Making Spiking Neural Networks Robust and Well-Performing via Fractional-Order Plasticity for IoT Edge Intelligence

Traditional artificial neural networks often face stability challenges in noisy Internet of Things (IoT) environments, owing to their dependence on static weights and deterministic computational frameworks. Spiking neural networks (SNNs), which mimic the spike-based coding mechanisms of biological neurons, present a promising alternative for edge devices due to their inherent temporal dynamics and energy efficiency. However, most existing Spiking neural network (SNN) models utilize fixed neuronal parameters, restricting their ability to adapt to diverse noise conditions commonly encountered in IoT applications. To address this issue, we propose a fractional-order plasticity approach integrated with spatio-temporal backpropagation algorithm. It enables simultaneous updating of synaptic weights and fractional-order parameters during training, allowing neurons to adjust their intrinsic dynamics. By incorporating fractional-order calculus, neurons can effectively integrate historical input information, enhancing their ability to filter out noise and extract relevant features effectively. Experimental results demonstrate that our proposed approach significantly improves classification performance, achieving accuracies of 99.67% on MNIST, 94.50% on Fashion-MNIST, and 93.43% on CIFAR-10, outperforming existing leading methods on all evaluated static datasets, and reaching a competitive accuracy of 97.25% on the DVS-Gesture dataset. The framework also exhibits superior resistance to noise compared to weight-training-only methods. This adaptive mechanism empowers neurons across different network layers to develop optimal fractional-order configurations, thereby enhancing networks’ temporal information processing ability and overall robustness in noisy environments. The proposed framework considerably advances the state of the art in efficient and robust spiking neural network learning for IoT applications.