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Spiking neural networks (SNNs) have attracted extensive attentions inBrain-inspired Artificial Intelligence and computational neuroscience. They canbe used to simulate biological information processing in the brain at multiplescales. More importantly, SNNs serve as an appropriate level of abstraction tobring inspirations from brain and cognition to Artificial Intelligence. In thispaper, we present the Brain-inspired Cognitive Intelligence Engine (BrainCog)for creating brain-inspired AI and brain simulation models. BrainCogincorporates different types of spiking neuron models, learning rules, brainareas, etc., as essential modules provided by the platform. Based on theseeasy-to-use modules, BrainCog supports various brain-inspired cognitivefunctions, including Perception and Learning, Decision Making, KnowledgeRepresentation and Reasoning, Motor Control, and Social Cognition. Thesebrain-inspired AI models have been effectively validated on various supervised,unsupervised, and reinforcement learning tasks, and they can be used to enableAI models to be with multiple brain-inspired cognitive functions. For brainsimulation, BrainCog realizes the function simulation of decision-making,working memory, the structure simulation of the Neural Circuit, and whole brainstructure simulation of Mouse brain, Macaque brain, and Human brain. An AIengine named BORN is developed based on BrainCog, and it demonstrates how thecomponents of BrainCog can be integrated and used to build AI models andapplications. To enable the scientific quest to decode the nature of biologicalintelligence and create AI, BrainCog aims to provide essential and easy-to-usebuilding blocks, and infrastructural support to develop brain-inspired spikingneural network based AI, and to simulate the cognitive brains at multiplescales. The online repository of BrainCog can be found athttps://github.com/braincog-x.

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