• BrainCog登上Cell旗下Patterns期刊2023年8月第4卷第8期的封面并正式发表。 BrainCog经过了10年的发展历程，并准备好迈向通用人工智能向善的未来！
• 智脉论文公开了！ 关于智脉10年发展的总结和更新，以及智脉的未来展望， 请点击这里了解。
• 关于智脉的愿景和进展，在2022人工智能基础与应用国际会议中作主题为 "构建符合伦理的类脑人工智能并与其共生" 的主题演讲。
• 主题为 "Brain-inspired Artificial Intelligence-Towards Brain-inspired Conscious Living Becomings" 的演讲反映了智脉项目的进展
• 中国中央电视台中文国际频道在新闻节目人工智能在中国系列中报道了智脉以及相关成果。
• CASIA Brain Simulation System : 查看我们在基于 BrainCog 的多尺度脑模拟方面所做的努力。

• N-Omniglot: A Large-scale Neuromorphic Dataset for Spatio-Temporal Sparse Few-shot Learning.
• Bullying10K: A Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition

• 整合不同精细程度的神经元计算模型、丰富的类脑学习与可塑性法则、不同类型的神经网络连接模式与编码方式；
• 基于脉冲神经网络实现感知和学习、决策、运动控制、知识表征和推理、社会认知等五大类面向人工智能应用的智能模型，可以映射到28个关键功能性脑区；
• 类脑学习机制：结合局部和全局可塑性实现了更具生物合理性的类脑可塑性前馈反馈融合模型。基于BrainCog实现的具有生物合理性的时间空间调节算法， 理论上与相同结构的人工神经网络相比，只用约3%的能量就能达到有相当竞争力的分类精度。基于BrainCog构建的ANN-SNN转换模型充分结合了反向传播算法与脉冲神经网络的优势，使SNN能以其他算法1/10和1/50的仿真时间几乎无损地完成图像分类和目标检测任务。由BrainCog支持的基于STDP算法的无监督脉冲神经网络模型，借由具有生物可解释性的优化算法和多种自适应机制，达到基于STDP无监督算法中的目前最好性能，同时实现了在极端少量样本下相同模型结构下优于ANN 4%-5%的性能。
• 社会认知脉冲神经网络模型赋能人形机器人通过镜像测试自我感知实验，使智能体帮助其他智能体避免潜在风险，表现出初步类道德行为。
• 对不同类型哺乳动物脑进行多尺度模拟，构建了包含不同类型点神经元的鼠脑模拟器、猴脑模拟器(12.1亿神经元、1.3万亿突触，1/5猴脑规模)、人脑模拟器(规模达8.6亿脉冲神经元，2.5 万亿突触，1/100人脑规模)。
• 基于智脉BrainCog的创生(BORN)人工智能引擎展示基于情感识别的乐曲创作与演奏能力。

类脑认知智能引擎(BrainCog)“智脉”以多尺度生物可塑性原理为基础，支持全脉冲神经网络建模，具备脑启发的人工智能模型以及脑功能和结构模拟能力。BrainCog为类脑人工智能和计算神经科学的研究者提供了一套完整的、系统化的接口组件。

• 不同精细程度的神经元计算模型：不仅包含了常用的积分-放电，漏电-积分-放电脉冲神经元，还包含了计算神经科学以及脑模拟领域常用的Hodgkin-Huxley，Izhikevich等神经元模型；
• 丰富的类脑学习与可塑性法则：如生物可塑性的赫布法则，脉冲时序依赖可塑性法则，短时程突触可塑性法则，以及基于代理梯度的反向传播算法和基于预训练人工神经网络的模型转换算法；
• 不同的神经网络连接模式：如侧抑制、局部连接、前馈连接、反馈连接等；
• 不同的编码方式：如频率编码，时序编码，群体编码等。
• 丰富的功能性脑区模型：基于上述组件构建了如前额叶皮层(PFC)、海马区、基底节、丘脑、视觉通路，运动皮层等若干个功能性脑区的脉冲神经网络计算模型。

“智脉(BrainCog)”允许研究者通过调用并连接基础模块，自由地设计所需要的网络以及相应的功能。在类脑人工智能方面，BrainCog协同多个脑区形成不同的神经环路，初步实现了五大类认知功能：感知和学习、决策、运动控制、知识表征和推理、社会认知。同时BrainCog实现了对部分脑认知功能的模拟，以及从多尺度对不同类型的生物脑结构机制进行模拟。目前，类脑脉冲神经网络领域缺乏一个公开、公平的平台， 在同一环境下评估算法的性能。BrainCog中集成了丰富的高性能， 易修改的SNN模型， 并对其进行了基准测试。用户可以在同一环境中评估自己的算法， 并与其他方法进行比较。可以使用BrainCog提供的组件轻松实现自己的算法， 并与其他最先进的方法进行公平比较。

“智脉(BrainCog)”旨在为实现专用、通用的类脑人工智能模型提供基础支持。目前，它提供的认知功能组件可以分为五类：感知和学习、决策、运动控制、知识表征和推理、社会认知。这些组件共同形成了与哺乳动物大脑中28个脑区相对应的神经环路。同时在有监督、无监督、强化学习和各种脑启发的认知任务上得到有效验证。

BrainCog支持多种有监督和无监督的训练法则，如基于突触可塑性的STDP，基于代理梯度的反向传播算法，和基于ANN到SNN的转换算法。同时在小样本以及噪声环境下取得突出的适应性。该引擎还提供了仿照人类进行概念学习的多感觉融合框架。

• 基于BrainCog完成的前馈连接与反馈连接结合的SNN，通过反馈连接引入全局的误差信号，提升了基于局部优化法则的脉冲神经网络在深层网络结构上的性能与稳定性，达到了与基于反向传播算法训练相抗衡的性能。
• 基于BrainCog中实现的具有生物合理性的时间空间调节算法，能够训练深层的SNN模型，并在MNIST，CIFAR10，ImageNet等图像分类任务上，以及DVS-CIFAR10，DVS-Gesture等事件分类任务上， 与其他的SNN相比， 有先进性能的同时，展现出明显的低能耗。
• 基于BrainCog构建的ANN-SNN转换模型充分结合了反向传播算法与脉冲神经网络的优势，使SNN能以更少的能耗和更高的效率在图像分类、目标检测和语义分割等任务中完成同ANN相媲美的性能。
• 使用BrainCog构建的基于STDP算法的无监督脉冲神经网络模型，借由具有生物可解释性的优化算法和多种自适应机制，极大提升了网络性能与效率。实现了在少量样本下相同模型结构下优于ANN的性能。

BrainCog 提供了多脑区协同的决策脉冲神经网络以及深度强化学习脉冲神经网络。

• 基于BrainCog 实现的具有生物合理性的类脑决策模型在Flappy bird游戏上实现了类人的学习能力，并具备支持无人机在线决策的能力，能够实现类果蝇的线性和非线性决策以及反转学习。
• 基于BrainCog构建的Spiking-DQN模型实现了深度脉冲神经网路和强化学习的结合，在Atari游戏上的得分超过传统DQN模型。

BrainCog初步实现了利用脉冲神经网络控制人形机器人运动。

• 受大脑运动控制机制的启发，团队构建了包括前运动皮层（PMC），辅助运动区（SMA）、基底神经节和小脑功能的多脑区协同机器人运动脉冲神经网络模型，并应用于人形机器人弹奏钢琴。

BrainCog通过融合多神经可塑性和群体编码机制进行知识表征和推理。

• 利用类脑的音乐记忆和风格创作模型实现了音符序列的知识表征和记忆，可以生成具有不同风格的乐曲，序列生成SNN实现了对符号序列的表征与记忆，并可根据不同的规则对符号序列进行重构。
• 常识表征SNN将常识知识库编码进SNN，可据此网络完成概念化知识生成后续认知任务。因果推理SNN将因果图编码为脉冲神经网络，实现了演绎推理、溯因推理等认知任务。

BrainCog实现了一种具有生物合理性的脑启发的社会认知脉冲神经网络模型。

• 该模型使得智能体初步地具备了理解自我和他人的能力，能够通过多机器人镜像自我识别测试并且降低了智能体交互过程中的安全风险。
• 前者是社会认知中自我感知的经典实验，后者则是社会认知中思维揣测实验的变体。

BrainCog可以支持不同尺度的脑结构与认知功能模拟，从而为在局部和全脑尺度计算验证科学猜想和科学解释提供强有力的支持。

BrainCog实现了果蝇线性、非线性决策和PFC工作记忆功能的模拟。

• 在果蝇线性和非线性决策模拟中，BrainCog验证了非线性抉择环路在两难抉择下的赢者通吃行为，得到了与果蝇生物学实验一致的结论，相应算法还应用到了无人机平台使其获得类脑决策能力。
• 用BrainCog实现的PFC网络，在不改变网络结构的情况下，使用人类神经元代替啮齿类动物神经元可以显著提高图像输出的准确性和完整性，这证明了生物脑结构的演化不仅仅体现在神经元和脑区尺度连接结构的演化，还体现在神经元这个基本计算单元的信息处理能力方面的优化

BrainCog可以模拟不同规模的生物脑结构，从微环路到皮质柱到全脑结构模拟。从解剖到成像的多尺度连接数据使鼠脑，猴脑和人脑建模更具有生物合理性。

类脑人工智能
• Ilana Witten, Daniel L.K. Yamins, Claudia Clopath, Matthias Bethge, Yi Zeng, Ann Kennedy, Abeba Birhane, Doris Tsao, Been Kim, Ila Fiete. Future views on neuroscience and AI. Cell, 2024.
• Bing Han, Feifei Zhao, Yi Zeng, Guobin Shen. Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
• Bing Han, Feifei Zhao, Wenxuan Pan, Yi Zeng. Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks. Information Sciences, 2024.
• Wenxuan Pan, Feifei Zhao, Zhuoya Zhao, Yi Zeng. Brain-inspired Evolutionary Architectures for Spiking Neural Networks. IEEE Transactions on Artificial Intelligence, 2024.
• Yang Li, Xiang He, Qingqun Kong, Yi Zeng. Spiking Neural Networks with Consistent Mapping Relations Allow High-Accuracy Inference. Information Sciences, 2024.
• Linghao Feng, Dongcheng Zhao, Yi Zeng. Spiking generative adversarial network with attention scoring decoding. Neural Networks, 2024.
• Yiting Dong, Xiang He, Guobin Shen, Dongcheng Zhao, Yang Li, Yi Zeng. EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision. arXiv preprint arXiv: 2405.18880 (2024).
• Zhuoya Zhao, Feifei Zhao, Shiwen Wang, Yinqian Sun, Yi Zeng. A Brain-inspired Theory of Collective Mind Model for Efficient Social Cooperation. IEEE Transactions on Artificial Intelligence, 2024.
• Xiang He, Yang Li, Dongcheng Zhao, Qingqun Kong, Yi Zeng. MSAT: biologically inspired multistage adaptive threshold for conversion of spiking neural networks. Neural Computing and Applications, 2024.
• Xiang He, Dongcheng Zhao, Yang Li, Guobin Shen, Qingqun Kong, Yi Zeng. An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain. Proceedings of the AAAI Conference on Artificial Intelligence, 2024.
• Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng. Firefly v2: Advancing hardware support for high-performance spiking neural network with a spatiotemporal fpga accelerator. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024
• Yao Liang, Yuwei Wang, Yang Li, Yi Zeng. Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning. arXiv preprint arXiv: 2403.07440 (2024).
• Yiting Dong, Dongcheng Zhao, Yi Zeng. Temporal knowledge sharing enable spiking neural network learning from past and future. IEEE Transactions on Artificial Intelligence, 2024
• Yi Zeng, Feifei Zhao, Yuxuan Zhao, Dongcheng Zhao, Enmeng Lu, Qian Zhang, Yuwei Wang, Hui Feng, Zhuoya Zhao, Jihang Wang, Qingqun Kong, Yinqian Sun, Yang Li, Guobin Shen, Bing Han, Yiting Dong, Wenxuan Pan, Xiang He, Aorigele Bao, Jin Wang. Brain-inspired and Self-based Artificial Intelligence. arXiv preprint arXiv: 2402.18784 (2024).
• Yang Li, Yinqian Sun, Xiang He, Yiting Dong, Dongcheng Zhao, Yi Zeng. Parallel Spiking Unit for Efficient Training of Spiking Neural Networks. IJCNN, 2024.
• Guobin Shen, Dongcheng Zhao, Yi Zeng. Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks. Neural Networks, 2024.
• Guobin Shen, Dongcheng Zhao, Tenglong Li, Jindong Li, Yi Zeng. Are Conventional SNNs Really Efficient? A Perspective from Network Quantization.. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.
• Jindong Li, Tenglong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng. Revealing Untapped DSP Optimization Potentials for FPGA-Based Systolic Matrix Engines. 34th International Conference on Field-Programmable Logic and Applications, 2024.
• Sicheng Shen, Dongcheng Zhao, Guobin Shen, Yi Zeng. TIM: An Efficient Temporal Interaction Module for Spiking Transformer. IJCAI, 2024.
• Yiting Dong, Yang Li, Dongcheng Zhao, Guobin Shen, Yi Zeng. Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition. Advances in Neural Information Processing Systems, 2024, 36.
• Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang, Yi Zeng. Neuro-Vision to Language: Image Reconstruction and Language enabled Interaction via Brain Recordings. arXiv preprint arXiv: 2404.19438 (2024).
• Wenxuan Pan, Feifei Zhao, Bing Han, Yiting Dong, Yi Zeng. Emergence of brain-inspired small-world spiking neural network through neuroevolution. iScience, Cell Press, 2024.
• Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Jindong Li, Kang Sun, Yi Zeng. Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling. Arxiv, 2023.
• Yuxuan Zhao, Enmeng Lu, Yi Zeng. Brain-inspired bodily self-perception model for robot rubber hand illusion. Patterns, Cell Press, 2023.
• Guobin Shen, Dongcheng Zhao, Yiting Dong, Yi Zeng. Brain-inspired neural circuit evolution for spiking neural networks. Proceedings of the National Academy of Sciences (PNAS), 2023.
• Wenxuan Pan, Feifei Zhao, Yi Zeng, Bing Han. Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks. Scientific Reports, Nature Press, 2023.
• Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, Zhuoya Zhao, Hongjian Fang, Yuwei Wang, Yang Li, Xin Liu, Chengcheng Du, Qingqun Kong, Zizhe Ruan, Weida Bi. BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation. Patterns, Cell Press, 2023.
• Yiting Dong, Dongcheng Zhao, Yang Li, Yi Zeng. An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections. Neural Networks, 165:799-808, 2023.
• Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng. FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks With Efficient DSP and Memory Optimization. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2023.
• Zhuoya Zhao, Feifei Zhao, Yuxuan Zhao, Yi Zeng, Yinqian Sun. A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition, Patterns, Cell Press, 2023.
• Guobin Shen, Dongcheng Zhao, Yi Zeng. EventMix: An efficient data augmentation strategy for event-based learning, Information Sciences, Volume 644, 119170, October 2023.
• Jinyu Fan, Yi Zeng. Challenging Deep Learning Models with Image Distortion Based on the Abutting Grating Illusion, Patterns, Cell Press, 2023.
• Yang Li, Yiting Dong, Dongcheng Zhao, Yi Zeng. N-Omniglot: a Large-scale Neuromorphic Dataset for Spatio-temporal Sparse Few-shot Learning. Scientific Data, 9(746), Nature Publishing Group, 2022.
• Feifei Zhao, Yi Zeng, Bing Han, Hongjian Fang, Zhuoya Zhao. Nature-inspired Self-organizing Collision Avoidance for Drone Swarm Based on Reward-modulated Spiking Neural Network, Patterns, Cell Press, 2022.
• Hui Feng, Yi Zeng. A Brain-Inspired Robot Pain Model Based on a Spiking Neural Network, Frontiers in Neurorobotics, 2022.
• Yinqian Sun, Yi Zeng, and Yang Li. Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization, Frontiers in Neuroscience, 2022.
• Yuxuan Zhao, Yi Zeng. A Brain-inspired Intention Prediction Model and Its Applications to Humanoid Robot, Frontiers in Neuroscience, 16:1009237, 2022.
• Yang Li, Dongcheng Zhao, and Yi Zeng. BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons, Frontiers in Neuroscience, 2022.
• Hui Feng, Yi Zeng, and Enmeng Lu. Brain-Inspired Affective Empathy Computational Model and Its Application on Altruistic Rescue Task, Frontiers in Computational Neuroscience, 2022.
• Dongcheng Zhao, Yi Zeng, Yang Li, Jihang Wang, Qian Zhang. Spiking Capsnet: A Spiking Neural Network With a Biologically Plausible Routing Rule Between Capsules, Information Sciences, 2022.
• Dongcheng Zhao, Yi Zeng, Yang Li. BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons, Neural Networks, Volume 154, 68-77, 2022.
• Guobin Shen, Dongcheng Zhao, Yi Zeng. Backpropagation with Biologically Plausible Spatiotemporal Adjustment for Training Deep Spiking Neural Networks. Patterns, Cell Press, 2022.
• Zhuoya Zhao, Enmeng Lu, Feifei Zhao, Yi Zeng, and Yuxuan Zhao. A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents. Frontiers in Neuroscience, 2022.
• Yang Li, Yi Zeng. Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes. Proceedings of the 31st International Joint Conferences on Artificial Intelligence (IJCAI-22), 2022.
• Yuwei Wang, Yi Zeng. Multisensory Concept Learning Framework Based on Spiking Neural Networks. Frontiers in Systems Neuroscience, 16:845177, 2022.
• Yuwei Wang, Yi Zeng. Statistical Analysis of Multisensory and Text-Derived Representations on Concept Learning. Frontiers in Computational Neuroscience, 16:861265, 2022.
• Hongjian Fang, Yi Zeng. A Brain-Inspired Causal Reasoning Model Based on Spiking Neural Networks. 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021.
• Yinqian Sun, Yi Zeng, Tielin Zhang. Quantum Superposition Inspired Spiking Neural Network. iScience, 24(8), 102880, Cell Press, 2021.
• Yuxuan Zhao, Yi Zeng, Guang Qiao. Brain-Inspired Classical Conditioning Model. iScience, 24(1), 101980, Cell Press, 2021.
• Yang Li, Yiting Dong, Dongcheng Zhao, Yi Zeng. N-Omniglot: a Large-scale Neuromorphic Dataset for Spatio-Temporal Sparse Few-shot Learning. Arxiv:2112.13230, 2021.
• Feifei Zhao, Yi Zeng, Jun Bai. Toward a Brain-Inspired Developmental Neural Network Based on Dendritic Spine Dynamics. Neural Computation, MIT Press, 2021.
• Qian Liang, Yi Zeng. Stylistic Composition of Melodies based on a Brain-inspired Spiking Neural Network. Frontiers in Systems Neuroscience, 15:639484, 2021.
• Feifei Zhao, Yi Zeng. Dynamically Optimizing Network Structure Based on Synaptic Pruning in the Brain. Frontiers in Systems Neuroscience, 15:620558, 2021.
• Hongjian Fang, Yi Zeng, Feifei Zhao. Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP. Frontiers in Computational Neuroscience, 15:612041, 2021.
• Dongcheng Zhao, Yi Zeng, Tielin Zhang, Mengting Shi, Feifei Zhao. GLSNN: A Multi-layer Spiking Neural Network based on Global Feedback Alignment and Local STDP Plasticity. Frontiers in Computational Neuroscience, 2020.
• Yi Zeng, Yuxuan Zhao, Tielin Zhang, Dongcheng Zhao, Feifei Zhao, and Enmeng Lu. A Brain-Inspired Model of Theory of Mind. Frontiers in Neurorobotics, 2020.
• Qian Liang, Yi Zeng, Bo Xu. Temporal-Sequential Learning with a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory. Frontiers in Computational Neuroscience, 14:51, 2020.
• Feifei Zhao, Qingqun Kong, Yi Zeng, Bo Xu. A Brain-inspired Visual Fear Responses Model for UAV Emergent Obstacle Dodging. IEEE Transactions on Cognitive and Developmental Systems, 12(1): 124-132, 2020.
• Yuwei Wang, Yi Zeng, Jianbo Tang and Bo Xu. Biological Neuron Coding Inspired Binary Word Embeddings. Cognitive Computation, Springer, 2019.
• Dongcheng Zhao, Yi Zeng. Dynamic Fusion of Convolutional Features based on Spatial and Temporal Attention for Visual Tracking. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019). Budapest, Hungary, July 14-19, 2019.
• 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.
• Yi Zeng, Guixiang Wang, Bo Xu. A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle. IEEE Transactions on Cognitive and Developmental Systems, 10(2): 290-303, 2018.
• Yi Zeng, Yuxuan Zhao, Jun Bai, Bo Xu. Towards Robot Self-consciousness (II): Brain-inspired Robot Bodily Self Model for Self-Recognition. Cognitive Computation, 10(2): 307-320, Springer, 2018.
• Yi Zeng, Tielin Zhang, Bo Xu. Improving Multi-layer Spiking Neural Networks by Incorporating Brain-inspired Rules. SCIENCE CHINA Information Sciences, 60(5): 052201, 2017.
• 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.
• Guang Qiao, Hongyue Du, Yi Zeng. A Quaternionic Rate-based Synaptic Learning Rule Derived from Spike-Timing Dependent Plasticity. Lecture Notes in Computer Science, 10261, 457-465, 2017.
• Xin Liu, Yi Zeng, Bo Xu. Parallel Brain Simulator: A Multi-scale and Parallel Brain-inspired Neural Network Modeling and Simulation Platform. Cognitive Computation, Volume 8, Issue 5, 967-981, Springer, 2016.
• Yi Zeng, Yuxuan Zhao, Jun Bai. Towards Robot Self-Consciousness (I): Brain-inspired Robot Mirror Neuron System Model and Its Application in Mirror Self-Recognition. Lecture Notes in Computer Science, 10023, 11-21, 2016.
• Guixiang Wang, Yi Zeng, Bo Xu. A Spiking Neural Network Based Autonomous Reinforcement Learning Model and Its Application in Decision Making. Lecture Notes in Computer Science, 10023, 125-137, 2016.
• Yi Zeng, Chenglin Liu, Tieniu Tan. Retrospect and Outlook of Brain-inspired Intelligence Research [in Chinese]. The Chinese Journal of Computers, Volume 39, Issue 1, 212-222, 2016.
• Bo Xu, Chenglin Liu, Yi Zeng. Research Status and Developments of Brain-inspired Intelligence [in Chinese]. Bulletin of Chinese Academy of Sciences, Volume 31, Issue 7, 793-802, 2016.
• Yi Zeng, Feifei Zhao, Guixiang Wang, Lingyu Zhang, Bo Xu. Brain-inspired Obstacle Detection based on the Biological Visual Pathway. Proceedings of the 2016 International Conference on Brain Informatics and Health, Lecture Notes in Artificial Intelligence 9919, Omaha, Nebraska, USA, 2016.
• Yuwei Wang, Yi Zeng, Bo Xu. SHTM: A Neocortex-inspired Algorithm for One-shot Text Generation. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 898-903, IEEE Press, Budapest, Hungary, October 9-12, 2016.
• Xiaomei kang, Qingqun Kong, Yi Zeng, Bo Xu. A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System. Frontiers in Computational Neuroscience, 2018.
• Feifei Zhao, Yi Zeng, Guixiang Wang, Jun Bai, Bo Xu. A Brain-inspired Decision Making Model based on Top-down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations. Cognitive Computation, 10(2): 296-306, Springer, 2018.
• 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.
脑模拟
• Qian Zhang, Han Lu, jihang Wang, Taoyi Yang, Weida Bi, Yi Zeng, Buwei Yu. Hierarchical rhythmic propagation of corticothalamic interactions for consciousness: A computational study. Computers in Biology and Medicine, 2023.
• Qian Zhang, Yi Zeng, Tielin Zhang, Taoyi Yang. Comparison Between Human and Rodent Neurons for Persistent Activity Performance: A Biologically Plausible Computational Investigation. Frontiers in System Neuroscience, 2021.
• Feifei Zhao, Yi Zeng, Aike Guo, Haifeng Su, Bo Xu. A Neural Algorithm for Drosophila Linear and Nonlinear Decision-making. Scientific Reports, 10: 18660, Nature Publishing Group, 2020.
• Qian Zhang, Yi Zeng, Taoyi Yang. Computational Investigation of Contributions from Different Subtypes of Interneurons in Prefrontal Cortex for Information Maintenance. Scientific Reports, 10: 4671, Nature Publishing Group, 2020.
• Qingqun Kong, Jiuqi Han, Yi Zeng, Bo Xu. Efficient Coding Matters in the Organization of the Early Visual System. Neural Networks, Elsevier, 2018.
• Yi Zeng, Tielin Zhang, Bo Xu. Neural Pathway Prediction based on Multi-neuron Spike Train Data. The Twenty-Third Annual Computational Neuroscience Meeting (CNS 2014), Québec City, Canada, July 26-31, 2014, BMC Neuroscience, 15(Suppl 1): P6, 2014.
• Jiuqi Han, Qingqun Kong, Yi Zeng and Hongwei Hao. HEVS: A Hierarchical Computational Model for Early Stages of the Visual System. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN 2015), 1-8, Killarney, Ireland, July 12-17, 2015.
• Yi Zeng, Weida Bi, Yun Wang, Xuan Tang and Bo Xu. Automatic Recovery of Z-Jumps for Neuronal Morphology Reconstruction. Frontiers in Neuroinformatics. Conference Abstract: The 7th Neuroinformatics Congress (Neuroinformatics 2014), Leiden, the Netherlands, August 25-27, 2014.
• Yi Zeng, Weida Bi, Xuan Tang, and Bo Xu. Neuronal Morphology Modeling Based on Microscopy Reconstruction Data in the Public Repositories. Proceedings of the 2014 International Conference on Brain Informatics and Health (BIH 2014), Lecture Notes in Artificial Intelligence 8609, 1-11, Springer, Warsaw, Poland, August 11-14, 2014.

• 课题组组长: 曾毅
• 团队成员: 赵东城, 赵菲菲, 申国斌, 董一廷, 鲁恩萌, 张倩, 孙胤乾, 梁倩, 赵宇轩, 赵卓雅, 方宏坚, 王寓巍, 李杨, 刘鑫, 杜骋骋, 孔庆群, 阮子喆, 毕韦达, 皇甫存青, 范津宇, 唐璇, 杨洮乙, 冯慧, 冯令昊, 韩冰, 潘文萱, 蔡孟玥, 何翔, 李金东, 李唯依, 梁尧, 王纪航, 王骞浩