Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks

ZHANG Tielin; LI Chengyu; WANG Gang; ZHANG Malu; YU Lei; XU Bo

doi:10.11999/JEIT221459

Volume 45 Issue 8

Aug. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(8): 2675-2688

ZHANG Tielin, LI Chengyu, WANG Gang, ZHANG Malu, YU Lei, XU Bo. Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2675-2688. doi: 10.11999/JEIT221459

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ZHANG Tielin, LI Chengyu, WANG Gang, ZHANG Malu, YU Lei, XU Bo. Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2675-2688. doi: 10.11999/JEIT221459

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Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks

doi: 10.11999/JEIT221459

ZHANG Tielin^{1
,
,},
LI Chengyu²,
WANG Gang³,
ZHANG Malu⁴,
YU Lei⁵,
XU Bo¹

1.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
3.
Center of Brain Sciences, Beijing Institute of Basic Medical Sciences, Beijing 100850, China
4.
University of Electronic Science and Technology of China, Chengdu 611731, China
5.
Wuhan University, Wuhan 430072, China

Funds: The National Key Research and Development Program of China (2020AAA0104305), The Strategic Priority Research Program of the Chinese Academy of Sciences (XDA27010000, XDB32070000), The Shanghai Municipal Science and Technology Major Project (2021SHZDZX), and The Youth Innovation Promotion Association CAS

Received Date: 2022-11-21
Rev Recd Date: 2023-05-08

Available Online: 2023-05-10

Publish Date: 2023-08-21

Abstract

Abstract

Spiking Neural Networks (SNN) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational efficiency. Herein, the historical development of SNN are analyzed to conclude that these two fields are intersecting and merging rapidly. After the successful application of Dynamic Vision Sensors (DVS) and Dynamic Audio Sensors (DAS), SNNs have found some proper paradigms, such as continuous visual signal tracking, automatic speech recognition, and reinforcement learning of continuous control, that have extensively supported their key features, including spiking encoding, neuronal heterogeneity, specific functional circuits, and multiscale plasticity. In comparison to these real-world paradigms, the brain contains a spiked version of the biology-world paradigm, which exhibits a similar level of complexity and is usually considered a mirror of the real world. Considering the projected rapid development of invasive and parallel Brain-computer Interface (BCI), as well as the new BCI-based paradigm, which includes online pattern recognition and stimulus control of biological spike trains, it is natural for SNNs to exhibit their key advantages of energy efficiency, robustness, and flexibility. The biological brain has inspired the present study of SNNs and effective SNN machine-learning algorithms, which can help enhance neuroscience discoveries in the brain by applying them to the new BCI paradigm. Such two-way interactions with positive feedback can accelerate brain science research and brain-inspired intelligence technology.
- Spiking Neural Network(SNN),
- Brain-inspired intelligence,
- Brain-Computer Interface(BCI),
- Experimental paradigm

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References(61)

References

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