Understanding the Functional Roles of Modelling Components in Spiking Neural Networks

• URL: http://arxiv.org/abs/2403.16674v1
• Date: Mon, 25 Mar 2024 12:13:20 GMT
• Title: Understanding the Functional Roles of Modelling Components in Spiking Neural Networks
• Authors: Huifeng Yin, Hanle Zheng, Jiayi Mao, Siyuan Ding, Xing Liu, Mingkun Xu, Yifan Hu, Jing Pei, Lei Deng,
• Abstract summary: Spiking neural networks (SNNs) are promising in achieving high computational efficiency with biological fidelity. We investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based 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.
• Score: 9.448298335007465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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