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.
翻译：Spik 神经网络(SNNS)吸引了大脑启发的人工智能和计算神经科学的广泛关注。 它们可以用来模拟大脑生物信息处理的多重规模。 更重要的是, SNNS可以作为适当的抽象层面, 从大脑和认知到人工智能。 在本文中, 我们展示了大脑启发的认知智能引擎( Braincoog), 用于创建大脑启发的AI和大脑模拟模型。 大脑C将神经模型的不同类型, 学习规则, 大脑区域等, 作为平台提供的基本模块。 基于这些容易使用的模块, 脑信息网络支持各种大脑启发的认知功能, 包括感知和学习、 决策、 知识说明和解释、 运动控制、 社会认知。 这些大脑启发的人工智能引擎(Braincoog) 可以在各种监督、 不受监控、 强化的学习任务中被有效验证。 它们可以用来让AI模型在多种大脑感动的认知功能中 。 在大脑模拟、 大脑智能智能的大脑智能智能数据库和大脑智能数据库中, 将大脑的智能智能智能智能智能数据库(ILOCO) 系统(OICOIDID) 系统(OID) 和大脑(OID) 系统(OIL) (OD) (OIL) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (O) (I) (I) (I) (I) (I) (I) (I) (I) (O) (O) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (I) (O) (O) (O)
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