研究了一个简单的数字尖峰神经元网络中尖峰序列的同步现象和目标尖峰序列的逼近。在周期性的基本信号和恒定的触发阈值之间重复积分和发射行为，神经元可以生成各种峰值训练。通过延迟交叉点火连接多个神经元，从而构建了网络。该网络可以展现出各种峰值序列的多相同步。理论上可以保证同步现象的稳定性。通过简单的赢家通吃切换，网络可以自动估算目标峰值列车。为了评估近似性能，我们提出了两个指标：尖峰位置误差和尖峰缺失率。使用指标，在典型的目标信号中研究了网络的近似能力。提出了一种基于FPGA的硬件原型，通过实验证实了典型的同步现象和尖峰脉冲逼近。