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Zhao Z, Wang Y, Zou Q, Xu T, Tao F, Zhang J, Wang X, Shi CJR, Luo J, Xie Y. The spike gating flow: A hierarchical structure-based spiking neural network for online gesture recognition. Front Neurosci 2022; 16:923587. [PMID: 36408382 PMCID: PMC9667043 DOI: 10.3389/fnins.2022.923587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 10/03/2022] [Indexed: 01/25/2023] Open
Abstract
Action recognition is an exciting research avenue for artificial intelligence since it may be a game changer in emerging industrial fields such as robotic visions and automobiles. However, current deep learning (DL) faces major challenges for such applications because of the huge computational cost and inefficient learning. Hence, we developed a novel brain-inspired spiking neural network (SNN) based system titled spiking gating flow (SGF) for online action learning. The developed system consists of multiple SGF units which are assembled in a hierarchical manner. A single SGF unit contains three layers: a feature extraction layer, an event-driven layer, and a histogram-based training layer. To demonstrate the capability of the developed system, we employed a standard dynamic vision sensor (DVS) gesture classification as a benchmark. The results indicated that we can achieve 87.5% of accuracy which is comparable with DL, but at a smaller training/inference data number ratio of 1.5:1. Only a single training epoch is required during the learning process. Meanwhile, to the best of our knowledge, this is the highest accuracy among the non-backpropagation based SNNs. Finally, we conclude the few-shot learning (FSL) paradigm of the developed network: 1) a hierarchical structure-based network design involves prior human knowledge; 2) SNNs for content-based global dynamic feature detection.
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Affiliation(s)
- Zihao Zhao
- School of Microelectronics, Fudan University, Shanghai, China,Alibaba DAMO Academy, Shanghai, China
| | - Yanhong Wang
- School of Microelectronics, Fudan University, Shanghai, China,Alibaba DAMO Academy, Shanghai, China
| | - Qiaosha Zou
- School of Microelectronics, Fudan University, Shanghai, China
| | - Tie Xu
- Alibaba Group, Hangzhou, China
| | | | | | - Xiaoan Wang
- BrainUp Research Laboratory, Shanghai, China
| | - C.-J. Richard Shi
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Junwen Luo
- Alibaba DAMO Academy, Shanghai, China,BrainUp Research Laboratory, Shanghai, China,*Correspondence: Junwen Luo
| | - Yuan Xie
- Alibaba DAMO Academy, Shanghai, China
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Theta oscillations shift towards optimal frequency for cognitive control. Nat Hum Behav 2022; 6:1000-1013. [PMID: 35449299 DOI: 10.1038/s41562-022-01335-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 03/10/2022] [Indexed: 12/19/2022]
Abstract
Cognitive control allows to flexibly guide behaviour in a complex and ever-changing environment. It is supported by theta band (4-7 Hz) neural oscillations that coordinate distant neural populations. However, little is known about the precise neural mechanisms permitting such flexible control. Most research has focused on theta amplitude, showing that it increases when control is needed, but a second essential aspect of theta oscillations, their peak frequency, has mostly been overlooked. Here, using computational modelling and behavioural and electrophysiological recordings, in three independent datasets, we show that theta oscillations adaptively shift towards optimal frequency depending on task demands. We provide evidence that theta frequency balances reliable set-up of task representation and gating of task-relevant sensory and motor information and that this frequency shift predicts behavioural performance. Our study presents a mechanism supporting flexible control and calls for a reevaluation of the mechanistic role of theta oscillations in adaptive behaviour.
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