In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA’s performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.

中文翻译：

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CRBA：基于竞争性尖刺神经网络的基于竞争率的算法

在本文中，我们提出了一种基于竞争率的算法（CRBA），该算法近似于竞争性尖峰神经网络（CSNN）的操作。CRBA基于在样本展示过程中对神经元之间竞争的建模，可以将其简化为基于点积运算和使用离散期望最大化算法对神经元进行排名；后者等效于与峰值时间有关的可塑性规则。在MNIST和Fashion-MNIST数据集上，将CRBA的性能与CSNN的性能进行了比较。结果表明，CRBA的性能与CSNN相当，而计算时间却减少了三个数量级。重要的是，我们证明了CRBA掌握的权重和触发阈值可用于初始化CSNN的参数，从而使CSNN的运行效率更高。