Understanding the Functional Roles of Modelling Components in Spiking Neural Networks

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.

References in corpus (7)

• Deep Learning in Spiking Neural Networks
• Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
• Visualizing the Loss Landscape of Neural Nets
• The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
• GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
• Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks
• Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition