Understanding the Functional Roles of Modelling Components in Spiking Neural Networks

2024-03-25

Neuromorph. Comput. Eng.

AI Summary

The functional roles and quantitative impacts of leak, reset, and recurrent mechanisms in Leaky Integrate-and-Fire (LIF) spiking neural networks (SNNs) on accuracy, generalization, and robustness remain poorly understood. Method: We conduct systematic ablation studies, multi-scenario generalization evaluation, and rigorous robustness stress testing—including noise corruption, temporal perturbation, and distributional shift. Contribution/Results: We establish that (i) the leak mechanism governs the trade-off between memory retention and noise robustness; (ii) reset operations are essential for modeling continuous temporal dynamics; and (iii) recurrent connectivity enhances dynamic representation capacity but substantially degrades robustness. Building on these insights, we propose a task-aware SNN component co-optimization principle that improves model stability and adaptability under diverse adversarial conditions—while preserving computational efficiency and neurobiological plausibility.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Spiking Neural Networks
• Computational Efficiency
• Biological Fidelity

Innovation

• Spiking Neural Networks (SNNs)
• Leaky-Reset Dynamics
• Performance Optimization Strategies