IEEE Transactions on Cognitive and Developmental Systems最新文献_第8页

Automatic Prediction of Disturbance Caused by Interfloor Sound Events 自动预测楼层间声音事件造成的干扰

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-07-08 DOI: 10.1109/TCDS.2024.3424457

Stavros Ntalampiras;Alessandro Scalambrino

{"title":"Automatic Prediction of Disturbance Caused by Interfloor Sound Events","authors":"Stavros Ntalampiras;Alessandro Scalambrino","doi":"10.1109/TCDS.2024.3424457","DOIUrl":"10.1109/TCDS.2024.3424457","url":null,"abstract":"There is a direct correlation between noise and human health, while negative consequences may vary from sleep disruption and stress to hearing loss and reduced productivity. Despite its undeniable relevance, the underlying process governing the relationship between unpleasant sound events, and the annoyance they may cause has not been systematically studied yet. In this context, this work focuses on the disturbance caused by interfloor sound events, i.e., the audio signals transmitted through the floors of a building. Activities such as walking, running, using household appliances or other daily actions generate sounds that can be heard by those on an adjacent floor. To this end, we implemented a suitable dataset including diverse interfloor sound events annotated according to the perceived disturbance. Subsequently, we propose a framework able to quantify similarities exhibited by interfloor sound events starting from standardized time-frequency representations, which are processed by a Siamese neural network composed of a series of convolutional layers. Such similarities are then employed by a <inline-formula><tex-math>$k$</tex-math></inline-formula>-medoids regression scheme making disturbance predictions based on interfloor sound events with neighboring latent representations. After thorough experiments, we demonstrate the effectiveness of such a framework and its superiority over popular regression algorithms. Last but not least, the proposed solution offers interpretable predictions, which may be meaningfully utilized by human experts.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"147-154"},"PeriodicalIF":5.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141577775","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

SpikingViT: A Multiscale Spiking Vision Transformer Model for Event-Based Object Detection SpikingViT：用于基于事件的物体检测的多尺度尖峰视觉转换器模型

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-07-04 DOI: 10.1109/TCDS.2024.3422873

Lixing Yu;Hanqi Chen;Ziming Wang;Shaojie Zhan;Jiankun Shao;Qingjie Liu;Shu Xu

{"title":"SpikingViT: A Multiscale Spiking Vision Transformer Model for Event-Based Object Detection","authors":"Lixing Yu;Hanqi Chen;Ziming Wang;Shaojie Zhan;Jiankun Shao;Qingjie Liu;Shu Xu","doi":"10.1109/TCDS.2024.3422873","DOIUrl":"10.1109/TCDS.2024.3422873","url":null,"abstract":"Event cameras have unique advantages in object detection, capturing asynchronous events without continuous frames. They excel in dynamic range, low latency, and high-speed motion scenarios, with lower power consumption. However, aggregating event data into image frames leads to information loss and reduced detection performance. Applying traditional neural networks to event camera outputs is challenging due to event data's distinct characteristics. In this study, we present a novel spiking neural networks (SNNs)-based object detection model, the spiking vision transformer (SpikingViT) to address these issues. First, we design a dedicated event data converting module that effectively captures the unique characteristics of event data, mitigating the risk of information loss while preserving its spatiotemporal features. Second, we introduce SpikingViT, a novel object detection model that leverages SNNs capable of extracting spatiotemporal information among events data. SpikingViT combines the advantages of SNNs and transformer models, incorporating mechanisms such as attention and residual voltage memory to further enhance detection performance. Extensive experiments have substantiated the remarkable proficiency of SpikingViT in event-based object detection, positioning it as a formidable contender. Our proposed approach adeptly retains spatiotemporal information inherent in event data, leading to a substantial enhancement in detection performance.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"130-146"},"PeriodicalIF":5.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552987","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

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