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Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories 开发海马神经假体,促进人类对刺激特征和类别的记忆编码和回忆
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-02-08 DOI: 10.3389/fncom.2024.1263311
Brent M. Roeder, Xiwei She, Alexander S. Dakos, Bryan Moore, Robert T. Wicks, Mark R. Witcher, Daniel E. Couture, Adrian W. Laxton, Heidi Munger Clary, Gautam Popli, Charles Liu, Brian Lee, Christianne Heck, George Nune, Hui Gong, Susan Shaw, Vasilis Z. Marmarelis, Theodore W. Berger, Sam A. Deadwyler, Dong Song, Robert E. Hampson
ObjectiveHere, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject’s own hippocampal spatiotemporal neural codes for memory.ApproachWe constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of targeted content into short-term memory. A memory decoding model (MDM) of hippocampal CA3 and CA1 neural firing was computed which derives a stimulation pattern for CA1 and CA3 neurons to be applied during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task.Main resultsMDM electrical stimulation delivered to the CA1 and CA3 locations in the hippocampus during the sample phase of DMS trials facilitated memory of images from the DMS task during a delayed recognition (DR) task that also included control images that were not from the DMS task. Across all subjects, the stimulated trials exhibited significant changes in performance in 22.4% of patient and category combinations. Changes in performance were a combination of both increased memory performance and decreased memory performance, with increases in performance occurring at almost 2 to 1 relative to decreases in performance. Across patients with impaired memory that received bilateral stimulation, significant changes in over 37.9% of patient and category combinations was seen with the changes in memory performance show a ratio of increased to decreased performance of over 4 to 1. Modification of memory performance was dependent on whether memory function was intact or impaired, and if stimulation was applied bilaterally or unilaterally, with nearly all increase in performance seen in subjects with impaired memory receiving bilateral stimulation.SignificanceThese results demonstrate that memory encoding in patients with impaired memory function can be facilitated for specific memory content, which offers a stimulation method for a future implantable neural prosthetic to improve human memory.
目的在这里,我们首次成功地利用静态神经刺激模式来刺激特定的信息内容。方法我们构建了一个新模型,该模型描述了海马通过神经组合的时空发射对特定记忆项目进行编码的过程,这是成功将目标内容编码到短时记忆的基础。通过计算海马CA3和CA1神经发射的记忆解码模型(MDM),得出了在延迟匹配到样本(DMS)人类短时记忆任务的编码(样本)阶段对CA1和CA3神经元的刺激模式。主要结果 在DMS试验的取样阶段对海马CA1和CA3位置进行MDM电刺激,有助于在延迟识别(DR)任务中对DMS任务中的图像进行记忆,该任务还包括非DMS任务中的对照图像。在所有受试者中,有 22.4% 的患者和类别组合在受刺激试验中表现出显著的成绩变化。成绩的变化既包括记忆成绩的提高,也包括记忆成绩的降低,成绩的提高与降低的比例几乎为 2:1。在接受双侧刺激的记忆受损患者中,超过 37.9% 的患者和类别组合的记忆表现发生了显著变化,记忆表现的提高和降低比例超过 4:1。记忆能力的改变取决于记忆功能是完好还是受损,以及是双侧还是单侧刺激,几乎所有记忆受损的受试者在接受双侧刺激后记忆能力都有所提高。
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引用次数: 0
Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson's disease at the subject-level 利用[18F]-FDG PET对中脑代谢低下进行随机森林分析,在受试者层面识别帕金森病
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-02-07 DOI: 10.3389/fncom.2024.1328699
Marina C. Ruppert-Junck, Gunter Kräling, Andrea Greuel, Marc Tittgemeyer, Lars Timmermann, Alexander Drzezga, Carsten Eggers, David Pedrosa
Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD-patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG) PET scans. Longitudinal analyses confirmed progressive metabolic changes in this region and, an independent study showed great potential of nigral metabolism for diagnostic workup of parkinsonian syndromes. In this study, we applied a machine learning approach to evaluate midbrain metabolism measured by [18F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-patients and 16 healthy control subjects underwent high-resolution [18F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The random forest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [18F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease.
目前,帕金森病(PD)的诊断主要依靠专家判断,神经影像学检查只是辅助工具。在最近的一项研究中,我们根据[18F]-氟脱氧葡萄糖([18F]-FDG)正电子发射计算机断层扫描的分组比较,确定了包括部分黑质在内的低代谢中脑群是区分帕金森病患者的最佳代谢特征。一项独立研究表明,黑质代谢在帕金森综合征的诊断工作中具有巨大潜力。在本研究中,我们采用了一种机器学习方法来评估用 [18F]-FDG PET 测量的中脑代谢,将其作为帕金森病的诊断标志物。共有 51 名中期帕金森病患者和 16 名健康对照受试者接受了高分辨率 [18F]-FDG PET 扫描。通过组间比较确定的中脑群的归一化示踪剂更新值从个人扫描中逐个体素提取。提取的摄取值采用随机森林特征分类算法。为了测试模型在区分患者和对照组方面的稳健性,采用了一种经过调整的 "留一弃一 "交叉验证方法。通过计算验证数据集的灵敏度、特异性和模型准确性,以及测试数据集正确分类受试者的百分比,评估了模型在所有运行中的性能。在验证数据集中,基于中脑簇体素摄取值的随机森林特征分类能识别出患者,平均灵敏度为 0.91(最低:0.82,最高:0.94)。在所有 67 次运行中,每个人都被作为测试数据集处理一次,测试数据集被我们的模型正确分类。所应用的特征重要性提取方法在所有运行中都一致识别出了中脑集群中重要性最高的一个体素子集,该子集在空间上与左侧黑质趋于一致。我们的数据表明,[18F]-FDG PET 测量的中脑代谢是一种很有前景的诊断帕金森病的成像工具。鉴于其与帕金森病病理生理学的密切关系和极高的鉴别准确性,这种方法有助于客观化帕金森病的诊断,并能在临床试验中进行更准确的分类,这也适用于前驱期疾病患者。
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引用次数: 0
Football referee gesture recognition algorithm based on YOLOv8s 基于 YOLOv8s 的足球裁判手势识别算法
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-02-07 DOI: 10.3389/fncom.2024.1341234
Zhiyuan Yang, Yuanyuan Shen, Yanfei Shen

Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees’ gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model’s attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.

手势是人与人之间以及人与机器之间交流的重要手段。在足球比赛中,裁判员通过手势传递判断信息。由于裁判手势的多样性和复杂性以及球员、观众和摄像机角度等干扰因素,足球裁判手势自动识别(FRGR)已成为一项具有挑战性的任务。现有的基于视觉传感器的方法往往无法提供令人满意的性能。为了解决 FRGR 问题,我们开发了基于 YOLOv8s 的深度学习模型。为了解决这些问题,我们整合了三种改进和优化策略。首先,我们采用了全局注意力机制(GAM)来引导模型关注手势,并将背景干扰降至最低。其次,在 YOLOv8s 模型中集成了 P2 检测头结构,以提高远距离检测较小物体的准确性。第三,使用了基于最小点距离交叉联合(MPDIoU)的新损失函数,以有效利用形状相同但大小不同的锚点框。最后,在 1,200 张图像中包含六种手势的数据集上进行了实验。将所提出的方法与 7 种不同的现有模型和 10 种不同的优化模型进行了比较。所提方法的精确率为 89.3%,召回率为 88.9%,mAP@0.5 率为 89.9%,mAP@0.5:0.95 率为 77.3%。这些比率分别比最新的 YOLOv8s 高出约 1.4%、2.0%、1.1% 和 5.4%。所提出的方法在足球比赛的自动手势识别中具有广阔的应用前景。
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引用次数: 0
End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism 基于端到端模型的双关注机制滚装船航线轨迹预测
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-01-31 DOI: 10.3389/fncom.2024.1358437
Licheng Zhao, Yi Zuo, Wenjun Zhang, Tieshan Li, C. L. Philip Chen

With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of prediction models remain critical bottlenecks. Therefore, this article proposes a dual-attention (DA) based end-to-end (E2E) neural network (DAE2ENet) for trajectory prediction. In the E2E structure, long short-term memory (LSTM) units are included for the task of pursuing sequential trajectory data from the encoder layer to the decoder layer. In DA mechanisms, global attention is introduced between the encoder and decoder layers to facilitate interactions between input and output trajectory sequences, and multi-head self-attention is utilized to extract sequential features from the input trajectory. In experiments, we use a ro-ro ship with a fixed navigation route as a case study. Compared with baseline models and benchmark neural networks, DAE2ENet can obtain higher performance on trajectory prediction, and better validation of environmental factors on ship navigation.

随着经济全球化的迅猛发展,航运量的大幅增长导致航道拥堵,为提供有效服务和进行高效管理,有必要进行航迹预测。虽然轨迹预测可以达到较高的精度,但预测模型的性能和泛化仍是关键瓶颈。因此,本文提出了一种基于双注意(DA)的端到端(E2E)神经网络(DAE2ENet)用于轨迹预测。在 E2E 结构中,长短时记忆(LSTM)单元被包含在内,用于执行从编码器层到解码器层的顺序轨迹数据的任务。在 DA 机制中,编码器层和解码器层之间引入了全局注意力,以促进输入和输出轨迹序列之间的互动,并利用多头自我注意力从输入轨迹中提取序列特征。在实验中,我们使用一艘具有固定导航路线的滚装船作为案例研究。与基线模型和基准神经网络相比,DAE2ENet 能够获得更高的轨迹预测性能,并能更好地验证环境因素对船舶航行的影响。
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引用次数: 0
Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas 时变广义线性模型:高级视觉区域神经元动态的特征描述与解码
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-01-29 DOI: 10.3389/fncom.2024.1273053
Geyu Weng, Kelsey Clark, Amir Akbarian, Behrad Noudoost, Neda Nategh
To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors’ contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.
为了创建与行为相关的视觉世界表征,高级视觉区域的神经元会表现出动态响应变化,以解释外部因素(如视觉输入)和内部因素(如奖励值)之间随时间变化的相互作用。由此产生的高维表征空间给精确量化单个因素对行为过程中感官信息的表征和读出所做的贡献带来了挑战。广泛使用的点过程广义线性模型(GLM)方法提供了一个强大的框架,可在单次试验和单个神经元的水平上,将神经元处理过程作为各种感觉和非感觉输入(编码)的函数进行定量描述,并将特定的反应成分与特定的行为(解码)联系起来。然而,大多数现有的 GLMs 变体都假定神经系统是时间不变的,因此不足以模拟高级视觉区域神经元灵敏度的非平稳特性。在这篇综述中,我们总结了一些现有的 GLM 变体,重点是时变扩展。我们将重点介绍它们在理解高级视觉区域神经表征、解码瞬时神经元敏感性以及通过操纵模型成分将生理学与行为学联系起来等方面的应用。这一类时变统计模型为了解高级视觉区域各种视觉行为的神经基础提供了宝贵的见解,并为揭示大脑不同区域各种行为的神经元处理的基本计算原理提供了巨大的潜力。
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引用次数: 0
Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks 利用神经启发人工智能加速器在 6G 网络中实现高速计算
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-01-29 DOI: 10.3389/fncom.2024.1345644
Chunxiao Lin, Muhammad Farhan Azmine, Yibin Liang, Yang Yi

The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.

随着 6G 技术的出现,无线通信领域正被推向新的极限。这种先进的技术要求大幅提高数据传输速率和处理速度,同时还要求高能效的解决方案符合现实世界的实用性。在这项工作中,我们将神经科学启发的机器学习模型--回声状态网络(ESN)--应用于大规模 MIMO-OFDM 系统(6G 网络的关键技术)中的符号检测这一关键任务。我们的工作包括设计一种硬件加速的贮存神经元架构,以加速基于 ESN 的符号检测器。然后,通过在 Xilinx Virtex-7 FPGA 板上进行实际应用场景的概念验证,对该设计进行了验证。实验结果表明,与线性最小均方误差等传统 MIMO 符号检测方法相比,我们的符号检测器设计在各种 MIMO 配置下都具有出色的性能和可扩展性。我们的研究结果还证实了整个系统的性能和可行性,这体现在低误码率、低资源利用率和高吞吐量上。
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引用次数: 0
Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario 人工神经网络模型:在互动场景中实施基于功能性近红外光谱的自发谎言检测
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-01-24 DOI: 10.3389/fncom.2023.1286664
M. Raheel Bhutta, Muhammad Umair Ali, Amad Zafar, Kwang Su Kim, Jong Hyuk Byun, Seung Won Lee
Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to identify deception using the fNIRS system. A card game frequently referred to as a bluff or cheat was introduced. This game was selected because its rules are ideal for testing our hypotheses. The optical probe of the fNIRS was placed on the subject’s forehead, and we acquired optical density signals, which were then converted into oxy-hemoglobin and deoxy-hemoglobin signals using the Modified Beer–Lambert law. The oxy-hemoglobin signal was preprocessed to eliminate noise. In this study, we proposed three artificial neural networks inspired by deep learning models, including AlexNet, ResNet, and GoogleNet, to classify deception and truth-telling. The proposed models achieved accuracies of 88.5%, 88.0%, and 90.0%, respectively. These proposed models were compared with other classification models, including k-nearest neighbor, linear support vector machines (SVM), quadratic SVM, cubic SVM, simple decision trees, and complex decision trees. These comparisons showed that the proposed models performed better than the other state-of-the-art methods.
欺骗是日常生活中不可避免的现象。人们使用各种方法来了解大脑欺骗的内在机制。此外,人们还在检测欺骗和讲真话方面做出了许多努力。与其他最先进的方法相比,功能性近红外光谱(fNIRS)在神经学应用方面具有巨大的潜力。因此,本研究采用了基于 fNIRS 的自发谎言检测模型。我们采访了 10 名健康受试者,利用 fNIRS 系统识别欺骗行为。我们介绍了一种经常被称为 "虚张声势 "或 "作弊 "的纸牌游戏。之所以选择这个游戏,是因为其规则非常适合测试我们的假设。我们将 fNIRS 的光学探头置于受试者的前额,然后获取光密度信号,再利用修正比尔-朗伯定律将其转换为氧合血红蛋白和脱氧血红蛋白信号。氧合血红蛋白信号经过预处理以消除噪声。在这项研究中,我们提出了三种受深度学习模型启发的人工神经网络,包括 AlexNet、ResNet 和 GoogleNet,用于对欺骗和讲真话进行分类。所提模型的准确率分别达到了 88.5%、88.0% 和 90.0%。我们将这些建议的模型与其他分类模型进行了比较,包括 k-近邻、线性支持向量机 (SVM)、二次 SVM、三次 SVM、简单决策树和复杂决策树。这些比较结果表明,所提出的模型比其他最先进的方法表现得更好。
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引用次数: 0
An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management 一种高效的蜂群智能方法,用于优化具有跨维约束条件的高维解决方案,并应用于供应链管理
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-01-18 DOI: 10.3389/fncom.2024.1283974
Hsin-Ping Liu, Frederick Kin Hing Phoa, Yun-Heh Chen-Burger, Shau-Ping Lin
IntroductionThe Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the complexities caused by the high-dimensional problems and cross-dimensional constraints.MethodIn this work, we introduce an extension of the SIB method that can simultaneously tackle these two challenges. To pursue faster computing, CPU parallelization techniques are employed for algorithm acceleration.ResultsThe performance of the SIB method is examined on the problems of designing selling schemes in different scales. It outperforms the Genetic Algorithm (GA) in terms of both the speed of convergence and the optimized capacity as measured using improvement multipliers.
引言 基于蜂群智能(SIB)的方法已被广泛应用于许多具有离散解域的高效优化领域。电子商务提高了设计合适的销售策略(包括渠道销售和直接销售以及它们之间的组合)的重要性,但由于高维问题和跨维约束带来的复杂性,该领域的研究人员很少在优化问题中采用先进的元启发式技术。为了追求更快的计算速度,我们采用了 CPU 并行化技术来加速算法。在收敛速度和优化容量方面,该方法都优于遗传算法(GA)。
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引用次数: 0
Information representation in an oscillating neural field model modulated by working memory signals 受工作记忆信号调制的振荡神经场模型中的信息表征
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-01-18 DOI: 10.3389/fncom.2023.1253234
William H. Nesse, Kelsey L. Clark, Behrad Noudoost
We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity—a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point—a stimulus feature—on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation—the phase of the spiking activity. From these two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of our model have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to our model. Our model results suggest a mechanism by which WM signals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity.
我们研究了如何在神经活动振荡模型的动态特征中体现刺激信息--该模型的活动可被类似于工作记忆(WM)信号的输入所调节。我们开发了一个神经场模型,该模型在振荡不稳定性附近进行调整,其中类似于工作记忆的输入可以调节振荡的频率和振幅。我们的神经场模型有一个类似空间的域,在这个域中,优先针对域上某一点--刺激特征--的输入会诱发特定特征的活动变化。这些特定特征的活动变化既会影响尖峰的平均速率,也会影响尖峰活动与全局场振荡的相对时间--即尖峰活动的相位。根据这两个动态特征,我们定义了尖峰率代码和振荡相位代码。我们通过信息理论分析评估了这两种代码在区分刺激特征方面的性能。我们发现,全局 WM 输入调制可以增强相位代码的辨别能力,同时降低速率代码的辨别能力。此外,我们还发现,在相同的模型解决方案下,相位编码的性能大约比速率编码的性能大两个数量级。我们的模型结果可应用于大脑的感觉区域,前额叶区域向这些区域发送反映 WM 内容的输入。这些输入到感觉区域的 WM 已被证实会诱发与我们的模型类似的振荡变化。我们的模型结果表明了一种机制,通过这种机制,WM 信号可以增强振荡活动所代表的感官信息,超越基于平均速率活动的相对较弱的表征。
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引用次数: 0
Research on eight machine learning algorithms applicability on different characteristics data sets in medical classification tasks 研究八种机器学习算法在医疗分类任务中对不同特征数据集的适用性
IF 3.2 4区 医学 Q2 Neuroscience Pub Date : 2024-01-15 DOI: 10.3389/fncom.2024.1345575
Yiyan Zhang, Qin Li, Yi Xin

With the vigorous development of data mining field, more and more algorithms have been proposed or improved. How to quickly select a data mining algorithm that is suitable for data sets in medical field is a challenge for some medical workers. The purpose of this paper is to study the comparative characteristics of the general medical data set and the general data sets in other fields, and find the applicability rules of the data mining algorithm suitable for the characteristics of the current research data set. The study quantified characteristics of the research data set with 26 indicators, including simple indicators, statistical indicators and information theory indicators. Eight machine learning algorithms with high maturity, low user involvement and strong family representation were selected as the base algorithms. The algorithm performances were evaluated by three aspects: prediction accuracy, running speed and memory consumption. By constructing decision tree and stepwise regression model to learn the above metadata, the algorithm applicability knowledge of medical data set is obtained. Through cross-verification, the accuracy of all the algorithm applicability prediction models is above 75%, which proves the validity and feasibility of the applicability knowledge.

随着数据挖掘领域的蓬勃发展,越来越多的算法被提出或改进。如何快速选择适合医学领域数据集的数据挖掘算法,是摆在一些医学工作者面前的难题。本文旨在研究一般医学数据集与其他领域一般数据集的比较特征,找到适合当前研究数据集特征的数据挖掘算法的适用性规则。研究用 26 个指标量化了研究数据集的特征,包括简单指标、统计指标和信息论指标。选择了成熟度高、用户参与度低、家族代表性强的 8 种机器学习算法作为基础算法。从预测精度、运行速度和内存消耗三个方面对算法性能进行了评估。通过构建决策树和逐步回归模型来学习上述元数据,从而获得医疗数据集的算法适用性知识。通过交叉验证,所有算法适用性预测模型的准确率都在 75% 以上,证明了适用性知识的有效性和可行性。
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Frontiers in Computational Neuroscience
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