Adaptive dendritic plasticity in brain-inspired dynamic neural networks for enhanced multi-timescale feature...

Brain-inspired neural networks, drawing insights from biological neural systems, have emerged as a promising paradigm for temporal information processing due to their inherent neural dynamics. Spiking Neural Networks (SNNs) have gained extensive attention among existing brain-inspired neural models. However, they often struggle with capturing multi-timescale temporal features due to the static parameters across time steps and the low-precision spike activities. To this end, we propose a dynamic SNN with enhanced dendritic heterogeneity to enhance the multi-timescale feature extraction capability. We design a Leaky Integrate Modulation neuron model with Dendritic Heterogeneity (DH-LIM) that replaces traditional spike activities with a continuous modulation mechanism for preserving the nonlinear behaviors while enhancing the feature expression capability. We also introduce an Adaptive Dendritic Plasticity (ADP) mechanism that dynamically adjusts dendritic timing factors based on the frequency domain information of input signals, enabling the model to capture both rapid- and slow-changing temporal patterns. Extensive experiments on multiple datasets with rich temporal features demonstrate that our proposed method achieves excellent performance in processing complex temporal signals. These optimizations provide fresh solutions for optimizing the multi-timescale feature extraction capability of SNNs, showcasing its broad application potential.