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29th International Joint Conference on Artificial intelligence, IJCAI 2020

作者： Cheng, Xiang Hao, Yunzhe Xu, Jiaming Xu, Bo Beijing China Research Center for Brain-inspired Intelligence CASIA University of Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology CAS China

ISBN: (纸本)9780999241165

Spiking Neural Network (SNN) is considered more biologically plausible and energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm has been utilized for training SNN, which allows SNN to go deeper and achieve higher performance. However, most existing SNN models for object recognition are mainly convolutional structures or fully-connected structures, which only have inter-layer connections, but no intra-layer connections. Inspired by Lateral Interactions in neuroscience, we propose a high-performance and noise-robust Spiking Neural Network (dubbed LISNN). Based on the convolutional SNN, we model the lateral interactions between spatially adjacent neurons and integrate it into the spiking neuron membrane potential formula, then build a multi-layer SNN on a popular deep learning framework, i. e., PyTorch. We utilize the pseudo-derivative method to solve the non-differentiable problem when applying backpropagation to train LISNN and test LISNN on multiple standard datasets. Experimental results demonstrate that the proposed model can achieve competitive or better performance compared to current state-of-the-art spiking neural networks on MNIST, Fashion-MNIST, and N-MNIST datasets. Besides, thanks to lateral interactions, our model processes stronger noise-robustness than other SNN. Our work brings a biologically plausible mechanism into SNN, hoping that it can help us understand the visual information processing in the brain. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Convolution

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Multifunctional biomimetic tactile system via a stick-slip sensing strategy for human–machine interactions

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npj Flexible Electronics 2022年第1期6卷 498-506页

作者： Yue Li Manjun Zhao Yadong Yan Luanxi He Yingyi Wang Zuoping Xiong Shuqi Wang Yuanyuan Bai Fuqin Sun Qifeng Lu Yu Wang Tie Li Ting Zhang School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefeiAnhuiPR China i-Lab Nano-X Vacuum Interconnected WorkstationKey Laboratory of Multifunction Nanomaterials and Smart SystemsSuzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)SuzhouPR China School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengduPR China School of Biological Science and Medical Engineering Beihang UniversityBeijingPR China Department of Health and Environmental Sciences School of ChipsXJTLU Entrepreneur College(Taicang)Xi’an Jiaotong Liverpool UniversitySuzhouJiangsuPR China Gusu Laboratory of Materials Science SuzhouPR China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceShanghaiPR China

A tactile sensor system enables natural interaction between humans and machines;this interaction is crucial for dexterous robotic hands,interactive entertainment,and other smart ***,the lack of sliding friction detection significantly limits the accuracy and scope of interactions due to the absence of sophisticated information,such as slippage,material and roughness of held ***,inspired by the stick-slip phenomena in the sliding process,we have developed a multifunctional biomimetic tactile system based on the stick-slip sensing strategy,which is a universal method to detect slippage and estimate the surface properties of objects by *** system consists of a flexible fingertip-inspired tactile sensor,a read-out circuit and a machinelearning *** on the stick-slip sensing strategy,our system was endowed with high recognition rates for slippage detection(100.0%),material classification(93.3%)and roughness discrimination(92.8%).Moreover,robotic hand manipulation,interactive games and object classification are demonstrated with this multifunctional system for comprehensive and promising human-machine interactions.

关键词： interactive system roughness

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Self-Stretchable Christmas-Tree-Shaped Ultraflexible Neural Probes 22

Self-Stretchable Christmas-Tree-Shaped Ultraflexible Neural ...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Tian, Ye Zhou, Cunkai Zhang, Kuikui Yang, Huiran Chen, Zhaohan Zhou, Zhitao Wei, Xiaoling Tao, Tiger H. Sun, Liuyang 2020 X-Lab Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences Shanghai China Schoool Of Graduate Study University Of Chinese Academy Of Sciences Beijing China College Of Electronics And Information Engineering Shanghai University Of Electric Power Shanghai China Institute Of Intelligent Manufacturing Nanjing Tech University Nanjing China State Key Laboratory Of Transducer Technology Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences Shanghai China College Of Life Sciences Shanghai Normal University Shanghai China Center Of Materials Science And Optoelectronics Engineering University Of Chinese Academy Of Sciences Beijing China Center For Excellence In Brain Science And Intelligence Technology Chinese Academy Of Sciences Shanghai China Jiangxi Nanchang China Guangdong Institute Of Intelligence Science And Technology Zhuhai Guangdong Hengqin China Tianqiao And Chrissy Chen Institute For Translational Research Shanghai China

ISBN: (纸本)9784886864352

We devised a novel Christmas-tree structure for flexible neural probes, which features recording sites on side-branches that can be folded along the shank of the probe by temporary encapsulation before implantation. This design reduces the size of the implant and minimizes the surgical footprint. Upon implantation, the temporary encapsulation dissolves in vivo, leading to self-stretching of the branches, and an expansion of the lateral detecting range of the probe. We conducted in vitro experiment with our probe in 0.6% agar that mimics the brain tissue environment. The results demonstrate that the neural probe we have developed exhibits self-stretching ability and provides an increased lateral recording range without causing additional damage to the brain tissue. © 2023 IEEJ.

关键词： brain computer interface Flexible probe Self-stretchable

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Cloning of a gene-edited macaque monkey by somatic cell nuclear transfer

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National science Review 2019年第1期6卷 101-108页

作者： Zhen Liu Yijun Cai Zhaodi Liao Yuting Xu Yan Wang Zhanyang Wang Xiaoyu Jiang Yuzhuo Li Yong Lu Yanhong Nie Xiaotong Zhang Chunyang Li Xinyan Bian Mu-ming Poo Hung-Chun Chang Qiang Sun Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence Technology CAS Key Laboratory of Primate Neurobiology State Key Laboratory of Neuroscience Chinese Academy of Sciences Shanghai Research Center for Brain Science and Brain-inspired Technology

Cloning of macaque monkeys by somatic cell nucleus transfer(SCNT) allows the generation of monkeys with uniform genetic backgrounds that are useful for the development of non-human primate models of human diseases. Here, we report the feasibility of this approach by SCNT of fibroblasts from a macaque monkey(Macaca fascicularis), in which a core circadian transcription factor BMAL1 was knocked out by clustered regularly interspaced short palindromic repeat/cas9 gene editing(see accompanying paper).Out of 325 SCNT embryos transferred into 65 surrogate monkeys, we cloned five macaque monkeys with BMAL1 mutations in both alleles without mosaicism, with nuclear genes identical to that of the fibroblast donor monkey. Further peripheral blood m RNA analysis confirmed the complete absence of the wild-type BMAL1 transcript. This study demonstrates that the SCNT approach could be used to generate cloned monkeys from fibroblasts of a young adult monkeys and paves the way for the development of macaque monkey disease models with uniform genetic backgrounds.

关键词： macaque monkey cloning somatic cell nuclear transfer non-human primate models CRISPR/cas9 gene editing circadian rhythm disorders

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Continual learning for fake audio detection

arXiv

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arXiv 2021年

作者： Ma, Haoxin Yi, Jiangyan Tao, Jianhua Bai, Ye Tian, Zhengkun Wang, Chenglong NLPR Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China CAS Center for Excellence in Brain Science and Intelligence Technology China

Fake audio attack becomes a major threat to the speaker verification system. Although current detection approaches have achieved promising results on dataset-specific scenarios, they encounter difficulties on unseen spoofing data. Fine-tuning and retraining from scratch have been applied to incorporate new data. However, fine-tuning leads to performance degradation on previous data. Retraining takes a lot of time and computation resources. Besides, previous data are unavailable due to privacy in some situations. To solve the above problems, this paper proposes detecting fake without forgetting, a continual-learning-based method, to make the model learn new spoofing attacks incrementally. A knowledge distillation loss is introduced to loss function to preserve the memory of original model. Supposing the distribution of genuine voice is consistent among different scenarios, an extra embedding similarity loss is used as another constraint to further do a positive sample alignment. Experiments are conducted on the ASVspoof2019 dataset. The results show that our proposed method outperforms fine-tuning by the relative reduction of average equal error rate up to 81.62%. Copyright © 2021, The Authors. All rights reserved.

关键词： Distillation

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New light on the horizon of Alzheimer's disease

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National science Review 2020年第5期7卷 831-831页

作者： Mu-ming Poo CAS Center for Excellence for Brain Science and Intelligence Technology NSR

The beginning of the year 2020 is marked by a groundbreaking event in drug development in China. A first-in-class drug for Alzheimer’s disease (AD), GV-971, received conditional approval by the China Food and Drug Administration for the Chinese market. China now has at least 10 million patients with diagnosed AD, afflicting almost one-third of the population

关键词： New light on the horizon of Alzheimer’s disease Alzheimer

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Orthogonal-Coding Based Feature Generation for Transductive Open-Set Recognition Via Dual-Space Consistent Sampling

SSRN

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SSRN 2022年

作者： Sun, Jiayin Dong, Qiulei National Laboratory of Pattern Recognition CASIA Beijing100190 China School of Artificial Intelligence UCAS Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing100190 China

Open-set recognition (OSR) aims to simultaneously detect unknown-class samples and classify known-class samples. Most of the existing OSR methods are inductive methods, which generally suffer from the domain shift problem that the learned model from the known-class domain might be unsuitable for the unknown-class domain. Addressing this problem, inspired by the success of transductive learning for alleviating the domain shift problem in many other visual tasks, we propose an Iterative Transductive OSR framework, called IT-OSR, which implements three explored modules iteratively, including a reliability sampling module, a feature generation module, and a baseline update module. Specifically, at each iteration, a dual-space consistent sampling approach is presented in the explored reliability sampling module for selecting some relatively more reliable ones from the test samples according to their pseudo labels assigned by a baseline method, which could be an arbitrary inductive OSR method. Then, a conditional dual-adversarial generative network under an orthogonal coding condition is designed in the feature generation module to generate discriminative sample features of both known and unknown classes according to the selected test samples with their pseudo labels. Finally, the baseline method is updated for sample re-prediction in the baseline update module by jointly utilizing the generated features, the selected test samples with pseudo labels, and the training samples. Extensive experimental results on both the standard-dataset and the cross-dataset settings demonstrate that the derived transductive methods, by introducing two typical inductive OSR methods into the proposed IT-OSR framework, achieve better performances than 19 state-of-the-art methods in most cases. © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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Learning Latent Causal Factors from the Intricate Sensor Feedback of Contact-Rich Robotic Assembly Tasks

SSRN

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SSRN 2023年

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To alleviate the low learning efficiency from the problematic observations, we propose introducing the concept of latent causal factors that cause the sensor observations and truly matter in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut assembly environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing intricate industrial challenges%. © 2023, The Authors. All rights reserved.

关键词： Robotic assembly

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Decoupling Pronunciation and Language for End-to-End Code-Switching Automatic Speech Recognition

Decoupling Pronunciation and Language for End-to-End Code-Sw...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shuai Zhang Jiangyan Yi Zhengkun Tian Ye Bai Jianhua Tao Zhengqi wen School of Artificial Intelligence University of Chinese Academy of Sciences China NLPR Institute of Automation Chinese Academy of Sciences China CAS Center for Excellence in Brain Science and Intelligence Technology China

Despite the recent significant advances witnessed in end-to-end (E2E) ASR system for code-switching, hunger for audio-text paired data limits the further improvement of the models' performance. In this paper, we propose a decoupled transformer model to use mono-lingual paired data and unpaired text data to alleviate the problem of code-switching data shortage. The model is decoupled into two parts: audio-to-phoneme (A2P) network and phoneme-to-text (P2T) network. The A2P network can learn acoustic pattern scenarios using large-scale monolingual paired data. Meanwhile, it generates multiple phoneme sequence candidates for single audio data in real time during the training process. Then the generated phoneme-text paired data is used to train the P2T network. This network can be pre-trained with large amounts of external unpaired text data. By using monolingual data and unpaired text data, the decoupled transformer model reduces the high dependency on code-switching paired training data of E2E model to a certain extent. Finally, the two networks are optimized jointly through attention fusion. We evaluate the proposed method on the public Mandarin-English code-switching dataset. Compared with our transformer baseline, the proposed method achieves 18.14% relative mix error rate reduction.

关键词： Training Speech coding Error analysis Conferences Training data Signal processing Data models

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EventMix: An Efficient Augmentation Strategy for Event-Based Data

arXiv

引用

arXiv 2022年

作者： Shen, Guobin Zhao, Dongcheng Zeng, Yi Research Center for Brain-Inspired Intelligence CASIA China Center for Excellence in Brain Science and Intelligence Technology CAS China National Laboratory of Pattern Recognition CASIA China School of Future Technology University of Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

High-quality and challenging event stream datasets play an important role in the design of an efficient event-driven mechanism that mimics the brain. Although event cameras can provide high dynamic range and low-energy event stream data, the scale is smaller and more difficult to obtain than traditional frame-based data, which restricts the development of neuromorphic computing. Data augmentation can improve the quantity and quality of the original data by processing more representations from the original data. This paper proposes an efficient data augmentation strategy for event stream data: EventMix. We carefully design the mixing of different event streams by Gaussian Mixture Model to generate random 3D masks and achieve arbitrary shape mixing of event streams in the spatio-temporal dimension. By computing the relative distances of event streams, we propose a more reasonable way to assign labels to the mixed samples. The experimental results on multiple neuromorphic datasets have shown that our strategy can improve its performance on neuromorphic datasets both for ANNs and SNNs, and we have achieved state-of-the-art performance on DVS-CIFAR10, N-Caltech101, N-CARS, and DVS-Gesture datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Mixing

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