首页 > 最新文献

Frontiers in Computational Neuroscience最新文献

英文 中文
Frontiers | SaE-GBLS: an effective self-adaptive evolutionary optimized graph-broad model for EEG-based automatic epileptic seizure detection 前沿 | SaE-GBLS:基于脑电图的癫痫发作自动检测的有效自适应进化优化图宽模型
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-26 DOI: 10.3389/fncom.2024.1379368
Liming Cheng, Jiaqi Xiong, Junwei Duan, Yuhang Zhang, Chun Chen, Jingxin Zhong, Zhiguo Zhou, Yujuan Quan
IntroductionEpilepsy is a common neurological condition that affects a large number of individuals worldwide. One of the primary challenges in epilepsy is the accurate and timely detection of seizure. Recently, the graph regularized broad learning system (GBLS) has achieved superior performance improvement with its flat structure and less time-consuming training process compared to deep neural networks. Nevertheless, the number of feature and enhancement nodes in GBLS is predetermined. These node settings are also randomly selected and remain unchanged throughout the training process. The characteristic of randomness is thus more easier to make non-optimal nodes generate, which cannot contribute significantly to solving the optimization problem.MethodsTo obtain more optimal nodes for optimization and achieve superior automatic detection performance, we propose a novel broad neural network named self-adaptive evolutionary graph regularized broad learning system (SaE-GBLS). Self-adaptive evolutionary algorithm, which can construct mutation strategies in the strategy pool based on the experience of producing solutions for selecting network parameters, is incorporated into SaE-GBLS model for optimizing the node parameters. The epilepsy seizure is automatic detected by our proposed SaE-GBLS model based on three publicly available EEG datasets and one private clinical EEG dataset.Results and discussionThe experimental results indicate that our suggested strategy has the potential to perform as well as current machine learning approaches.
导言癫痫是一种常见的神经系统疾病,影响着全球众多患者。癫痫的主要挑战之一是准确及时地检测癫痫发作。最近,与深度神经网络相比,图正则化广泛学习系统(GBLS)凭借其扁平化结构和较少的训练过程耗时,实现了卓越的性能提升。然而,GBLS 中特征节点和增强节点的数量是预先确定的。这些节点的设置也是随机选择的,并在整个训练过程中保持不变。为了获得更多优化节点,实现更优越的自动检测性能,我们提出了一种名为自适应进化图正则化广义学习系统(SaE-GBLS)的新型广义神经网络。在 SaE-GBLS 模型中加入了自适应进化算法,该算法可根据产生解决方案的经验在策略池中构建突变策略,以选择网络参数,从而优化节点参数。我们提出的 SaE-GBLS 模型基于三个公开的脑电图数据集和一个私人临床脑电图数据集自动检测癫痫发作。
{"title":"Frontiers | SaE-GBLS: an effective self-adaptive evolutionary optimized graph-broad model for EEG-based automatic epileptic seizure detection","authors":"Liming Cheng, Jiaqi Xiong, Junwei Duan, Yuhang Zhang, Chun Chen, Jingxin Zhong, Zhiguo Zhou, Yujuan Quan","doi":"10.3389/fncom.2024.1379368","DOIUrl":"https://doi.org/10.3389/fncom.2024.1379368","url":null,"abstract":"IntroductionEpilepsy is a common neurological condition that affects a large number of individuals worldwide. One of the primary challenges in epilepsy is the accurate and timely detection of seizure. Recently, the graph regularized broad learning system (GBLS) has achieved superior performance improvement with its flat structure and less time-consuming training process compared to deep neural networks. Nevertheless, the number of feature and enhancement nodes in GBLS is predetermined. These node settings are also randomly selected and remain unchanged throughout the training process. The characteristic of randomness is thus more easier to make non-optimal nodes generate, which cannot contribute significantly to solving the optimization problem.MethodsTo obtain more optimal nodes for optimization and achieve superior automatic detection performance, we propose a novel broad neural network named self-adaptive evolutionary graph regularized broad learning system (SaE-GBLS). Self-adaptive evolutionary algorithm, which can construct mutation strategies in the strategy pool based on the experience of producing solutions for selecting network parameters, is incorporated into SaE-GBLS model for optimizing the node parameters. The epilepsy seizure is automatic detected by our proposed SaE-GBLS model based on three publicly available EEG datasets and one private clinical EEG dataset.Results and discussionThe experimental results indicate that our suggested strategy has the potential to perform as well as current machine learning approaches.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"16 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique 利用深度学习技术增强核磁共振成像图像中脑肿瘤的模式检测和分割
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-26 DOI: 10.3389/fncom.2024.1418280
Lubna Kiran, Asim Zeb, Qazi Nida Ur Rehman, Taj Rahman, Muhammad Shehzad Khan, Shafiq Ahmad, Muhammad Irfan, Muhammad Naeem, Shamsul Huda, Haitham Mahmoud
Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholding method and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks.
神经科学是一门进展迅速的学科,旨在揭示人类大脑和思维的复杂运作。脑肿瘤从非癌症到恶性肿瘤,有 100 多种不同类型,给诊断带来了巨大挑战。有效的治疗取决于对这些肿瘤的早期精确检测和分割。为此,我们引入了一种采用二元卷积神经网络(BCNN)的尖端深度学习方法。这种方法可用于分割 10 种最常见的脑肿瘤类型,与目前只能分割四种类型的模型相比有了显著改进。我们的方法首先是获取核磁共振成像图像,然后是详细的预处理阶段,使用自适应阈值法和形态学操作对图像进行二进制转换。这为下一步即分割做好了数据准备。分割可识别肿瘤类型,并根据其等级(I 级到 IV 级)对其进行分类,将其与健康脑组织区分开来。我们还专门为这项研究设计了一个独特的数据集,其中包括 6,600 张脑核磁共振成像图像。我们提出的模型的总体性能达到了 99.36%。在分割任务中,我们的模型达到了 99.40% 的准确率、99.32% 的精确率、99.45% 的召回率和 99.28% 的 F-Measure,这些出色的性能指标凸显了我们模型的有效性。
{"title":"An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique","authors":"Lubna Kiran, Asim Zeb, Qazi Nida Ur Rehman, Taj Rahman, Muhammad Shehzad Khan, Shafiq Ahmad, Muhammad Irfan, Muhammad Naeem, Shamsul Huda, Haitham Mahmoud","doi":"10.3389/fncom.2024.1418280","DOIUrl":"https://doi.org/10.3389/fncom.2024.1418280","url":null,"abstract":"Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholding method and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"91 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Frontiers | Purkinje cell models: past, present and future 前沿|浦肯野细胞模型:过去、现在和未来
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-24 DOI: 10.3389/fncom.2024.1426653
Elías Mateo Fernández Santoro, Arun Karim, Pascal Warnaar, Chris I. De Zeeuw, Aleksandra Badura, Mario Negrello
The investigation of the dynamics of Purkinje cell (PC) activity is crucial to unravel the role of the cerebellum in motor control, learning and cognitive processes. Within the cerebellar cortex (CC), these neurons receive all the incoming sensory and motor information, transform it and generate the entire cerebellar output. The relatively homogenous and repetitive structure of the CC, common to all vertebrate species, suggests a single computation mechanism shared across all PCs. While PC models have been developed since the 70′s, a comprehensive review of contemporary models is currently lacking. Here, we provide an overview of PC models, ranging from the ones focused on single cell intracellular PC dynamics, through complex models which include synaptic and extrasynaptic inputs. We review how PC models can reproduce physiological activity of the neuron, including firing patterns, current and multistable dynamics, plateau potentials, calcium signaling, intrinsic and synaptic plasticity and input/output computations. We consider models focusing both on somatic and on dendritic computations. Our review provides a critical performance analysis of PC models with respect to known physiological data. We expect our synthesis to be useful in guiding future development of computational models that capture real-life PC dynamics in the context of cerebellar computations.
研究浦肯野细胞(PC)的动态活动对于揭示小脑在运动控制、学习和认知过程中的作用至关重要。在小脑皮层(CC)中,这些神经元接收所有传入的感觉和运动信息,将其转化并产生整个小脑输出。小脑皮层的结构相对单一且具有重复性,是所有脊椎动物的共同特征,这表明所有小脑皮层都有一个共同的计算机制。虽然 PC 模型自上世纪 70 年代就已出现,但目前还缺乏对当代模型的全面回顾。在本文中,我们将概述 PC 模型,从侧重于单细胞胞内 PC 动态的模型,到包括突触和突触外输入的复杂模型。我们回顾了 PC 模型如何再现神经元的生理活动,包括发射模式、电流和多稳态动力学、高原电位、钙信号、内在和突触可塑性以及输入/输出计算。我们考虑的模型既关注体细胞计算,也关注树突计算。我们的综述结合已知的生理数据,对 PC 模型进行了重要的性能分析。我们希望我们的综述有助于指导未来计算模型的开发,从而在小脑计算的背景下捕捉现实生活中的PC动态。
{"title":"Frontiers | Purkinje cell models: past, present and future","authors":"Elías Mateo Fernández Santoro, Arun Karim, Pascal Warnaar, Chris I. De Zeeuw, Aleksandra Badura, Mario Negrello","doi":"10.3389/fncom.2024.1426653","DOIUrl":"https://doi.org/10.3389/fncom.2024.1426653","url":null,"abstract":"The investigation of the dynamics of Purkinje cell (PC) activity is crucial to unravel the role of the cerebellum in motor control, learning and cognitive processes. Within the cerebellar cortex (CC), these neurons receive all the incoming sensory and motor information, transform it and generate the entire cerebellar output. The relatively homogenous and repetitive structure of the CC, common to all vertebrate species, suggests a single computation mechanism shared across all PCs. While PC models have been developed since the 70′s, a comprehensive review of contemporary models is currently lacking. Here, we provide an overview of PC models, ranging from the ones focused on single cell intracellular PC dynamics, through complex models which include synaptic and extrasynaptic inputs. We review how PC models can reproduce physiological activity of the neuron, including firing patterns, current and multistable dynamics, plateau potentials, calcium signaling, intrinsic and synaptic plasticity and input/output computations. We consider models focusing both on somatic and on dendritic computations. Our review provides a critical performance analysis of PC models with respect to known physiological data. We expect our synthesis to be useful in guiding future development of computational models that capture real-life PC dynamics in the context of cerebellar computations.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"21 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification 混合深度空间和统计特征融合,实现精确的磁共振成像脑肿瘤分类
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-24 DOI: 10.3389/fncom.2024.1423051
Saeed Iqbal, Adnan N. Qureshi, Musaed Alhussein, Khursheed Aurangzeb, Imran Arshad Choudhry, Muhammad Shahid Anwar
The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.
医学图像的分类在生物医学领域至关重要,尽管人们一直在努力解决这一问题,但重大挑战依然存在。要对医学图像进行有效分类,收集和整合能准确描述图像的统计信息至关重要。本研究提出了一种独特的特征提取方法,将深度空间特征与手工统计特征相结合。该方法包括使用先进技术提取放射线组学统计特征,然后使用受 ResNet 深度学习模型启发的新型手工特征融合方法。然后使用新的特征融合框架(FusionNet)来降低图像维度并简化计算。研究人员在 BraTS 数据集中的脑肿瘤 MRI 图像上对所提出的方法进行了测试,结果表明该方法在分类准确性方面优于现有方法。研究提出了三个模型,包括一个基于手工制作的模型和两个 CNN 模型,它们完成了二元分类任务。推荐的混合方法取得了较高的 F1 分数(96.12 ± 0.41)、精确度(97.77 ± 0.32)和准确度(97.53 ± 0.24),表明它有潜力成为病理学家的重要工具。
{"title":"Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification","authors":"Saeed Iqbal, Adnan N. Qureshi, Musaed Alhussein, Khursheed Aurangzeb, Imran Arshad Choudhry, Muhammad Shahid Anwar","doi":"10.3389/fncom.2024.1423051","DOIUrl":"https://doi.org/10.3389/fncom.2024.1423051","url":null,"abstract":"The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"8 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Translational symmetry in convolutions with localized kernels causes an implicit bias toward high frequency adversarial examples 具有局部核的卷积中的平移对称性会导致对高频对抗性示例的隐含偏见
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-20 DOI: 10.3389/fncom.2024.1387077
Josue O. Caro, Yilong Ju, Ryan Pyle, Sourav Dey, Wieland Brendel, Fabio Anselmi, Ankit B. Patel
Adversarial attacks are still a significant challenge for neural networks. Recent efforts have shown that adversarial perturbations typically contain high-frequency features, but the root cause of this phenomenon remains unknown. Inspired by theoretical work on linear convolutional models, we hypothesize that translational symmetry in convolutional operations together with localized kernels implicitly bias the learning of high-frequency features, and that this is one of the main causes of high frequency adversarial examples. To test this hypothesis, we analyzed the impact of different choices of linear and non-linear architectures on the implicit bias of the learned features and adversarial perturbations, in spatial and frequency domains. We find that, independently of the training dataset, convolutional operations have higher frequency adversarial attacks compared to other architectural parameterizations, and that this phenomenon is exacerbated with stronger locality of the kernel (kernel size) end depth of the model. The explanation for the kernel size dependence involves the Fourier Uncertainty Principle: a spatially-limited filter (local kernel in the space domain) cannot also be frequency-limited (local in the frequency domain). Using larger convolution kernel sizes or avoiding convolutions (e.g., by using Vision Transformers or MLP-style architectures) significantly reduces this high-frequency bias. Looking forward, our work strongly suggests that understanding and controlling the implicit bias of architectures will be essential for achieving adversarial robustness.
对抗性攻击仍然是神经网络面临的重大挑战。最近的研究表明,对抗性扰动通常包含高频特征,但这一现象的根本原因仍然未知。受线性卷积模型理论研究的启发,我们假设卷积运算中的平移对称性和局部化内核会使高频特征的学习产生隐性偏差,而这正是高频对抗范例的主要原因之一。为了验证这一假设,我们分析了线性和非线性架构的不同选择在空间域和频率域对所学特征的隐性偏差和对抗性扰动的影响。我们发现,与训练数据集无关,卷积运算与其他架构参数化相比,具有更高频率的对抗性攻击,而且这种现象会随着模型内核(内核大小)末端深度更强的局部性而加剧。对内核大小依赖性的解释涉及傅立叶不确定性原理:空间受限的滤波器(空间域中的局部内核)不可能同时也是频率受限的(频域中的局部)。使用更大的卷积核大小或避免卷积(例如,通过使用视觉变换器或 MLP 型架构)可显著减少这种高频偏差。展望未来,我们的工作有力地表明,理解和控制架构的隐含偏差对于实现对抗鲁棒性至关重要。
{"title":"Translational symmetry in convolutions with localized kernels causes an implicit bias toward high frequency adversarial examples","authors":"Josue O. Caro, Yilong Ju, Ryan Pyle, Sourav Dey, Wieland Brendel, Fabio Anselmi, Ankit B. Patel","doi":"10.3389/fncom.2024.1387077","DOIUrl":"https://doi.org/10.3389/fncom.2024.1387077","url":null,"abstract":"Adversarial attacks are still a significant challenge for neural networks. Recent efforts have shown that adversarial perturbations typically contain high-frequency features, but the root cause of this phenomenon remains unknown. Inspired by theoretical work on linear convolutional models, we hypothesize that <jats:italic>translational symmetry in convolutional operations</jats:italic> together with <jats:italic>localized kernels implicitly bias the learning of high-frequency features</jats:italic>, and that this is one of the main causes of <jats:italic>high frequency adversarial examples</jats:italic>. To test this hypothesis, we analyzed the impact of different choices of linear and <jats:italic>non-linear</jats:italic> architectures on the implicit bias of the learned features and adversarial perturbations, in spatial and frequency domains. We find that, independently of the training dataset, convolutional operations have higher frequency adversarial attacks compared to other architectural parameterizations, and that this phenomenon is exacerbated with stronger locality of the kernel (kernel size) end depth of the model. The explanation for the kernel size dependence involves the Fourier Uncertainty Principle: a spatially-limited filter (local kernel in the space domain) cannot also be frequency-limited (local in the frequency domain). Using larger convolution kernel sizes or avoiding convolutions (e.g., by using Vision Transformers or MLP-style architectures) significantly reduces this high-frequency bias. Looking forward, our work strongly suggests that understanding and controlling the implicit bias of architectures will be essential for achieving adversarial robustness.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"86 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals. 用于识别癫痫脑电信号的新型多特征融合注意力神经网络。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-19 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1393122
Congshan Sun, Cong Xu, Hongwei Li, Hongjian Bo, Lin Ma, Haifeng Li

Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis.

癫痫是一种常见的慢性脑部疾病。通过观察脑电图(EEG)检测癫痫是神经学家使用的主要方法,但这种方法耗时较长。脑电信号是非稳态、非线性的,而且通常噪声很大,因此要更准确、更自动地识别癫痫脑电信号仍是一项挑战。本文提出了一种基于注意力网络的新型单通道脑电图信号分类系统,该系统综合了时间频率和非线性动态特征。该系统有三个新颖的模块。第一个模块在双通道并行卷积网络中构建具有高时频分辨率的希尔伯特频谱(HS)。通过对两个分支的高维特征进行补充,充分提取时频特征。第二个模块是构建灰度递归图(GRP),它比传统的 RP 包含更多的非线性动态特征,并将其输入残差连接卷积模块,以有效学习非线性动态特征。第三个模块是基于自注意机制的特征融合模块,为不同类型的特征分配最佳权重,进一步提高系统的信息提取能力。因此,该系统被命名为 HG-SANet。在波恩脑电数据库和伯尔尼-巴塞罗那脑电数据库上进行的多项分类任务结果表明,HG-SANet 能有效捕捉不同领域提取特征的贡献度,显著增强模型的表达能力,提高癫痫脑电信号的识别准确率。HG-SANet可以提高癫痫的诊断和治疗效率,在脑疾病诊断领域具有广阔的应用前景。
{"title":"A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals.","authors":"Congshan Sun, Cong Xu, Hongwei Li, Hongjian Bo, Lin Ma, Haifeng Li","doi":"10.3389/fncom.2024.1393122","DOIUrl":"10.3389/fncom.2024.1393122","url":null,"abstract":"<p><p>Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1393122"},"PeriodicalIF":2.1,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141497603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Residual and bidirectional LSTM for epileptic seizure detection. 用于癫痫发作检测的残差和双向 LSTM。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-06-17 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1415967
Wei Zhao, Wen-Feng Wang, Lalit Mohan Patnaik, Bao-Can Zhang, Su-Jun Weng, Shi-Xiao Xiao, De-Zhi Wei, Hai-Feng Zhou

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.

脑电图(EEG)在癫痫发作的检测和分析中起着举足轻重的作用,全世界有 7000 多万人受到癫痫发作的影响。然而,用于癫痫检测的脑电信号的可视化解读既费力又费时。为了应对这一挑战,我们引入了一种简单而高效的混合深度学习方法,名为 ResBiLSTM,用于利用脑电信号检测癫痫发作。首先,我们定制了一个一维残差神经网络(ResNet),以巧妙地提取脑电信号的局部空间特征。然后,将获得的特征输入双向长短期记忆(BiLSTM)层,以模拟时间相关性。这些输出特性通过两个全连接层进一步处理,以实现最终的癫痫发作检测。ResBiLSTM 的性能在波恩大学和坦普尔大学医院(TUH)提供的癫痫发作数据集上进行了评估。在波恩数据集的二元和三元分类中,ResBiLSTM 模型的癫痫发作检测准确率达到 98.88%-100%。在 TUH 癫痫发作语料库 (TUSZ) 数据集上进行的七种癫痫发作类型的发作识别实验结果表明,ResBiLSTM 模型的分类准确率为 95.03%,在 10 倍交叉验证下的加权 F1 分数为 95.03%。这些结果表明,ResBiLSTM 的表现优于最近几种最先进的深度学习方法。
{"title":"Residual and bidirectional LSTM for epileptic seizure detection.","authors":"Wei Zhao, Wen-Feng Wang, Lalit Mohan Patnaik, Bao-Can Zhang, Su-Jun Weng, Shi-Xiao Xiao, De-Zhi Wei, Hai-Feng Zhou","doi":"10.3389/fncom.2024.1415967","DOIUrl":"10.3389/fncom.2024.1415967","url":null,"abstract":"<p><p>Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1415967"},"PeriodicalIF":2.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11215953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141476309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Synergy quality assessment of muscle modules for determining learning performance using a realistic musculoskeletal model 利用逼真的肌肉骨骼模型对肌肉模块进行协同质量评估,以确定学习成绩
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-05-30 DOI: 10.3389/fncom.2024.1355855
Akito Fukunishi, Kyo Kutsuzawa, Dai Owaki, Mitsuhiro Hayashibe
How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based on muscle synergies can facilitate motor learning without compromising task performance. However, the effectiveness of modularity in motor control remains debated. This ambiguity can, in part, stem from overlooking that the performance of modularity depends on the mechanical aspects of modules of interest, such as the torque the modules exert. To address this issue, this study introduces two criteria to evaluate the quality of module sets based on commonly used performance metrics in motor learning studies: the accuracy of torque production and learning speed. One evaluates the regularity in the direction of mechanical torque the modules exert, while the other evaluates the evenness of its magnitude. For verification of our criteria, we simulated motor learning of torque production tasks in a realistic musculoskeletal system of the upper arm using feed-forward neural networks while changing the control conditions. We found that the proposed criteria successfully explain the tendency of learning performance in various control conditions. These result suggest that regularity in the direction of and evenness in magnitude of mechanical torque of utilized modules are significant factor for determining learning performance. Although the criteria were originally conceived for an error-based learning scheme, the approach to pursue which set of modules is better for motor control can have significant implications in other studies of modularity in general.
我们的中枢神经系统如何有效控制复杂的肌肉骨骼系统仍存在争议。肌肉协同作用假说假定存在协调多块肌肉的功能神经模块,从而简化了这一复杂系统。基于肌肉协同作用的模块化可以促进运动学习,同时又不影响任务的完成。然而,模块化在运动控制中的有效性仍存在争议。这种模糊性可能部分源于忽略了模块化的性能取决于相关模块的机械方面,如模块施加的扭矩。为了解决这个问题,本研究根据运动学习研究中常用的性能指标:扭矩产生的准确性和学习速度,引入了两个标准来评估模块集的质量。其中一个标准评估模块产生机械扭矩方向的规律性,另一个标准评估其大小的均匀性。为了验证我们的标准,我们使用前馈神经网络模拟了上臂真实肌肉骨骼系统的扭矩产生任务的运动学习,同时改变了控制条件。我们发现,所提出的标准成功地解释了各种控制条件下学习成绩的变化趋势。这些结果表明,所使用模块的机械扭矩方向的规律性和大小的均匀性是决定学习成绩的重要因素。虽然这些标准最初是为基于错误的学习方案而设计的,但这种追求哪组模块更适合运动控制的方法对其他一般模块化研究具有重要意义。
{"title":"Synergy quality assessment of muscle modules for determining learning performance using a realistic musculoskeletal model","authors":"Akito Fukunishi, Kyo Kutsuzawa, Dai Owaki, Mitsuhiro Hayashibe","doi":"10.3389/fncom.2024.1355855","DOIUrl":"https://doi.org/10.3389/fncom.2024.1355855","url":null,"abstract":"How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based on muscle synergies can facilitate motor learning without compromising task performance. However, the effectiveness of modularity in motor control remains debated. This ambiguity can, in part, stem from overlooking that the performance of modularity depends on the mechanical aspects of modules of interest, such as the torque the modules exert. To address this issue, this study introduces two criteria to evaluate the quality of module sets based on commonly used performance metrics in motor learning studies: the accuracy of torque production and learning speed. One evaluates the regularity in the direction of mechanical torque the modules exert, while the other evaluates the evenness of its magnitude. For verification of our criteria, we simulated motor learning of torque production tasks in a realistic musculoskeletal system of the upper arm using feed-forward neural networks while changing the control conditions. We found that the proposed criteria successfully explain the tendency of learning performance in various control conditions. These result suggest that regularity in the direction of and evenness in magnitude of mechanical torque of utilized modules are significant factor for determining learning performance. Although the criteria were originally conceived for an error-based learning scheme, the approach to pursue which set of modules is better for motor control can have significant implications in other studies of modularity in general.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"24 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DT-SCNN: dual-threshold spiking convolutional neural network with fewer operations and memory access for edge applications DT-SCNN:双阈值尖峰卷积神经网络,运算和内存访问更少,适用于边缘应用
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-05-30 DOI: 10.3389/fncom.2024.1418115
Fuming Lei, Xu Yang, Jian Liu, Runjiang Dou, Nanjian Wu
The spiking convolutional neural network (SCNN) is a kind of spiking neural network (SNN) with high accuracy for visual tasks and power efficiency on neuromorphic hardware, which is attractive for edge applications. However, it is challenging to implement SCNNs on resource-constrained edge devices because of the large number of convolutional operations and membrane potential (Vm) storage needed. Previous works have focused on timestep reduction, network pruning, and network quantization to realize SCNN implementation on edge devices. However, they overlooked similarities between spiking feature maps (SFmaps), which contain significant redundancy and cause unnecessary computation and storage. This work proposes a dual-threshold spiking convolutional neural network (DT-SCNN) to decrease the number of operations and memory access by utilizing similarities between SFmaps. The DT-SCNN employs dual firing thresholds to derive two similar SFmaps from one Vm map, reducing the number of convolutional operations and decreasing the volume of Vms and convolutional weights by half. We propose a variant spatio-temporal back propagation (STBP) training method with a two-stage strategy to train DT-SCNNs to decrease the inference timestep to 1. The experimental results show that the dual-thresholds mechanism achieves a 50% reduction in operations and data storage for the convolutional layers compared to conventional SCNNs while achieving not more than a 0.4% accuracy loss on the CIFAR10, MNIST, and Fashion MNIST datasets. Due to the lightweight network and single timestep inference, the DT-SCNN has the least number of operations compared to previous works, paving the way for low-latency and power-efficient edge applications.
尖峰卷积神经网络(SCNN)是一种尖峰神经网络(SNN),在视觉任务中具有高精确度,在神经形态硬件上具有高能效,对边缘应用很有吸引力。然而,由于需要大量卷积运算和膜电位(Vm)存储,在资源受限的边缘设备上实现 SCNN 是一项挑战。以前的工作主要集中在减少时间步长、网络剪枝和网络量化上,以实现在边缘设备上实施 SCNN。然而,他们忽略了尖峰特征图(SFmaps)之间的相似性,这些特征图包含大量冗余,会造成不必要的计算和存储。本研究提出了一种双阈值尖峰卷积神经网络(DT-SCNN),通过利用 SFmaps 之间的相似性来减少运算次数和内存访问。DT-SCNN 采用双发射阈值,从一个 Vm 映射中推导出两个相似的 SF 映射,从而减少了卷积操作的数量,并将 Vm 和卷积权重的体积减少了一半。实验结果表明,与传统 SCNN 相比,双阈值机制减少了卷积层 50% 的操作和数据存储,同时在 CIFAR10、MNIST 和时尚 MNIST 数据集上的准确率损失不超过 0.4%。由于采用了轻量级网络和单时间步推理,DT-SCNN 的操作次数与以前的作品相比最少,为低延迟、高能效的边缘应用铺平了道路。
{"title":"DT-SCNN: dual-threshold spiking convolutional neural network with fewer operations and memory access for edge applications","authors":"Fuming Lei, Xu Yang, Jian Liu, Runjiang Dou, Nanjian Wu","doi":"10.3389/fncom.2024.1418115","DOIUrl":"https://doi.org/10.3389/fncom.2024.1418115","url":null,"abstract":"The spiking convolutional neural network (SCNN) is a kind of spiking neural network (SNN) with high accuracy for visual tasks and power efficiency on neuromorphic hardware, which is attractive for edge applications. However, it is challenging to implement SCNNs on resource-constrained edge devices because of the large number of convolutional operations and membrane potential (Vm) storage needed. Previous works have focused on timestep reduction, network pruning, and network quantization to realize SCNN implementation on edge devices. However, they overlooked similarities between spiking feature maps (SFmaps), which contain significant redundancy and cause unnecessary computation and storage. This work proposes a dual-threshold spiking convolutional neural network (DT-SCNN) to decrease the number of operations and memory access by utilizing similarities between SFmaps. The DT-SCNN employs dual firing thresholds to derive two similar SFmaps from one Vm map, reducing the number of convolutional operations and decreasing the volume of Vms and convolutional weights by half. We propose a variant spatio-temporal back propagation (STBP) training method with a two-stage strategy to train DT-SCNNs to decrease the inference timestep to 1. The experimental results show that the dual-thresholds mechanism achieves a 50% reduction in operations and data storage for the convolutional layers compared to conventional SCNNs while achieving not more than a 0.4% accuracy loss on the CIFAR10, MNIST, and Fashion MNIST datasets. Due to the lightweight network and single timestep inference, the DT-SCNN has the least number of operations compared to previous works, paving the way for low-latency and power-efficient edge applications.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"15 10 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Simulated dynamical transitions in a heterogeneous marmoset pFC cluster 模拟异构狨猴 pFC 集群的动态转变
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-05-28 DOI: 10.3389/fncom.2024.1398898
Bernard A. Pailthorpe
Network analysis of the marmoset cortical connectivity data indicates a significant 3D cluster in and around the pre-frontal cortex. A multi-node, heterogeneous neural mass model of this six-node cluster was constructed. Its parameters were informed by available experimental and simulation data so that each neural mass oscillated in a characteristic frequency band. Nodes were connected with directed, weighted links derived from the marmoset structural connectivity data. Heterogeneity arose from the different link weights and model parameters for each node. Stimulation of the cluster with an incident pulse train modulated in the standard frequency bands induced a variety of dynamical state transitions that lasted in the range of 5–10 s, suggestive of timescales relevant to short-term memory. A short gamma burst rapidly reset the beta-induced transition. The theta-induced transition state showed a spontaneous, delayed reset to the resting state. An additional, continuous gamma wave stimulus induced a new beating oscillatory state. Longer or repeated gamma bursts were phase-aligned with the beta oscillation, delivering increasing energy input and causing shorter transition times. The relevance of these results to working memory is yet to be established, but they suggest interesting opportunities.
对狨猴皮层连接数据的网络分析表明,在前额叶皮层及其周围有一个重要的三维集群。我们为这个六节点群构建了一个多节点、异质神经块模型。该模型的参数参考了现有的实验和模拟数据,因此每个神经块都在一个特征频率带内振荡。根据狨猴结构连通性数据得出的有向、加权链接将节点连接起来。每个节点的链接权重和模型参数不同,因此会产生异质性。用标准频带调制的入射脉冲串刺激集群,会诱发各种动态状态转换,持续时间在 5-10 秒之间,这表明了与短时记忆相关的时间尺度。一个短伽玛脉冲串迅速重置了贝塔诱导的转换。θ诱导的过渡状态显示出一种自发的、延迟的重置静息状态。额外的、持续的伽玛波刺激会诱发新的跳动振荡状态。较长或重复的伽玛脉冲与β振荡相位对齐,提供了更多的能量输入,并缩短了过渡时间。这些结果与工作记忆的相关性尚待确定,但它们暗示了有趣的机会。
{"title":"Simulated dynamical transitions in a heterogeneous marmoset pFC cluster","authors":"Bernard A. Pailthorpe","doi":"10.3389/fncom.2024.1398898","DOIUrl":"https://doi.org/10.3389/fncom.2024.1398898","url":null,"abstract":"Network analysis of the marmoset cortical connectivity data indicates a significant 3D cluster in and around the pre-frontal cortex. A multi-node, heterogeneous neural mass model of this six-node cluster was constructed. Its parameters were informed by available experimental and simulation data so that each neural mass oscillated in a characteristic frequency band. Nodes were connected with directed, weighted links derived from the marmoset structural connectivity data. Heterogeneity arose from the different link weights and model parameters for each node. Stimulation of the cluster with an incident pulse train modulated in the standard frequency bands induced a variety of dynamical state transitions that lasted in the range of 5–10 s, suggestive of timescales relevant to short-term memory. A short gamma burst rapidly reset the beta-induced transition. The theta-induced transition state showed a spontaneous, delayed reset to the resting state. An additional, continuous gamma wave stimulus induced a new beating oscillatory state. Longer or repeated gamma bursts were phase-aligned with the beta oscillation, delivering increasing energy input and causing shorter transition times. The relevance of these results to working memory is yet to be established, but they suggest interesting opportunities.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"11 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141167607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Frontiers in Computational Neuroscience
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1