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摘要: 聚焦于生物结构与类脑智能的交叉研究方向, 探讨前额叶皮层的结构及其认知功能对人工智能领域Transformer模型的启发. 前额叶皮层在认知控制和决策制定中扮演着关键角色. 首先介绍前额叶皮层的注意力机制、生物编码、多感觉融合等相关生物研究进展, 然后探讨这些生物机制如何启发新型的类脑Transformer架构, 重点提升其在自注意力、位置编码、多模态整合等方面的生物合理性与计算高效性. 最后, 总结前额叶皮层启发的类脑新模型, 在支持多类型神经网络组合、多领域应用、世界模型构建等方面的发展与潜力, 为生物和人工智能两大领域之间交叉融合构建桥梁.
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关键词:
- 生物结构 /
- 类脑智能 /
- 前额叶皮层 /
- Transformer /
- 世界模型
Abstract: This article focuses on the integration of biological network connectomes and brain-inspired intelligence, exploring how the structure and cognitive functions of the prefrontal cortex can inspire transformer models in the field of artificial intelligence. The prefrontal cortex plays a critical role in cognitive control and decision-making. Firstly, the article introduces some advancements of biological research related to the prefrontal cortex＇s attention mechanisms, biological encoding, and multisensory integration. Then, it discusses how these biological mechanisms can inspire novel brain-inspired transformer architectures, with a focus on enhancing their biological plausibility and computational efficiency in self-attention, positional encoding, and multimodal integration. Finally, it summarizes the development and potential of new brain-inspired models influenced by the prefrontal cortex, highlighting their support for the integration of various neural network types, multi-domain applications, and the construction of world models in reinforcement learning, thereby building a bridge between the fields of biology and artificial intelligence.-
Key words:
- Biological structures /
- brain-inspired intelligence /
- prefrontal cortex /
- transformer /
- world model
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图 2 PFC生物功能启发生物功能模型与神经网络架构
Fig. 2 PFC biofunctional-inspired biofunctional model with neural network architecture
图 3 PFC与类脑智能相互促进、共同进步
Fig. 3 PFC and brain-like intelligence promote each other and progress together
图 4 PFC与Transformer注意力相关模型架构
Fig. 4 PFC and transformer attention-related model architecture
图 5 PFC与Transformer以注意力机制为媒介相互启发
Fig. 5 PFC and transformer inspire each other through the medium of the attention mechanism
图 7 PFC生物编码过程启发Transformer位置编码
Fig. 7 PFC biological coding process inspired transformer position coding
图 8 不同模态下EEG分类的多尺度卷积Transformer模型
Fig. 8 Multi-scale convolutional transformer model for EEG classification in different modalities
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