前额叶皮层启发的Transformer模型应用及其进展

潘雨辰; 贾克斌; 张铁林

doi:10.16383/j.aas.c240538

前额叶皮层启发的Transformer模型应用及其进展

doi: 10.16383/j.aas.c240538 cstr: 32138.14.j.aas.c240538

潘雨辰^{1, 2,},
贾克斌^2,,
张铁林^{1, 3, 4,}

1.
中国科学院脑科学与智能技术卓越创新中心上海 200031
2.
北京工业大学北京 100124
3.
中国科学院大学北京 100049
4.
中国科学院自动化研究所北京100190

基金项目: 北京市科技新星(20230484369), 上海市市级科技重大专项(2021SHZDZX), 中科院青促会, 多模态人工智能系统全国重点实验室开放课题基金等资助.

详细信息

作者简介:
潘雨辰：北京工业大学信息科学技术学院硕士研究生, 中科院脑科学与智能技术卓越创新中心联合培养学生. 2019年获得北京工业大学工学学士学位. 主要研究方向为类脑模型算法.E-mail: 18201335023@sina.cn

贾克斌：北京工业大学信息科学技术学院教授, 博士. 主要研究方向为图像/视频处理技术与生物医学信息处理技术.E-mail: kebinj@bjut.edu.cn

张铁林：中国科学院脑智卓越中心, 脑认知与类脑智能国重实验室研究员, 课题组长, 兼职中科院自动化所复杂系统认知与决策实验室. 主要从事类脑脉冲神经网络算法, 类脑芯片及AI for Neuroscience研究. 本文通信作者.E-mail: zhangtielin@ion.ac.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2024-12-13
- 录用日期: 2024-12-13
- 网络出版日期: 2025-03-03

The Application and Progress of Prefrontal Cortex-inspired Transformer Model

PAN Yu-Chen^{1, 2
,},
JIA Ke-Bin^2
,,
ZHANG Tie-lin^{1, 3, 4
,}

1.
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031
2.
Beijing University of Technology, Beijing 100124
3.
University of Chinese Academy of Sciences, Beijing 100049
4.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Funds: Supported by Beijing Nova Program (20230484369), Shanghai Municipal Science and Technology Major Project (2021SHZDZX), Youth Innovation Promotion Association of Chinese Academy of Sciences, and Open Projects Program of State Key Laboratory of Multimodal Artificial Intelligence Systems.

More Information

Author Bio:
PAN Yu-Chen　Master’s student in the School of Information Science and Technology, Beijing University of Technology, co-supervised by the Center for Excellence of Brain Science and Intelligence Technology, Chinese Academy of Sciences. He received his bachelor degree in engineering from Beijing University of Technology in 2019. His research interest covers brain-inspired algorithms

JIA Ke-Bin　Professor at the School of Information Science and Technology, Beijing University of Technology. His research interests are focused on Image/Video coding and processing, bioinformation processing

ZHANG Tie-Lin　Principal indicator at the Key Laboratory of Brain Cognition and Brain-inspired Intelligence, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences (CAS), also a co-PI at Key Laboratory of Complex System for Recognition and Decision-making, Institute of Automation, CAS. He mainly engaged in the research of brain-inspired spiking neural network algorithms, brain-inspired chips, and AI for Neuroscience. Corresponding author of this paper

摘要

摘要: 本文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, 本文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.
- 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 sensory, memory 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.
- Biological structures /
- brain-inspired intelligence /
- prefrontal cortex /
- Transformer /
- world model

HTML全文

图 1 PFC启发Transformer结构

Fig. 1 PFC-inspired Transformer structure

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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

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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

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图 4 PFC与Transformer注意力相关模型架构^{[14, 23]}

Fig. 4 PFC and Transformer attention-related model architecture^{[14, 23]}

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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

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图 6 标量相对位置编码 (SRPE) 原理

Fig. 6 Principle of Scalar Relative Position Encoding(SRPE)

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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

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图 8 不同模态下EEG分类的多尺度卷积Transformer模型^[63]

Fig. 8 Multi-scale convolutional Transformer model for EEG classification in different modalities^[63]

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图 9 PFC多感觉融合与多模态Transformer逻辑结构图

Fig. 9 PFC multisensory fusion with multimodal Transformer logic structure diagram

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