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BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation (2207.08533v2)

Published 18 Jul 2022 in cs.NE

Abstract: Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological information processing in the brain at multiple scales. More importantly, SNNs serve as an appropriate level of abstraction to bring inspirations from brain and cognition to Artificial Intelligence. In this paper, we present the Brain-inspired Cognitive Intelligence Engine (BrainCog) for creating brain-inspired AI and brain simulation models. BrainCog incorporates different types of spiking neuron models, learning rules, brain areas, etc., as essential modules provided by the platform. Based on these easy-to-use modules, BrainCog supports various brain-inspired cognitive functions, including Perception and Learning, Decision Making, Knowledge Representation and Reasoning, Motor Control, and Social Cognition. These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions. For brain simulation, BrainCog realizes the function simulation of decision-making, working memory, the structure simulation of the Neural Circuit, and whole brain structure simulation of Mouse brain, Macaque brain, and Human brain. An AI engine named BORN is developed based on BrainCog, and it demonstrates how the components of BrainCog can be integrated and used to build AI models and applications. To enable the scientific quest to decode the nature of biological intelligence and create AI, BrainCog aims to provide essential and easy-to-use building blocks, and infrastructural support to develop brain-inspired spiking neural network based AI, and to simulate the cognitive brains at multiple scales. The online repository of BrainCog can be found at https://github.com/braincog-x.

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Authors (19)
  1. Yi Zeng (153 papers)
  2. Dongcheng Zhao (48 papers)
  3. Feifei Zhao (29 papers)
  4. Guobin Shen (37 papers)
  5. Yiting Dong (22 papers)
  6. Enmeng Lu (12 papers)
  7. Qian Zhang (308 papers)
  8. Yinqian Sun (14 papers)
  9. Qian Liang (19 papers)
  10. Yuxuan Zhao (32 papers)
  11. Zhuoya Zhao (5 papers)
  12. Hongjian Fang (5 papers)
  13. Yuwei Wang (60 papers)
  14. Yang Li (1140 papers)
  15. Xin Liu (820 papers)
  16. Chengcheng Du (1 paper)
  17. Qingqun Kong (12 papers)
  18. Zizhe Ruan (4 papers)
  19. Weida Bi (1 paper)
Citations (57)
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  1. Brain-inspired Cognitive Intelligence Engine (BrainCog) · GitHub