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Wang Z, Tao P, Chen L. Brain-inspired chaotic spiking backpropagation. Natl Sci Rev 2024; 11:nwae037. [PMID: 38707198 PMCID: PMC11067972 DOI: 10.1093/nsr/nwae037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/19/2023] [Accepted: 01/17/2024] [Indexed: 05/07/2024] Open
Abstract
Spiking neural networks (SNNs) have superior energy efficiency due to their spiking signal transmission, which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation (CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets (e.g. DVS-CIFAR10 and DVS-Gesture) and large-scale static data sets (e.g. CIFAR100 and ImageNet) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics, consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.
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Affiliation(s)
- Zijian Wang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Peng Tao
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Luonan Chen
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China
- Pazhou Laboratory (Huangpu), Guangzhou 510555, China
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Ma G, Yan R, Tang H. Exploiting noise as a resource for computation and learning in spiking neural networks. PATTERNS (NEW YORK, N.Y.) 2023; 4:100831. [PMID: 37876899 PMCID: PMC10591140 DOI: 10.1016/j.patter.2023.100831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/06/2023] [Accepted: 08/07/2023] [Indexed: 10/26/2023]
Abstract
Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.
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Affiliation(s)
- Gehua Ma
- College of Computer Science and Technology, Zhejiang University, Hangzhou, PRC
| | - Rui Yan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, PRC
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, PRC
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, PRC
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Wu Z, Shen Y, Zhang J, Liang H, Zhao R, Li H, Xiong J, Zhang X, Chua Y. BIDL: a brain-inspired deep learning framework for spatiotemporal processing. Front Neurosci 2023; 17:1213720. [PMID: 37564366 PMCID: PMC10410154 DOI: 10.3389/fnins.2023.1213720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/22/2023] [Indexed: 08/12/2023] Open
Abstract
Brain-inspired deep spiking neural network (DSNN) which emulates the function of the biological brain provides an effective approach for event-stream spatiotemporal perception (STP), especially for dynamic vision sensor (DVS) signals. However, there is a lack of generalized learning frameworks that can handle various spatiotemporal modalities beyond event-stream, such as video clips and 3D imaging data. To provide a unified design flow for generalized spatiotemporal processing (STP) and to investigate the capability of lightweight STP processing via brain-inspired neural dynamics, this study introduces a training platform called brain-inspired deep learning (BIDL). This framework constructs deep neural networks, which leverage neural dynamics for processing temporal information and ensures high-accuracy spatial processing via artificial neural network layers. We conducted experiments involving various types of data, including video information processing, DVS information processing, 3D medical imaging classification, and natural language processing. These experiments demonstrate the efficiency of the proposed method. Moreover, as a research framework for researchers in the fields of neuroscience and machine learning, BIDL facilitates the exploration of different neural models and enables global-local co-learning. For easily fitting to neuromorphic chips and GPUs, the framework incorporates several optimizations, including iteration representation, state-aware computational graph, and built-in neural functions. This study presents a user-friendly and efficient DSNN builder for lightweight STP applications and has the potential to drive future advancements in bio-inspired research.
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Affiliation(s)
- Zhenzhi Wu
- Lynxi Technologies, Co. Ltd., Beijing, China
| | - Yangshu Shen
- Lynxi Technologies, Co. Ltd., Beijing, China
- Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China
| | - Jing Zhang
- Lynxi Technologies, Co. Ltd., Beijing, China
| | - Huaju Liang
- Neuromorphic Computing Laboratory, China Nanhu Academy of Electronics and Information Technology (CNAEIT), Jiaxing, Zhejiang, China
| | | | - Han Li
- Lynxi Technologies, Co. Ltd., Beijing, China
| | - Jianping Xiong
- Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China
| | - Xiyu Zhang
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yansong Chua
- Neuromorphic Computing Laboratory, China Nanhu Academy of Electronics and Information Technology (CNAEIT), Jiaxing, Zhejiang, China
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Chen Y, Liu H, Shi K, Zhang M, Qu H. Spiking neural network with working memory can integrate and rectify spatiotemporal features. Front Neurosci 2023; 17:1167134. [PMID: 37389360 PMCID: PMC10300445 DOI: 10.3389/fnins.2023.1167134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/26/2023] [Indexed: 07/01/2023] Open
Abstract
In the real world, information is often correlated with each other in the time domain. Whether it can effectively make a decision according to the global information is the key indicator of information processing ability. Due to the discrete characteristics of spike trains and unique temporal dynamics, spiking neural networks (SNNs) show great potential in applications in ultra-low-power platforms and various temporal-related real-life tasks. However, the current SNNs can only focus on the information a short time before the current moment, its sensitivity in the time domain is limited. This problem affects the processing ability of SNN in different kinds of data, including static data and time-variant data, and reduces the application scenarios and scalability of SNN. In this work, we analyze the impact of such information loss and then integrate SNN with working memory inspired by recent neuroscience research. Specifically, we propose Spiking Neural Networks with Working Memory (SNNWM) to handle input spike trains segment by segment. On the one hand, this model can effectively increase SNN's ability to obtain global information. On the other hand, it can effectively reduce the information redundancy between adjacent time steps. Then, we provide simple methods to implement the proposed network architecture from the perspectives of biological plausibility and neuromorphic hardware friendly. Finally, we test the proposed method on static and sequential data sets, and the experimental results show that the proposed model can better process the whole spike train, and achieve state-of-the-art results in short time steps. This work investigates the contribution of introducing biologically inspired mechanisms, e.g., working memory, and multiple delayed synapses to SNNs, and provides a new perspective to design future SNNs.
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Yu C, Gu Z, Li D, Wang G, Wang A, Li E. STSC-SNN: Spatio-Temporal Synaptic Connection with temporal convolution and attention for spiking neural networks. Front Neurosci 2022; 16:1079357. [PMID: 36620452 PMCID: PMC9817103 DOI: 10.3389/fnins.2022.1079357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/08/2022] [Indexed: 12/25/2022] Open
Abstract
Spiking neural networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing capability, low power consumption, and high biological plausibility. The potential to efficiently extract spatio-temporal features makes it suitable for processing event streams. However, existing synaptic structures in SNNs are almost full-connections or spatial 2D convolution, neither of which can extract temporal dependencies adequately. In this work, we take inspiration from biological synapses and propose a Spatio-Temporal Synaptic Connection SNN (STSC-SNN) model to enhance the spatio-temporal receptive fields of synaptic connections, thereby establishing temporal dependencies across layers. Specifically, we incorporate temporal convolution and attention mechanisms to implement synaptic filtering and gating functions. We show that endowing synaptic models with temporal dependencies can improve the performance of SNNs on classification tasks. In addition, we investigate the impact of performance via varied spatial-temporal receptive fields and reevaluate the temporal modules in SNNs. Our approach is tested on neuromorphic datasets, including DVS128 Gesture (gesture recognition), N-MNIST, CIFAR10-DVS (image classification), and SHD (speech digit recognition). The results show that the proposed model outperforms the state-of-the-art accuracy on nearly all datasets.
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Affiliation(s)
- Chengting Yu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China,Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
| | - Zheming Gu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
| | - Da Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
| | - Gaoang Wang
- Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
| | - Aili Wang
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China,Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China,*Correspondence: Aili Wang ✉
| | - Erping Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China,Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
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Wu Z, Zhang Z, Gao H, Qin J, Zhao R, Zhao G, Li G. Modeling learnable electrical synapse for high precision spatio-temporal recognition. Neural Netw 2022; 149:184-194. [DOI: 10.1016/j.neunet.2022.02.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 11/30/2021] [Accepted: 02/06/2022] [Indexed: 10/19/2022]
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Lin Y, Ding W, Qiang S, Deng L, Li G. ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks. Front Neurosci 2021; 15:726582. [PMID: 34899154 PMCID: PMC8655353 DOI: 10.3389/fnins.2021.726582] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 10/22/2021] [Indexed: 11/16/2022] Open
Abstract
With event-driven algorithms, especially spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream dataset is urgently needed. However, it is well-known that creating an ES-dataset is a time-consuming and costly task with neuromorphic cameras like dynamic vision sensors (DVS). In this work, we propose a fast and effective algorithm termed Omnidirectional Discrete Gradient (ODG) to convert the popular computer vision dataset ILSVRC2012 into its event-stream (ES) version, generating about 1,300,000 frame-based images into ES-samples in 1,000 categories. In this way, we propose an ES-dataset called ES-ImageNet, which is dozens of times larger than other neuromorphic classification datasets at present and completely generated by the software. The ODG algorithm implements image motion to generate local value changes with discrete gradient information in different directions, providing a low-cost and high-speed method for converting frame-based images into event streams, along with Edge-Integral to reconstruct the high-quality images from event streams. Furthermore, we analyze the statistics of ES-ImageNet in multiple ways, and a performance benchmark of the dataset is also provided using both famous deep neural network algorithms and spiking neural network algorithms. We believe that this work shall provide a new large-scale benchmark dataset for SNNs and neuromorphic vision.
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Affiliation(s)
- Yihan Lin
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China
| | - Wei Ding
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China
| | - Shaohua Qiang
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China
| | - Lei Deng
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China
| | - Guoqi Li
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China
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