IEEE Transactions on Cognitive and Developmental Systems最新文献_第6页

Regulating Temporal Neural Coding via Fast and Slow Synaptic Dynamics 通过快慢突触动态调节时态神经编码

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-07-01 DOI: 10.1109/TCDS.2024.3417477

Yuanhong Tang;Lingling An;Xingyu Zhang;Huiling Huang;Zhaofei Yu

{"title":"Regulating Temporal Neural Coding via Fast and Slow Synaptic Dynamics","authors":"Yuanhong Tang;Lingling An;Xingyu Zhang;Huiling Huang;Zhaofei Yu","doi":"10.1109/TCDS.2024.3417477","DOIUrl":"10.1109/TCDS.2024.3417477","url":null,"abstract":"The NMDA receptor (NMDAR), as a ubiquitous type of synapse in neural systems of the brain, presents slow dynamics to modulate neural spiking activity. For the cerebellum, NMDARs have been suggested for contributing complex spikes in Purkinje cells (PCs) as a mechanism for cognitive activity, learning, and memory. Recent experimental studies are debating the role of NMDAR in PC dendritic input, yet it remains unclear how the distribution of NMDARs in PC dendrites can affect their neural spiking coding properties. In this work, a detailed multiple-compartment PC model was used to study how slow-scale NMDARs together with fast-scale AMPA, regulate neural coding. We find that NMDARs act as a band-pass filter, increasing the excitability of PC firing under low-frequency input while reducing it under high frequency. This effect is positively related to the strength of NMDARs. For a response sequence containing a large number of regular and irregular spiking patterns, NMDARs reduce the overall regularity under high-frequency input while increasing the local regularity under low-frequency. Moreover, the inhibitory effect of NMDA receptors during high-frequency stimulation is associated with a reduced conductance of large conductance calcium-activated potassium (BK) channel. Taken together, our results suggest that NMDAR plays an important role in the regulation of neural coding strategies by utilizing its complex dendritic structure.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"102-114"},"PeriodicalIF":5.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prepulse Inhibition and Prestimulus Nonlinear Brain Dynamics in Childhood: A Lyapunov Exponent Approach 儿童期的前脉冲抑制和前刺激非线性脑动力学：一种莱普诺夫指数方法

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-26 DOI: 10.1109/TCDS.2024.3418841

Anastasios E. Giannopoulos;Ioanna Zioga;Vaios Ziogas;Panos Papageorgiou;Georgios N. Papageorgiou;Charalabos Papageorgiou

{"title":"Prepulse Inhibition and Prestimulus Nonlinear Brain Dynamics in Childhood: A Lyapunov Exponent Approach","authors":"Anastasios E. Giannopoulos;Ioanna Zioga;Vaios Ziogas;Panos Papageorgiou;Georgios N. Papageorgiou;Charalabos Papageorgiou","doi":"10.1109/TCDS.2024.3418841","DOIUrl":"10.1109/TCDS.2024.3418841","url":null,"abstract":"The acoustic startle reflex (ASR) relies on the sensorimotor system and is affected by aging, sex, and psychopathology. ASR can be modulated by the prepulse inhibition (PPI) paradigm, which achieves the inhibition of reactivity to a startling stimulus (pulse) following a weak prepulse stimulus. Additionally, neurophysiological studies have found that brain activity is characterized by irregular patterns with high complexity, which however reduces with age. Our study investigated the relationship between prestartle nonlinear dynamics and PPI in healthy children versus adults. Fifty-six individuals took part in the experiment: 31 children and adolescents and 25 adults. Participants heard 51 pairs of tones (prepulse and startle) with a time difference of 30 to 500 ms. Subsequently, we assessed neural complexity by computing the largest Lyapunov exponent (LLE) during the prestartle period and assessed PPI by analyzing the poststartle event-related potentials (ERPs). Results showed higher neural complexity for children compared to adults, in line with previous research showing reduced complexity in the physiological signals in aging. As expected, PPI (as reflected in the P50 and P200 components) was enhanced in adults compared to children, potentially due to the maturation of the ASR for the former. Interestingly, prestartle complexity was correlated with the P50 component in children only, but not in adults, potentially due to the different stage of sensorimotor maturation between age groups. Overall, our study offers novel contributions for investigating brain dynamics, linking nonlinear with linear measures. Our findings are consistent with the loss of neural complexity in aging, and suggest differentiated links between nonlinear and linear metrics in children and adults.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"115-129"},"PeriodicalIF":5.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10572331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Distinction Between Object Recognition and Object Identification in Brain Connectivity for Brain–Computer Interface Applications 脑连接中物体识别与物体识别的区别，用于脑机接口应用

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-24 DOI: 10.1109/TCDS.2024.3417299

Daniel Leong;Thomas Do;Chin-Teng Lin

{"title":"The Distinction Between Object Recognition and Object Identification in Brain Connectivity for Brain–Computer Interface Applications","authors":"Daniel Leong;Thomas Do;Chin-Teng Lin","doi":"10.1109/TCDS.2024.3417299","DOIUrl":"10.1109/TCDS.2024.3417299","url":null,"abstract":"Object recognition and object identification are complex cognitive processes where information is integrated and processed by an extensive network of brain areas. However, although object recognition and object identification are similar, they are considered separate functions in the brain. Interestingly, the difference between object recognition and object identification has still not been characterized in a way that brain–computer interface (BCI) applications can detect or use. Hence, in this study, we investigated neural features during object recognition and identification tasks through functional brain connectivity. We conducted an experiment involving 25 participants to explore these neural features. Participants completed two tasks: an object recognition task, where they determined whether a target object belonged to a specified category, and an object identification task, where they identified the target object among four displayed images. Our aim was to discover reliable features that could distinguish between object recognition and identification. The results demonstrate a significant difference between object recognition and identification in the participation coefficient (PC) and clustering coefficient (CC) of delta activity in the visual and temporal regions of the brain. Further analysis at the category level shows that this coefficient differs for different categories of objects. Utilizing these discovered features for binary classification, the accuracy for the animal category reached 80.28%. The accuracy for flower and vehicle categories also improved when combining the PC and CC, although no improvement was observed for the food category. Overall, what we have found is a feature that might be able to be used to differentiate between object recognition and identification within a BCI object recognition system. Further, it may help BCI object recognition systems to determine a user’s intentions when selecting an object.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"89-101"},"PeriodicalIF":5.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Cognitive and Developmental Systems Information for Authors 电气和电子工程师学会《认知与发展系统》期刊为作者提供的信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-10 DOI: 10.1109/TCDS.2024.3398475

引用次数: 0

IEEE Transactions on Cognitive and Developmental Systems Publication Information 电气和电子工程师学会认知与发展系统论文集》出版信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-10 DOI: 10.1109/TCDS.2024.3398471

引用次数: 0

IEEE Computational Intelligence Society 电气和电子工程师学会计算智能学会

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-10 DOI: 10.1109/TCDS.2024.3398473

引用次数: 0

Automotive Object Detection via Learning Sparse Events by Spiking Neurons 通过尖峰神经元学习稀疏事件进行汽车物体检测

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-06 DOI: 10.1109/TCDS.2024.3410371

Hu Zhang;Yanchen Li;Luziwei Leng;Kaiwei Che;Qian Liu;Qinghai Guo;Jianxing Liao;Ran Cheng

{"title":"Automotive Object Detection via Learning Sparse Events by Spiking Neurons","authors":"Hu Zhang;Yanchen Li;Luziwei Leng;Kaiwei Che;Qian Liu;Qinghai Guo;Jianxing Liao;Ran Cheng","doi":"10.1109/TCDS.2024.3410371","DOIUrl":"10.1109/TCDS.2024.3410371","url":null,"abstract":"Event-based sensors, distinguished by their high temporal resolution of \u0000<inline-formula><tex-math>$1 {boldsymbol{mu}}text{s}$</tex-math></inline-formula>\u0000 and a dynamic range of \u0000<inline-formula><tex-math>$120 mathrm{dB}$</tex-math></inline-formula>\u0000, stand out as ideal tools for deployment in fast-paced settings such as vehicles and drones. Traditional object detection techniques that utilize artificial neural networks (ANNs) face challenges due to the sparse and asynchronous nature of the events these sensors capture. In contrast, spiking neural networks (SNNs) offer a promising alternative, providing a temporal representation that is inherently aligned with event-based data. This article explores the unique membrane potential dynamics of SNNs and their ability to modulate sparse events. We introduce an innovative spike-triggered adaptive threshold mechanism designed for stable training. Building on these insights, we present a specialized spiking feature pyramid network (SpikeFPN) optimized for automotive event-based object detection. Comprehensive evaluations demonstrate that SpikeFPN surpasses both traditional SNNs and advanced ANNs enhanced with attention mechanisms. Evidently, SpikeFPN achieves a mean average precision (mAP) of 0.477 on the GEN1 automotive detection (GAD) benchmark dataset, marking significant increases over the selected SNN baselines. Moreover, the efficient design of SpikeFPN ensures robust performance while optimizing computational resources, attributed to its innate sparse computation capabilities.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 6","pages":"2110-2124"},"PeriodicalIF":5.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of the Cyclopean Eye From Binocular Smooth Pursuit Tests 通过双目平滑追视测试估测回旋眼的视力

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-05 DOI: 10.1109/TCDS.2024.3410110

Elisa Luque-Buzo;Mehdi Bejani;Julián D. Arias-Londoñ;Jorge A. Gómez-García;Francisco Grandas-Pérez;Juan I. Godino-Llorente

{"title":"Estimation of the Cyclopean Eye From Binocular Smooth Pursuit Tests","authors":"Elisa Luque-Buzo;Mehdi Bejani;Julián D. Arias-Londoñ;Jorge A. Gómez-García;Francisco Grandas-Pérez;Juan I. Godino-Llorente","doi":"10.1109/TCDS.2024.3410110","DOIUrl":"10.1109/TCDS.2024.3410110","url":null,"abstract":"In binocular vision, the visual system combines images in the retina to generate a single perception, which triggers a sensorimotor process that forces the eyes to point to the same target. Thus, following a moving target, both eyes are expected to move synchronously following identical motor triggers but, in practise, significant differences between eyes are found due to the presence of certain artifacts and effects. Thus, a better indirect characterization of the underlying neurological behavior during eye motion would require new automatic preprocessing methods applied to the eye-tracking sequences for rendering the common and most significant movements of both eyes. To address this need, the present study proposes an automatic method for extracting the common components of the left- and right-eye motions from a set of Smooth Pursuit tests by applying an independent component analysis. To do so, both sequences are decomposed into two independent latent components: the first presumably correlates with the common motor triggering at the brain, while the second collects artifacts introduced during the recording process and small effects due to convergence deficits and eye dominance biases. The evaluations were carried out using data corresponding to 12 different smooth pursuit eye movements tests, which were collected using an infrared high-speed video-based eye-tracking device from 41 parkinsonian patients and 47 controls. The results show that the automatic method can separate the aforementioned components in 99.50% of cases, extracting a latent component correlated with the common motor triggering at the brain, which we hypothesize is characterizing the movements of the cyclopean eye. The estimated component could be used to simplify any other potential automatic analysis.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 6","pages":"2125-2137"},"PeriodicalIF":5.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10549994","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoding Joint-Level Hand Movements With Intracortical Neural Signals in a Human Brain–Computer Interface 在人脑-计算机接口中利用皮层内神经信号解码关节级手部运动

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-04 DOI: 10.1109/TCDS.2024.3409555

Huaqin Sun;Yu Qi;Xiaodi Wu;Junming Zhu;Jianmin Zhang;Yueming Wang

{"title":"Decoding Joint-Level Hand Movements With Intracortical Neural Signals in a Human Brain–Computer Interface","authors":"Huaqin Sun;Yu Qi;Xiaodi Wu;Junming Zhu;Jianmin Zhang;Yueming Wang","doi":"10.1109/TCDS.2024.3409555","DOIUrl":"10.1109/TCDS.2024.3409555","url":null,"abstract":"Fine movements of hands play an important role in everyday life. While existing studies have successfully decoded hand gestures or finger movements from brain signals, direct decoding of single-joint kinematics remains challenging. This study aims to investigate the decoding of fine hand movements at the single-joint level. Neural activities were recorded from the motor cortex (MC) of a human participant while imagining eleven different hand movements. We comprehensively evaluated the decoding efficiency of various brain signal features, neural decoding algorithms, and single-joint kinematic variables for decoding. Results showed that using the spiking band power (SBP) signals, we could faithfully decode the single-joint angles with an average correlation coefficient of 0.77, outperforming other brain signal features. Nonlinear approaches that incorporate temporal context information, particularly recurrent neural networks, significantly outperformed traditional methods. Decoding joint angles yielded superior results compared to joint angular velocities. Our approach facilitates the construction of high-performance brain–computer interfaces for dexterous hand control.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 6","pages":"2100-2109"},"PeriodicalIF":5.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Strategies and Cooperative Teaming for 3-D Multiplayer Reach-Avoid Games 三维多人避险游戏的最佳策略与合作组队

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-06-03 DOI: 10.1109/TCDS.2024.3406889

Peng Gao;Xiuxian Li;Jinwen Hu

{"title":"Optimal Strategies and Cooperative Teaming for 3-D Multiplayer Reach-Avoid Games","authors":"Peng Gao;Xiuxian Li;Jinwen Hu","doi":"10.1109/TCDS.2024.3406889","DOIUrl":"10.1109/TCDS.2024.3406889","url":null,"abstract":"This article studies multiplayer reach-avoid games with a plane being the goal in 3-D space. Due to the difficulty that directly analyzing multipursuer multievader scenarios brings the curse of dimensionality, the whole problem is decomposed to distinct subgames. In the subgames, a single pursuer or multiple pursuers, which have different speeds, form a team to capture one evader cooperatively while the evader struggles to reach the plane. With the players’ dominance region based on the definition of isochronous surfaces, the target points and value functions are obtained for the game of degree by using Apollonius spheres. Additionally, the corresponding closed-loop saddle-point strategies are shown to be Nash equilibrium. The degeneration between scenarios of different scales is also discussed. To minimize the sum of subgames’ costs, the tasks of intercepting multiple evaders are assigned to individuals or teams in the form of bipartite graph matching. A hierarchical matching algorithm and a state-feedback rematching method are proposed which can be updated in real-time to improve the solution. Finally, diverse empirical experiments and comparisons with state-of-the-art methods are illustrated to demonstrate the optimality of proposed strategies and algorithms in this article.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 6","pages":"2085-2099"},"PeriodicalIF":5.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0