  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.
