Spiking neural networks (SNNs) has attracted much attention due to its great potential of modeling time-dependent signals. The firing rate of spiking neurons is decided by control rate which is fixed manually in advance, and thus, whether the firing rate is adequate for modeling actual time series relies on fortune. Though it is demanded to have an adaptive control rate, it is a non-trivial task because the control rate and the connection weights learned during the training process are usually entangled. In this paper, we show that the firing rate is related to the eigenvalue of the spike generation function. Inspired by this insight, by enabling the spike generation function to have adaptable eigenvalues rather than parametric control rates, we develop the Bifurcation Spiking Neural Network (BSNN), which has an adaptive firing rate and is insensitive to the setting of control rates. Experiments validate the effectiveness of BSNN on a broad range of tasks, showing that BSNN achieves superior performance to existing SNNs and is robust to the setting of control rates.

翻译：Spik 神经网络(SNNS)因其具有模拟时间性信号的巨大潜力而吸引了很大关注。 Spik 神经网络(SNNs)的发射率是由预先人工固定的控制率决定的,因此,发射率是否足以模拟实际时间序列取决于命运。尽管要求它有一个适应性控制率,但它是一项非三重性的任务,因为培训过程中学到的控制率和连接权重通常被缠绕在一起。在本文中,我们表明发射率与峰值生成功能的二元值有关。我们受这一洞察的启发,通过使峰值生成功能具有适应性超重的天值,而不是准度控制率,我们开发了具有适应性发射率且对控制率设定不敏感的双倍Spik Neural网络(BSNNN) 。实验证实BSNN在广泛任务上的有效性,表明BSNN达到比现有SNN的更高性能,并且与控制率的设定是稳健的。