BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation

被引：22

作者：

Zeng, Yi ^{[1
,2
,3
,4
,5
]}

Zhao, Dongcheng ^{[1
]}

Zhao, Feifei ^{[1
]}

Shen, Guobin ^{[1
,5
]}

Dong, Yiting ^{[1
,5
]}

Lu, Enmeng ^{[1
]}

Zhang, Qian ^{[1
,4
]}

Sun, Yinqian ^{[1
,5
]}

Liang, Qian ^{[1
]}

Zhao, Yuxuan ^{[1
]}

Zhao, Zhuoya ^{[1
,5
]}

Fang, Hongjian ^{[1
,5
]}

Wang, Yuwei ^{[1
]}

Li, Yang ^{[1
,4
]}

Liu, Xin ^{[1
]}

Du, Chengcheng ^{[1
,5
]}

Kong, Qingqun ^{[1
,5
]}

Ruan, Zizhe ^{[1
]}

Bi, Weida ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Brain Inspired Cognit Intelligence Lab, Beijing 100190, Peoples R China

[2] Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China

[3] Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China

[4] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China

[5] Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China

来源：

PATTERNS | 2023年 / 4卷 / 08期

关键词：

MODALITY EXCLUSIVITY NORMS; LARGE-SCALE MODEL; DECISION-MAKING; BASAL GANGLIA; BINOCULAR INTERACTION; PREFRONTAL CORTEX; FIRE MODEL; NEURONS; ARCHITECTURE; ANATOMY;

D O I：

10.1016/j.patter.2023.100789

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Spiking neural networks (SNNs) serve as a promising computational framework for integrating insights from the brain into artificial intelligence (AI). Existing software infrastructures based on SNNs exclusively support brain simulation or brain-inspired AI, but not both simultaneously. To decode the nature of biological intelligence and create AI, we present the brain-inspired cognitive intelligence engine (BrainCog). This SNN-based platform provides essential infrastructure support for developing brain-inspired AI and brain simulation. BrainCog integrates different biological neurons, encoding strategies, learning rules, brain areas, and hardware-software co-design as essential components. Leveraging these user-friendly components, BrainCog incorporates various cognitive functions, including perception and learning, decision-making, knowledge representation and reasoning, motor control, social cognition, and brain structure and function simulations across multiple scales. BORN is an AI engine developed by BrainCog, showcasing seamless integration of BrainCog's components and cognitive functions to build advanced AI models and applications.

引用

页数：19

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