DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networks
This addresses the problem of limited expressive power in SNNs for researchers and practitioners in neuromorphic computing, offering an incremental improvement with minimal parameter overhead.
The paper tackled the limitations of the Leaky Integrate-and-Fire model in Spiking Neural Networks by proposing the Dual Adaptive Leaky Integrate-and-Fire model, which achieved superior accuracy with fewer timesteps on datasets like CIFAR10/100, ImageNet, CIFAR10-DVS, and DVS128 Gesture compared to state-of-the-art methods.
Spiking Neural Networks (SNNs) are valued for their ability to process spatio-temporal information efficiently, offering biological plausibility, low energy consumption, and compatibility with neuromorphic hardware. However, the commonly used Leaky Integrate-and-Fire (LIF) model overlooks neuron heterogeneity and independently processes spatial and temporal information, limiting the expressive power of SNNs. In this paper, we propose the Dual Adaptive Leaky Integrate-and-Fire (DA-LIF) model, which introduces spatial and temporal tuning with independently learnable decays. Evaluations on both static (CIFAR10/100, ImageNet) and neuromorphic datasets (CIFAR10-DVS, DVS128 Gesture) demonstrate superior accuracy with fewer timesteps compared to state-of-the-art methods. Importantly, DA-LIF achieves these improvements with minimal additional parameters, maintaining low energy consumption. Extensive ablation studies further highlight the robustness and effectiveness of the DA-LIF model.