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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bao, Cong Zheng, Weihao Zhang, Qin Yang, Songyu Yao, Zhijun Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China

ISBN: (纸本)9798350337488

Functional magnetic resonance imaging (fMRI) plays a vital role in brain science as it measures and maps brain activity through the analysis of blood flow changes, offering valuable insights into cognitive functions and neural processes. However, due to the intricacy and dynamism of brain activity, conventional approaches failed to accurately predict the blood oxygen level-dependent (BOLD) time series in fMRI data. To tackle this issue, we proposed an end-to-end adversarial U-network (AUN) to verify the predictability of existing BOLD signals in primary cortex (i.e., primary visual, primary motor and primary sensory) and higher cortex(i.e., dorsolateral prefrontal and posterior cingulate). The model combined the U-network architecture and adversarial strategy to ensure that the predicted results capture both the intricate nonlinear details and the overall distribution characteristics. We performed the experiment using the Human Connectome Project (HCP) database. The results demonstrated the predictability of both primary and higher cortex, with primary cortex showing higher predictability. Additionally, the AUN performed better than other popular methods. We also found the improvement in dynamic functional connectivity (dFC) metrics through accurate prediction. The above results confirm the feasibility of predicting BOLD signals and their potential application in clinical settings.1 © 2023 IEEE.

关键词： adversarial paradigm blood oxygen level-dependent series prediction dynamic functional connectivity functional magnetic resonance imaging

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SUMOylation of α-tubulin is a novel modification regulating microtubule dynamics

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Journal of Molecular Cell Biology 2021年第2期13卷 91-103页

作者： Wenfeng Feng Rong Liu Xuan Xie Lei Diao Nannan Gao Jinke Cheng Xu Zhang Yong Li Lan Bao State Key Laboratory of Cell Biology CAS Center for Excellence in Molecular Cell Science/Shanghai Institute of Biochemistry and Cell BiologyUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Institute of Brain-Intelligence Technology Zhangjiang LaboratoryShanghai Research Center for Brain Science&Brain-Inspired IntelligenceShanghai 201210China Discipline of Neuroscience and Department of Biochemistry Collaborative Innovation Center for Brain ScienceShanghai Jiao Tong University School of MedicineShanghai 200025China Institute of Neuroscience and State Key Laboratory of Neuroscience CAS Center for Excellence in Brain ScienceChinese Academy of SciencesShanghai 200031China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China

Microtubules (MTs) are regulated by a number of known posttranslational modifications (PTMs) on α/β-tubulin to fulfill diverse cellular functions. Here, we showed that SUMOylation is a novel PTM on α-tubulin in vivo and in vitro. The SUMOylation on α-tubulin mainly occurred at Lys 96 (K96), K166, and K304 of soluble α-tubulin and could be removed by small ubiquitin-related modifier (SUMO)-specific peptidase 1. In vitro experiments showed that tubulin SUMOylation could reduce interprotofilament interaction, promote MT catastrophe, and impede MT polymerization. In cells, mutation of the SUMOylation sites on α-tubulin reduced catastrophe frequency and increased the proportion of polymerized α-tubulin, while upregulation of SUMOylation with fusion of SUMO1 reduced α-tubulin assembly into MTs. Additionally, overexpression of SUMOylation-deficient α-tubulin attenuated the neurite extension in Neuro-2a cells. Thus, SUMOylation on α-tubulin represents a new player in the regulation of MT properties.

关键词： α-tubulin SUMOylation microtubule dynamics microtubule assembly

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Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding

arXiv

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arXiv 2024年

作者： Song, Yu Han, Liyuan Xu, Bo Zhang, Tielin School of Artificial Intelligence University of Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China Institute of Automation Chinese Academy of Sciences China

brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MFSNN). The MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. It then enhances decoding performance by integrating these features through skip connections. Additionally, the MFSNN improves generalizability and robustness in cross-day signal decoding through mini-batch supervised generalization learning. In two benchmark invasive BCI paradigms, including the single-hand grasp-and-touch and center-and-out reach tasks, the MFSNN surpasses traditional artificial neural network methods, such as MLP and GRU, in both accuracy and computational efficiency. Moreover, the MFSNN's multiscale feature fusion framework is well-suited for the implementation on neuromorphic chips, offering an energy-efficient solution for online decoding of invasive BCI signals. © 2024, CC BY.

关键词： brain computer interface

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Enhanced-efficiency Capacitive Coupling Intra-body Power Transfer Systems with 1.8 V Output for Neural Interfaces

Enhanced-efficiency Capacitive Coupling Intra-body Power Tra...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Cheng Han Chuer Lin Jingna Mao Shan Yu Zhiwei Zhang School of Future Technology University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

Traditional wireless power transfer methods for powering neural interfaces have many restrictions such as short transmission distance and strict device alignment. The recently proposed capacitive coupling intra-body power transfer (CC-IBPT) which utilizes human body as the medium supports flexible placements of the transmitter electrode. In this paper, we established two prototype systems based on CC-IBPT with different power sources of a grounded signal generator and a battery-powered board to explore the maximum output power levels with 1.8 V load voltage. To improve the power transmission efficiency, LC impedance matching (IM) and backward compensation (BC) are conducted at the transmitter (TX) and receiver (RX) respectively. Measured results show that 2.5 and 7.4 times load power is enhanced in the two prototype systems. Moreover, the maximum power transfer efficiency (PTE) of 11.16% can be obtained with the TX-RX distance of 16 cm. Therefore, our work verifies CC-IBPT’s capability of achieving a high PTE in long-distance wireless power supply for neural interfaces and promotes its widespread application.

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Multi-channel Wireless Implantable brain-Computer Interface System

Multi-channel Wireless Implantable Brain-Computer Interface ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Chuer Lin Cheng Han Jingna Mao Shan Yu Zhiwei Zhang School of Future Technology University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

The implantable brain-computer interface has been widely used in recent years due to its great application potential and research value. Few neural implants have been designed to gather neural spikes, which require a higher sampling frequency than ECoG and LFPs. These systems are still constrained by low channel counts and their bulky size. Furthermore, wire connection is still used in many neural interfaces for further data analysis, facing challenges such as tissue infection, limited movement, and increased noise interference. To address the aforementioned problems, this paper presents a compact multi-channel wireless implantable brain-computer interface system that meets the requirements of spike signals collection and miniaturization. A WiFi module is utilized to transmit information between the system and terminal equipment to eliminate the tethering effects. A 128-channel signal acquisition module, consisting of two pieces of commercial digital electrophysiology amplifier chips, is designed to realize high channel counts for capturing spike events. The proposed system has successfully recorded the analog spike signals from a digital neural signal simulator.

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Dive into the Power of Neuronal Heterogeneity

arXiv

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arXiv 2023年

作者： Shen, Guobin Zhao, Dongcheng Dong, Yiting Li, Yang Zeng, Yi Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology CAS School of Future Technology University of Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

The biological neural network is a vast and diverse structure with high neural heterogeneity. Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weights of connections through training while modeling neurons as highly homogenized entities and lacking exploration of neural heterogeneity. Only a few studies have addressed neural heterogeneity by optimizing neuronal properties and connection weights to ensure network performance. However, this strategy impact the specific contribution of neuronal heterogeneity. In this paper, we first demonstrate the challenges faced by backpropagation-based methods in optimizing Spiking Neural Networks (SNNs) and achieve more robust optimization of heterogeneous neurons in random networks using an Evolutionary Strategy (ES). Experiments on tasks such as working memory, continuous control, and image recognition show that neuronal heterogeneity can improve performance, particularly in long sequence tasks. Moreover, we find that membrane time constants play a crucial role in neural heterogeneity, and their distribution is similar to that observed in biological experiments. Therefore, we believe that the neglected neuronal heterogeneity plays an essential role, providing new approaches for exploring neural heterogeneity in biology and new ways for designing more biologically plausible neural networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Image recognition

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Innovation and reform:China's 14th Five-Year Plan unfolds

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National science Review 2021年第1期8卷 6页

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

The Chinese government has recently announced a comprehensive proposal of the 14th Five-Year Plan for the next phase of development in all sectors of Chinese society. The Plan is formulated within the context of China’s long-term goal of building a moderately prosperous (‘xiaokang’) society by 2035. Importantly, the Plan includes detailed guidelines for the development of science and technology (S&T),

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SPIRONet: SPatial-Frequency Learning and TopologIcal Channel InteRactiOn Network for Vessel Segmentation

arXiv

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arXiv 2024年

作者： Huang, De-Xing Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Feng, Zhen-Qiu Gui, Mei-Jiang Li, Hao Xiang, Tian-Yu Yao, Bo-Xian Hou, Zeng-Guang the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China the CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China the Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China

Automatic vessel segmentation is paramount for developing next-generation interventional navigation systems. However, current approaches suffer from suboptimal segmentation performances due to significant challenges in intraoperative images (i.e., low signal-to-noise ratio, small or slender vessels, and strong interference). In this paper, a novel SPatial-frequency learning and topologIcal channel inteRactiOn Network (SPIRONet) is proposed to address the above issues. Specifically, dual encoders are utilized to comprehensively capture local spatial and global frequency vessel features. Then, a cross-attention fusion module is introduced to effectively fuse spatial and frequency features, thereby enhancing feature discriminability. Furthermore, a topological channel interaction module is designed to filter out task-irrelevant responses based on graph neural networks. Extensive experimental results on several challenging datasets (CADSA, CAXF, DCA1, and XCAD) demonstrate state-of-the-art performances of our method. Moreover, the inference speed of SPIRONet is 21 FPS with a 512 × 512 input size, surpassing clinical real-time requirements (6∼12FPS). These promising outcomes indicate SPIRONet’s potential for integration into vascular interventional navigation systems. Code is available at https://***/Dxhuang-casIA/SPIRONet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks

arXiv

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arXiv 2023年

作者： Shen, Guobin Zhao, Dongcheng Dong, Yiting Li, Yang Zhao, Feifei Zeng, Yi Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology CAS School of Future Technology University of Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our exploration into a new paradigm for Spiking Neural Networks (SNNs): a Plasticity-Driven Learning Framework (PDLF). This paradigm diverges from traditional neural network models that primarily focus on direct training of synaptic weights, leading to static connections that limit adaptability in dynamic environments. Instead, our approach delves into the heart of synaptic behavior, prioritizing the learning of plasticity rules themselves. This shift in focus from weight adjustment to mastering the intricacies of synaptic change offers a more flexible and dynamic pathway for neural networks to evolve and adapt. Our PDLF does not merely adapt existing concepts of functional and Presynaptic-Dependent Plasticity but redefines them, aligning closely with the dynamic and adaptive nature of biological learning. This reorientation enhances key cognitive abilities in artificial intelligence systems, such as working memory and multitasking capabilities, and demonstrates superior adaptability in complex, real-world scenarios. Moreover, our framework sheds light on the intricate relationships between various forms of plasticity and cognitive functions, thereby contributing to a deeper understanding of the brain’s learning mechanisms. Integrating this groundbreaking plasticity-centric approach in SNNs marks a significant advancement in the fusion of neuroscience and artificial intelligence. It paves the way for developing AI systems that not only learn but also adapt in an ever-changing world, much like the human brain. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks

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SMOC-Net: Leveraging Camera Pose for Self-Supervised Monocular Object Pose Estimation

SMOC-Net: Leveraging Camera Pose for Self-Supervised Monocul...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Tao Tan Qiulei Dong School of Artificial Intelligence UCAS State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Center for Excellence in Brain Science and Intelligence Technology CAS

Recently, self-supervised 6D object pose estimation, where synthetic images with object poses (sometimes jointly with un-annotated real images) are used for training, has attracted much attention in computer vision. Some typical works in literature employ a time-consuming differentiable renderer for object pose prediction at the training stage, so that (i) their performances on real images are generally limited due to the gap between their rendered images and real images and (ii) their training process is computationally expensive. To address the two problems, we propose a novel Network for Self-supervised Monocular Object pose estimation by utilizing the predicted Camera poses from unannotated real images, called SMOC-Net. The proposed network is explored under a knowledge distillation framework, consisting of a teacher model and a student model. The teacher model contains a backbone estimation module for initial object pose estimation, and an object pose refiner for refining the initial object poses using a geometric constraint (called relative-pose constraint) derived from relative camera poses. The student model gains knowledge for object pose estimation from the teacher model by imposing the relative-pose constraint. Thanks to the relative-pose constraint, SMOC-Net could not only narrow the domain gap between synthetic and real data but also reduce the training cost. Experimental results on two public datasets demonstrate that SMOC-Net outperforms several state-of-the-art methods by a large margin while requiring much less training time than the differentiable-renderer-based methods.

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