Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyz...Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.展开更多
视觉神经信息编解码旨在利用功能磁共振成像(functional magnetic resonance imaging,fMRI)等神经影像数据研究视觉刺激与大脑神经活动之间的关系。编码研究可以对神经活动模式进行建模和预测,有助于脑科学与类脑智能的发展;解码研究可...视觉神经信息编解码旨在利用功能磁共振成像(functional magnetic resonance imaging,fMRI)等神经影像数据研究视觉刺激与大脑神经活动之间的关系。编码研究可以对神经活动模式进行建模和预测,有助于脑科学与类脑智能的发展;解码研究可以对人的视知觉状态进行解译,能够促进脑机接口领域的发展。因此,基于fMRI的视觉神经信息编解码方法研究具有重要的科学意义和工程价值。本文在总结基于fMRI的视觉神经信息编解码关键技术与研究进展的基础上,分析现有视觉神经信息编解码方法的局限。在视觉神经信息编码方面,详细介绍了基于群体感受野估计方法的发展过程;在视觉神经信息解码方面,首先,按照任务类型将其划分为语义分类、图像辨识和图像重建3个部分,并深入阐述了每个部分的代表性研究工作和所用的方法。特别地,在图像重建部分着重介绍了基于深度生成模型(主要包括变分自编码器和生成对抗网络)的简单图像、人脸图像和复杂自然图像的重建技术。其次,统计整理了该领域常用的10个开源数据集,并对数据集的样本规模、被试个数、刺激类型、研究用途及下载地址进行了详细归纳。最后,详细介绍了视觉神经信息编解码模型常用的度量指标,分析了当前视觉神经信息编码和解码方法的不足,提出可行的改进意见,并对未来发展方向进行展望。展开更多
Vision plays a peculiar role in intelligence.Visual information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans becom...Vision plays a peculiar role in intelligence.Visual information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents.Recent advances have led to the development of brain-inspired algorithms and models for machine vision.One of the key components of these methods is the utilization of the computational principles underlying biological neurons.Additionally,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information.Thus,there is a high demand for mapping out functional models for reading out visual information from neural signals.Here,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.展开更多
<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This artic...<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's </span><span><a href="http://publicationethics.org/files/retraction%20guidelines.pdf"><span style="font-size:10.0pt;font-family:;" "="">Retraction Guidelines</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"="">. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.</span><span style="font-size:10.0pt;font-family:" color:black;"=""></span> </p> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">Please see the </span><span><a href="https://www.scirp.org/journal/paperinformation.aspx?paperid=101825"><span style="font-size:10.0pt;font-family:;" "="">article page</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> for more details. </span><span><a href="https://www.scirp.org/pdf/opj_2020072814494052.pdf"><span style="font-size:10.0pt;font-family:;" "="">The full retraction notice</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> in PDF is preceding the original paper which is marked "RETRACTED". </span> </p> <br /> </div>展开更多
<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm im...<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm improves the situation in theoretically but not performs well as expected in practical for the workload of nodes identification and the existence of many short blocks. Meanwhile, Neural network (NN) based decoders have appeared as potential candidates to replace conventional decoders for polar codes. But the exponentially increasing training complexity with information bits is unacceptable which means it is only suitable for short codes. In this paper, we present an improvement that increases decoding efficiency without degrading the error-correction performance. The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes NN decoders. The result shows that time steps the proposed algorithm need only equal to 79.8% of fast simplified successive-cancellation decoders require. Moreover, it has up to 21.2 times faster than successive-cancellation decoding algorithm. More importantly, the proposed algorithm decreases the hardness when applying in some degree. </div>展开更多
Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is pro...Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is proposed in this paper. Compared with linear filter with its revi-sion,the general relationship between the input and output of the inverse model of turbo decoding system can be established exactly by Nonlinear Auto-Regressive eXogeneous input (NARX) filter. Combined with linear inverse system,it has simpler structure and costs less computation,thus can satisfy the demand of real-time turbo decoding. Simulation results show that neural network in-verse control system can improve the performance of turbo decoding further than other linear con-trol system.展开更多
Overlapped X domain multiplexing(Ov XDM) is a promising encoding technique to obtain high spectral efficiency by utilizing Inter-Symbol Interference(ISI). However, the computational complexity of Maximum Likelihood Se...Overlapped X domain multiplexing(Ov XDM) is a promising encoding technique to obtain high spectral efficiency by utilizing Inter-Symbol Interference(ISI). However, the computational complexity of Maximum Likelihood Sequence Detection(MLSD) increases exponentially with the growth of spectral efficiency in Ov XDM, which is unbearable for practical implementations. This paper proposes an Ov TDM decoding method based on Recurrent Neural Network(RNN) to realize fast decoding of Ov TDM system, which has lower decoding complexity than the traditional fast decoding method. The paper derives the mathematical model of the Ov TDM decoder based on RNN and constructs the decoder model. And we compare the performance of the proposed decoding method with the MLSD algorithm and the Fano algorithm. It’s verified that the proposed decoding method exhibits a higher performance than the traditional fast decoding algorithm, especially for the scenarios of a high overlapped multiplexing coefficient.展开更多
Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer curs...Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decoder and the modified PNN (MPNN) decoder. In the ex-periment,rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity,and pressure was recorded by a pressure sensor synchronously. After training,the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their per-formances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder,with a CC of 0.8657 and an MSE of 0.2563,outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance,indicating that the MPNN decoder can handle different tasks in BMI system,including the detection of movement states and estimation of continuous kinematic parameters.展开更多
In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preferenceforkinetics andkinematics,a dynamical system perspective emerging in the last deca...In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preferenceforkinetics andkinematics,a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution.In this review,we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view.Here,we aim to reconcile the above perspectives,and evaluate their theoretical impact,future direction,and potential applications in brain-machine interfaces.展开更多
In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggreg...In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on MultiResUNet.Firstly,aggregate connection is introduced from the original feature aggregation at the same level.Skip connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip connections.Secondly,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature map.Finally,the original convolution block is improved.The convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial features.Residual connections are retained and the convolution block is turned into a multi-channel convolution block.The model is made to extract multi-scale spatial features.The experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.展开更多
基金supported by the National Natural Science Foundation of China (61031002, 61001172)the National Basic Research Program of China (2011CB504405)the Zhejiang Provincial Key Science and Technology Program for International Cooperation (2011C14005)
文摘Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.
文摘视觉神经信息编解码旨在利用功能磁共振成像(functional magnetic resonance imaging,fMRI)等神经影像数据研究视觉刺激与大脑神经活动之间的关系。编码研究可以对神经活动模式进行建模和预测,有助于脑科学与类脑智能的发展;解码研究可以对人的视知觉状态进行解译,能够促进脑机接口领域的发展。因此,基于fMRI的视觉神经信息编解码方法研究具有重要的科学意义和工程价值。本文在总结基于fMRI的视觉神经信息编解码关键技术与研究进展的基础上,分析现有视觉神经信息编解码方法的局限。在视觉神经信息编码方面,详细介绍了基于群体感受野估计方法的发展过程;在视觉神经信息解码方面,首先,按照任务类型将其划分为语义分类、图像辨识和图像重建3个部分,并深入阐述了每个部分的代表性研究工作和所用的方法。特别地,在图像重建部分着重介绍了基于深度生成模型(主要包括变分自编码器和生成对抗网络)的简单图像、人脸图像和复杂自然图像的重建技术。其次,统计整理了该领域常用的10个开源数据集,并对数据集的样本规模、被试个数、刺激类型、研究用途及下载地址进行了详细归纳。最后,详细介绍了视觉神经信息编解码模型常用的度量指标,分析了当前视觉神经信息编码和解码方法的不足,提出可行的改进意见,并对未来发展方向进行展望。
基金supported by National Natural Science Foundation of China(Nos.62176003 and 62088102)the Royal Society Newton Advanced Fellowship of the UK(No.NAF-R1-191082)。
文摘Vision plays a peculiar role in intelligence.Visual information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents.Recent advances have led to the development of brain-inspired algorithms and models for machine vision.One of the key components of these methods is the utilization of the computational principles underlying biological neurons.Additionally,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information.Thus,there is a high demand for mapping out functional models for reading out visual information from neural signals.Here,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.
文摘<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's </span><span><a href="http://publicationethics.org/files/retraction%20guidelines.pdf"><span style="font-size:10.0pt;font-family:;" "="">Retraction Guidelines</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"="">. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.</span><span style="font-size:10.0pt;font-family:" color:black;"=""></span> </p> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">Please see the </span><span><a href="https://www.scirp.org/journal/paperinformation.aspx?paperid=101825"><span style="font-size:10.0pt;font-family:;" "="">article page</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> for more details. </span><span><a href="https://www.scirp.org/pdf/opj_2020072814494052.pdf"><span style="font-size:10.0pt;font-family:;" "="">The full retraction notice</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> in PDF is preceding the original paper which is marked "RETRACTED". </span> </p> <br /> </div>
文摘<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm improves the situation in theoretically but not performs well as expected in practical for the workload of nodes identification and the existence of many short blocks. Meanwhile, Neural network (NN) based decoders have appeared as potential candidates to replace conventional decoders for polar codes. But the exponentially increasing training complexity with information bits is unacceptable which means it is only suitable for short codes. In this paper, we present an improvement that increases decoding efficiency without degrading the error-correction performance. The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes NN decoders. The result shows that time steps the proposed algorithm need only equal to 79.8% of fast simplified successive-cancellation decoders require. Moreover, it has up to 21.2 times faster than successive-cancellation decoding algorithm. More importantly, the proposed algorithm decreases the hardness when applying in some degree. </div>
文摘Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is proposed in this paper. Compared with linear filter with its revi-sion,the general relationship between the input and output of the inverse model of turbo decoding system can be established exactly by Nonlinear Auto-Regressive eXogeneous input (NARX) filter. Combined with linear inverse system,it has simpler structure and costs less computation,thus can satisfy the demand of real-time turbo decoding. Simulation results show that neural network in-verse control system can improve the performance of turbo decoding further than other linear con-trol system.
基金supported by the National Natural Science Foundation of China under Grant No.61871049.
文摘Overlapped X domain multiplexing(Ov XDM) is a promising encoding technique to obtain high spectral efficiency by utilizing Inter-Symbol Interference(ISI). However, the computational complexity of Maximum Likelihood Sequence Detection(MLSD) increases exponentially with the growth of spectral efficiency in Ov XDM, which is unbearable for practical implementations. This paper proposes an Ov TDM decoding method based on Recurrent Neural Network(RNN) to realize fast decoding of Ov TDM system, which has lower decoding complexity than the traditional fast decoding method. The paper derives the mathematical model of the Ov TDM decoder based on RNN and constructs the decoder model. And we compare the performance of the proposed decoding method with the MLSD algorithm and the Fano algorithm. It’s verified that the proposed decoding method exhibits a higher performance than the traditional fast decoding algorithm, especially for the scenarios of a high overlapped multiplexing coefficient.
基金Project supported by the National Natural Science Foundation of China (Nos. 30800287 and 60703038)the Natural Science Foundation of Zhejiang Province, China (No. Y2090707)
文摘Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decoder and the modified PNN (MPNN) decoder. In the ex-periment,rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity,and pressure was recorded by a pressure sensor synchronously. After training,the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their per-formances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder,with a CC of 0.8657 and an MSE of 0.2563,outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance,indicating that the MPNN decoder can handle different tasks in BMI system,including the detection of movement states and estimation of continuous kinematic parameters.
基金This review was supported by the National Key R&D Program(2017YFA0701102)the National Natural Science Foundation of China(31871047 and 31671075)+1 种基金Shanghai Municipal Science and Technology(18JC1415100 and 2018SHZDZX05)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32040100).
文摘In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preferenceforkinetics andkinematics,a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution.In this review,we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view.Here,we aim to reconcile the above perspectives,and evaluate their theoretical impact,future direction,and potential applications in brain-machine interfaces.
基金National Natural Science Foundation of China(No.61806006)Jiangsu University Superior Discipline Construction Project。
文摘In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on MultiResUNet.Firstly,aggregate connection is introduced from the original feature aggregation at the same level.Skip connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip connections.Secondly,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature map.Finally,the original convolution block is improved.The convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial features.Residual connections are retained and the convolution block is turned into a multi-channel convolution block.The model is made to extract multi-scale spatial features.The experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.
