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The paper presents BrainGPT, a novel large language model (LLM) architecture inspired by spiking neural networks (SNNs) and neurobiological principles, aiming to improve computational efficiency and biological interpretability. Utilizing a dual-model structure and a multi-stage training strategy, BrainGPT achieves a 33.4% increase in energy efficiency and a 66.7% improvement in training convergence speed while maintaining 100% performance of the original ANN model. This work addresses the limitations of tra

Author

supersoul117

Language

EN