BioRWKV: RWKV Large Model Inspired by Biological Brain Inspired

BioRWKV aims to enhance the biological plausibility and efficiency of artificial intelligence systems by developing a novel large-scale Spiking Neural Network (SNN) architecture inspired by principles from biological neural systems. This research integrates Leaky Integrate-and-Fire (LIF) neuron activation layers, event-driven temporal encoding mechanisms, and Spike-Timing-Dependent Plasticity (STDP) learning rules into the model, effectively simulating synaptic plasticity in the brain. Additionally, the extended BioViRWKV model incorporates patch embedding and positional encoding to improve visual data processing capabilities. The results demonstrate that BioRWKV significantly enhances the processing of temporal sequence data and offers new perspectives for multimodal learning tasks. These advancements mark a significant step toward developing more efficient and biologically inspired AI systems, with potential applications in intelligent sensing, robotic vision, and biomedical signal processing.