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Feng L, Zhao D, Zeng Y. Spiking generative adversarial network with attention scoring decoding. Neural Netw 2024; 178:106423. [PMID: 38906053 DOI: 10.1016/j.neunet.2024.106423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/22/2024] [Accepted: 05/31/2024] [Indexed: 06/23/2024]
Abstract
Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural networks, offer a closer approximation to brain-like processing due to their rich spatiotemporal dynamics. However, generative models based on spiking neural networks are not well studied. Particularly, previous works on generative adversarial networks based on spiking neural networks are conducted on simple datasets and do not perform well. In this work, we pioneer constructing a spiking generative adversarial network capable of handling complex images and having higher performance. Our first task is to identify the problems of out-of-domain inconsistency and temporal inconsistency inherent in spiking generative adversarial networks. We address these issues by incorporating the Earth-Mover distance and an attention-based weighted decoding method, significantly enhancing the performance of our algorithm across several datasets. Experimental results reveal that our approach outperforms existing methods on the MNIST, FashionMNIST, CIFAR10, and CelebA. In addition to our examination of static datasets, this study marks our inaugural investigation into event-based data, through which we achieved noteworthy results. Moreover, compared with hybrid spiking generative adversarial networks, where the discriminator is an artificial analog neural network, our methodology demonstrates closer alignment with the information processing patterns observed in the mouse. Our code can be found at https://github.com/Brain-Cog-Lab/sgad.
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Affiliation(s)
- Linghao Feng
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Future Technology, University of Chinese Academy of Sciences, China.
| | - Dongcheng Zhao
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Center for Long-term Artificial Intelligence, China.
| | - Yi Zeng
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Center for Long-term Artificial Intelligence, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, CAS, China; School of Future Technology, University of Chinese Academy of Sciences, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China.
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Yuan M, Zhang C, Wang Z, Liu H, Pan G, Tang H. Trainable Spiking-YOLO for low-latency and high-performance object detection. Neural Netw 2024; 172:106092. [PMID: 38211460 DOI: 10.1016/j.neunet.2023.106092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 12/06/2023] [Accepted: 12/26/2023] [Indexed: 01/13/2024]
Abstract
Spiking neural networks (SNNs) are considered an attractive option for edge-side applications due to their sparse, asynchronous and event-driven characteristics. However, the application of SNNs to object detection tasks faces challenges in achieving good detection accuracy and high detection speed. To overcome the aforementioned challenges, we propose an end-to-end Trainable Spiking-YOLO (Tr-Spiking-YOLO) for low-latency and high-performance object detection. We evaluate our model on not only frame-based PASCAL VOC dataset but also event-based GEN1 Automotive Detection dataset, and investigate the impacts of different decoding methods on detection performance. The experimental results show that our model achieves competitive/better performance in terms of accuracy, latency and energy consumption compared to similar artificial neural network (ANN) and conversion-based SNN object detection model. Furthermore, when deployed on an edge device, our model achieves a processing speed of approximately from 14 to 39 FPS while maintaining a desirable mean Average Precision (mAP), which is capable of real-time detection on resource-constrained platforms.
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Affiliation(s)
- Mengwen Yuan
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311100, China
| | - Chengjun Zhang
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311100, China
| | - Ziming Wang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Huixiang Liu
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311100, China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310027, China; MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou 310027, China
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310027, China; MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou 310027, China.
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Pan W, Zhao F, Han B, Dong Y, Zeng Y. Emergence of brain-inspired small-world spiking neural network through neuroevolution. iScience 2024; 27:108845. [PMID: 38327781 PMCID: PMC10847652 DOI: 10.1016/j.isci.2024.108845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/23/2023] [Accepted: 01/03/2024] [Indexed: 02/09/2024] Open
Abstract
Studies suggest that the brain's high efficiency and low energy consumption may be closely related to its small-world topology and critical dynamics. However, existing efforts on the performance-oriented structural evolution of spiking neural networks (SNNs) are time-consuming and ignore the core structural properties of the brain. Here, we introduce a multi-objective Evolutionary Liquid State Machine (ELSM), which blends the small-world coefficient and criticality to evolve models and guide the emergence of brain-inspired, efficient structures. Experiments reveal ELSM's consistent and comparable performance, achieving 97.23% on NMNIST and outperforming LSM models on MNIST and Fashion-MNIST with 98.12% and 88.81% accuracies, respectively. Further analysis shows its versatility and spontaneous evolution of topologies such as hub nodes, short paths, long-tailed degree distributions, and numerous communities. This study evolves recurrent spiking neural networks into brain-inspired energy-efficient structures, showcasing versatility in multiple tasks and potential for adaptive general artificial intelligence.
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Affiliation(s)
- Wenxuan Pan
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Feifei Zhao
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Bing Han
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yiting Dong
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yi Zeng
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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Shen G, Zhao D, Zeng Y. Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks. Neural Netw 2024; 170:190-201. [PMID: 37989040 DOI: 10.1016/j.neunet.2023.10.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 08/22/2023] [Accepted: 10/31/2023] [Indexed: 11/23/2023]
Abstract
Inspired by the information transmission process in the brain, Spiking Neural Networks (SNNs) have gained considerable attention due to their event-driven nature. However, as the network structure grows complex, managing the spiking behavior within the network becomes challenging. Networks with excessively dense or sparse spikes fail to transmit sufficient information, inhibiting SNNs from exhibiting superior performance. Current SNNs linearly sum presynaptic information in postsynaptic neurons, overlooking the adaptive adjustment effect of dendrites on information processing. In this study, we introduce the Dendritic Spatial Gating Module (DSGM), which scales and translates the input, reducing the loss incurred when transforming the continuous membrane potential into discrete spikes. Simultaneously, by implementing the Dendritic Temporal Adjust Module (DTAM), dendrites assign different importance to inputs of different time steps, facilitating the establishment of the temporal dependency of spiking neurons and effectively integrating multi-step time information. The fusion of these two modules results in a more balanced spike representation within the network, significantly enhancing the neural network's performance. This approach has achieved state-of-the-art performance on static image datasets, including CIFAR10 and CIFAR100, as well as event datasets like DVS-CIFAR10, DVS-Gesture, and N-Caltech101. It also demonstrates competitive performance compared to the current state-of-the-art on the ImageNet dataset.
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Affiliation(s)
- Guobin Shen
- Brain-Inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Dongcheng Zhao
- Brain-Inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China
| | - Yi Zeng
- Brain-Inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences (CAS), Shanghai, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
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Zeng Y, Zhao D, Zhao F, Shen G, Dong Y, Lu E, Zhang Q, Sun Y, Liang Q, Zhao Y, Zhao Z, Fang H, Wang Y, Li Y, Liu X, Du C, Kong Q, Ruan Z, Bi W. BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation. PATTERNS (NEW YORK, N.Y.) 2023; 4:100789. [PMID: 37602224 PMCID: PMC10435966 DOI: 10.1016/j.patter.2023.100789] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/06/2023] [Accepted: 06/05/2023] [Indexed: 08/22/2023]
Abstract
Spiking neural networks (SNNs) serve as a promising computational framework for integrating insights from the brain into artificial intelligence (AI). Existing software infrastructures based on SNNs exclusively support brain simulation or brain-inspired AI, but not both simultaneously. To decode the nature of biological intelligence and create AI, we present the brain-inspired cognitive intelligence engine (BrainCog). This SNN-based platform provides essential infrastructure support for developing brain-inspired AI and brain simulation. BrainCog integrates different biological neurons, encoding strategies, learning rules, brain areas, and hardware-software co-design as essential components. Leveraging these user-friendly components, BrainCog incorporates various cognitive functions, including perception and learning, decision-making, knowledge representation and reasoning, motor control, social cognition, and brain structure and function simulations across multiple scales. BORN is an AI engine developed by BrainCog, showcasing seamless integration of BrainCog's components and cognitive functions to build advanced AI models and applications.
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Affiliation(s)
- Yi Zeng
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Dongcheng Zhao
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Feifei Zhao
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Guobin Shen
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yiting Dong
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Enmeng Lu
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Qian Zhang
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yinqian Sun
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Qian Liang
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yuxuan Zhao
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhuoya Zhao
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Hongjian Fang
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yuwei Wang
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yang Li
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Xin Liu
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Chengcheng Du
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Qingqun Kong
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Zizhe Ruan
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Weida Bi
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Shirsavar SR, Vahabie AH, Dehaqani MRA. Models Developed for Spiking Neural Networks. MethodsX 2023; 10:102157. [PMID: 37077894 PMCID: PMC10106956 DOI: 10.1016/j.mex.2023.102157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023] Open
Abstract
Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared to the brain. Spiking neural networks (SNNs) have been around for a long time, and they have been investigated to understand the dynamics of the brain. However, their application in real-world and complicated machine learning tasks were limited. Recently, they have shown great potential in solving such tasks. Due to their energy efficiency and temporal dynamics there are many promises in their future development. In this work, we reviewed the structures and performances of SNNs on image classification tasks. The comparisons illustrate that these networks show great capabilities for more complicated problems. Furthermore, the simple learning rules developed for SNNs, such as STDP and R-STDP, can be a potential alternative to replace the backpropagation algorithm used in DNNs.•Different building blocks of spiking neural networks are explained in this work.•Developed models for SNNs are introduced based on their characteristics and building blocks.
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BPLC + NOSO: backpropagation of errors based on latency code with neurons that only spike once at most. COMPLEX INTELL SYST 2023. [DOI: 10.1007/s40747-023-00983-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
AbstractFor mathematical completeness, we propose an error-backpropagation algorithm based on latency code (BPLC) with spiking neurons conforming to the spike–response model but allowed to spike once at most (NOSOs). BPLC is based on gradients derived without approximation unlike previous temporal code-based error-backpropagation algorithms. The latency code uses the spiking latency (period from the first input spike to spiking) as a measure of neuronal activity. To support the latency code, we introduce a minimum-latency pooling layer that passes the spike of the minimum latency only for a given patch. We also introduce a symmetric dual threshold for spiking (i) to avoid the dead neuron issue and (ii) to confine a potential distribution to the range between the symmetric thresholds. Given that the number of spikes (rather than timesteps) is the major cause of inference delay for digital neuromorphic hardware, NOSONets trained using BPLC likely reduce inference delay significantly. To identify the feasibility of BPLC + NOSO, we trained CNN-based NOSONets on Fashion-MNIST and CIFAR-10. The classification accuracy on CIFAR-10 exceeds the state-of-the-art result from an SNN of the same depth and width by approximately 2%. Additionally, the number of spikes for inference is significantly reduced (by approximately one order of magnitude), highlighting a significant reduction in inference delay.
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Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsules. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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