Title: Adaptive Neuronal Dynamics Drive Accurate and Efficient Spiking Neural Networks
Primary area: applications to neuroscience & cognitive science
Keywords: spiking neural networks, adaptive neuronal dynamics

Abstract: Neuronal dynamics are fundamental to the temporal processing capabilities of spiking neural networks (SNNs), directly influencing their responsiveness and integration of dynamic input features. While most existing advances in SNNs focus on architectural optimizations or learning algorithms, the potential of adaptive neuronal dynamics remains underexplored. In this work, we pioneer a new direction by proposing a novel neuron model that achieves adaptive neuronal dynamics through a combined mechanism of learnable and dynamically modulated membrane time constant. This design enables each neuron to regulate its integration behavior in response to stimulus intensity and firing history, achieving short-term adaptive responses while maintaining long-term stability. Importantly, our approach maintains computational simplicity throughout the implementation. Experiments on multiple benchmarks, including physiological signals (DEAP) and neuromorphic vision datasets (DVS-Gesture, CIFAR10-DVS), demonstrate superior performance in both classification accuracy and robustness. In particular, our model achieves state-of-the-art accuracy of 90.11\% and 92.57\% on the DEAP valence and arousal classification tasks, representing improvements of +9.08\% and +9.97\% over the baseline. Furthermore, the proposed neuron demonstrates strong generalization across various network architectures, confirming its generality as a fundamental component for SNN-based systems. Additional energy consumption analysis confirms the high efficiency of our method. These results establish adaptive neuronal dynamics as a new pathway for developing effective and efficient  neuromorphic systems.

TL;DR: This work enhances the capability and efficiency of spiking neural networks through the implementation of adaptive neuronal dynamics.