BrainCog: Brain-inspired Cognitive Engine for Brain Simulation and Brain-inspired Artificial Intelligence
BrainCog: Brain-inspired Cognitive Engine (BrainCog) is a brain-inspired neural network based platform for simulating the cognitive brains of different animal species at multiple scales and realizing brain-inspired Artificial Intelligence. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its evolution, and develop artificial brains for brain-inspired conscious living machines in future human-machine society.
Simulation of Hippocampus and Memory
Multi-Scale Whole Mouse Brain Point Neuron Simulation
Brain-inspired Reinforcement Learning
Brain-inspired Robot Self Consciousness Model

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Related Publications

  • Feifei Zhao, Yi Zeng, and Bo Xu. A Brain-inspired Decision-Making Spiking Neural Network and Its Application in Unmanned Aerial Vehicle. Frontiers in Neurorobotics, 2018.
  • Feifei Zhao, Tielin Zhang, Yi Zeng, Bo Xu. Towards a Brain-inspired Developmental Neural Network by Adaptive Synaptic Pruning. Proceedings of the 24th International Conference on Neural Information Processing (ICONIP 2017), 2017.
  • Qingqun Kong, Yi Zeng, Qiulei Dong. Biologically Inspired Deep Stereo Model. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP 2015), 3700-3704, Quebec City, Canada, 2015.


BrainCog Team

  • Participants: Tielin Zhang, Xin Liu, Qingqun Kong, Qian Zhang, Jun Bai, Qian Liang, Xinhe Zhang, Xuan Tang, Feifei Zhao, Yuwei Wang, Enmeng Lu, Yuxuan Zhao, Taoyi Yang, Mengting Shi, Dongcheng Zhao.


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