Understanding the Functional Roles of Modelling Components in Spiking
  Neural Networks
Huifeng Yin Hanle Zheng Jiayi Mao Siyuan Ding Xing Liu Mingkun Xu Yifan Hu Jing Pei Lei Deng
Spiking neural networks (SNNs), inspired by the neural circuits of the brain,
are promising in achieving high computational efficiency with biological
fidelity. Nevertheless, it is quite difficult to optimize SNNs because the
functional roles of their modelling components remain unclear. By designing and
evaluating several variants of the classic model, we systematically investigate
the functional roles of key modelling components, leakage, reset, and
recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive
experiments, we demonstrate how these components influence the accuracy,
generalization, and robustness of SNNs. Specifically, we find that the leakage
plays a crucial role in balancing memory retention and robustness, the reset
mechanism is essential for uninterrupted temporal processing and computational
efficiency, and the recurrence enriches the capability to model complex
dynamics at a cost of robustness degradation. With these interesting
observations, we provide optimization suggestions for enhancing the performance
of SNNs in different scenarios. This work deepens the understanding of how SNNs
work, which offers valuable guidance for the development of more effective and
robust neuromorphic models.