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Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation 强化学习作为昆虫导航的机器人启发框架:从空间表征到神经实现
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-09 DOI: 10.3389/fncom.2024.1460006
Stephan Lochner, Daniel Honerkamp, Abhinav Valada, Andrew D. Straw
Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.
蜜蜂是昆虫世界的导航大师。尽管机器人导航研究取得了令人瞩目的进展,但就训练效率和泛化能力而言,这些昆虫的表现仍然是任何人工系统无法比拟的,特别是考虑到有限的计算能力。另一方面,人们对这些非凡壮举背后的计算原理仍然只有部分了解。强化学习(RL)的理论框架提供了一个理想的焦点,可将这两个领域结合起来,互惠互利。特别是,我们通过 RL 的视角来分析和比较机器人和昆虫导航模型中的空间表征,因为昆虫导航的效率很可能源于一种高效而强大的内部表征,它将视网膜(以自我为中心)的视觉输入与环境的几何形状联系在一起。虽然 RL 长期以来一直是机器人导航研究的核心,但目前昆虫导航的计算理论并不常见于这一框架内,而主要是在昆虫大脑,尤其是蘑菇体(MB)中实施的联想学习过程。在这里,我们提出了蘑菇体电路的具体假定组件,这些组件能够实现某类相对简单的 RL 算法,能够整合导航任务的不同组件,让人联想到机器人导航中使用的分层 RL 模型。我们讨论了当前的昆虫和机器人导航模型是如何探索经典的、完整的地图式表征之外的表征的,空间信息在不同程度上被嵌入到各自的潜在表征中。
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引用次数: 0
Editorial: Understanding the role of oscillations, mutual information and synchronization in perception and action. 社论:了解振荡、相互信息和同步在感知和行动中的作用。
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-04 DOI: 10.3389/fncom.2024.1452001
Andreas Bahmer,Johanna M Rimmele,Daya Shankar Gupta
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引用次数: 0
Capturing biomarkers associated with Alzheimer disease subtypes using data distribution characteristics 利用数据分布特征捕捉与阿尔茨海默病亚型相关的生物标记物
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-03 DOI: 10.3389/fncom.2024.1388504
Kenneth Smith, Sharlee Climer
Late-onset Alzheimer disease (AD) is a highly complex disease with multiple subtypes, as demonstrated by its disparate risk factors, pathological manifestations, and clinical traits. Discovery of biomarkers to diagnose specific AD subtypes is a key step towards understanding biological mechanisms underlying this enigmatic disease, generating candidate drug targets, and selecting participants for drug trials. Popular statistical methods for evaluating candidate biomarkers, fold change (FC) and area under the receiver operating characteristic curve (AUC), were designed for homogeneous data and we demonstrate the inherent weaknesses of these approaches when used to evaluate subtypes representing less than half of the diseased cases. We introduce a unique evaluation metric that is based on the distribution of the values, rather than the magnitude of the values, to identify analytes that are associated with a subset of the diseased cases, thereby revealing potential biomarkers for subtypes. Our approach, Bimodality Coefficient Difference (BCD), computes the difference between the degrees of bimodality for the cases and controls. We demonstrate the effectiveness of our approach with large-scale synthetic data trials containing nearly perfect subtypes. In order to reveal novel AD biomarkers for heterogeneous subtypes, we applied BCD to gene expression data for 8,650 genes for 176 AD cases and 187 controls. Our results confirm the utility of BCD for identifying subtypes of heterogeneous diseases.
晚发性阿尔茨海默病(AD)是一种高度复杂的疾病,具有多种亚型,其风险因素、病理表现和临床特征各不相同。发现诊断特定阿尔茨海默病亚型的生物标志物是了解这种神秘疾病的生物机制、产生候选药物靶点和选择药物试验参与者的关键一步。评估候选生物标记物的常用统计方法--折叠变化(FC)和接收者工作特征曲线下面积(AUC)--是针对同质数据设计的,我们证明了这些方法在用于评估占患病病例不到一半的亚型时存在固有的缺陷。我们引入了一种独特的评估指标,它基于值的分布而不是值的大小,以确定与患病病例子集相关的分析物,从而揭示亚型的潜在生物标记物。我们的方法--双峰系数差(BCD)--计算病例和对照组的双峰程度之差。我们用包含近乎完美亚型的大规模合成数据试验证明了我们方法的有效性。为了揭示异质性亚型的新型 AD 生物标记物,我们将 BCD 应用于 176 例 AD 病例和 187 例对照的 8650 个基因的基因表达数据。我们的结果证实了 BCD 在识别异质性疾病亚型方面的实用性。
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引用次数: 0
Nonlinear analysis of neuronal firing modulated by sinusoidal stimulation at axons in rat hippocampus 大鼠海马轴突受到正弦波刺激时神经元发射调制的非线性分析
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-30 DOI: 10.3389/fncom.2024.1388224
Yue Yuan, Xiangyu Ye, Jian Cui, Junyang Zhang, Zhaoxiang Wang
IntroductionElectrical stimulation of the brain has shown promising prospects in treating various brain diseases. Although biphasic pulse stimulation remains the predominant clinical approach, there has been increasing interest in exploring alternative stimulation waveforms, such as sinusoidal stimulation, to improve the effectiveness of brain stimulation and to expand its application to a wider range of brain disorders. Despite this growing attention, the effects of sinusoidal stimulation on neurons, especially on their nonlinear firing characteristics, remains unclear.MethodsTo address the question, 50 Hz sinusoidal stimulation was applied on Schaffer collaterals of the rat hippocampal CA1 region in vivo. Single unit activity of both pyramidal cells and interneurons in the downstream CA1 region was recorded and analyzed. Two fractal indexes, namely the Fano factor and Hurst exponent, were used to evaluate changes in the long-range correlations, a manifestation of nonlinear dynamics, in spike sequences of neuronal firing.ResultsThe results demonstrate that sinusoidal electrical stimulation increased the firing rates of both pyramidal cells and interneurons, as well as altered their firing to stimulation-related patterns. Importantly, the sinusoidal stimulation increased, rather than decreased the scaling exponents of both Fano factor and Hurst exponent, indicating an increase in the long-range correlations of both pyramidal cells and interneurons.DiscussionThe results firstly reported that periodic sinusoidal stimulation without long-range correlations can increase the long-range correlations of neurons in the downstream post-synaptic area. These results provide new nonlinear mechanisms of brain sinusoidal stimulation and facilitate the development of new stimulation modes.
导言脑电刺激在治疗各种脑部疾病方面前景广阔。虽然双相脉冲刺激仍是临床上的主要方法,但人们越来越有兴趣探索其他刺激波形,如正弦波刺激,以提高脑刺激的效果,并将其应用扩展到更广泛的脑部疾病。为了解决这个问题,研究人员在体内对大鼠海马 CA1 区的沙弗副神经进行了 50 赫兹的正弦波刺激。记录并分析了下游 CA1 区锥体细胞和中间神经元的单细胞活动。结果表明,正弦波电刺激提高了锥体细胞和中间神经元的发射率,并改变了它们的发射模式,使之与刺激相关。讨论结果首次报道了无长程相关性的周期性正弦波刺激可以增加下游突触后区域神经元的长程相关性。这些结果为脑部正弦波刺激提供了新的非线性机制,促进了新刺激模式的发展。
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引用次数: 0
Bursting gamma oscillations in neural mass models 神经质量模型中的迸发伽马振荡
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-30 DOI: 10.3389/fncom.2024.1422159
Manoj Kumar Nandi, Michele Valla, Matteo di Volo
Gamma oscillations (30–120 Hz) in the brain are not periodic cycles, but they typically appear in short-time windows, often called oscillatory bursts. While the origin of this bursting phenomenon is still unclear, some recent studies hypothesize its origin in the external or endogenous noise of neural networks. We demonstrate that an exact neural mass model of excitatory and inhibitory quadratic-integrate and fire-spiking neurons theoretically predicts the emergence of a different regime of intrinsic bursting gamma (IBG) oscillations without any noise source, a phenomenon due to collective chaos. This regime is indeed observed in the direct simulation of spiking neurons, characterized by highly irregular spiking activity. IBG oscillations are distinguished by higher phase-amplitude coupling to slower theta oscillations concerning noise-induced bursting oscillations, thus indicating an increased capacity for information transfer between brain regions. We demonstrate that this phenomenon is present in both globally coupled and sparse networks of spiking neurons. These results propose a new mechanism for gamma oscillatory activity, suggesting deterministic collective chaos as a good candidate for the origin of gamma bursts.
大脑中的γ振荡(30-120赫兹)并不是周期性的,但它们通常出现在短时间窗口中,通常被称为振荡猝发。虽然这种猝发现象的起源尚不清楚,但最近的一些研究假设其起源于神经网络的外部或内源性噪声。我们证明,一个由兴奋性和抑制性二次积分和火刺神经元组成的精确神经质量模型,从理论上预测了在没有任何噪声源的情况下,会出现不同的内在伽马猝发(IBG)振荡机制,这是一种集体混沌现象。在对尖峰神经元的直接模拟中确实观察到了这种机制,其特点是尖峰活动极不规则。IBG 振荡的特点是与噪声诱发的猝发振荡有关的较慢的 Theta 振荡具有更高的相位-振幅耦合,从而表明大脑区域之间的信息传递能力增强。我们证明,这种现象在全局耦合和稀疏的尖峰神经元网络中都存在。这些结果为伽马振荡活动提出了一种新的机制,表明确定性集体混沌是伽马猝发起源的一个很好的候选者。
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引用次数: 0
Quantifying network behavior in the rat prefrontal cortex 量化大鼠前额叶皮层的网络行为
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-29 DOI: 10.3389/fncom.2024.1293279
Congzhou M. Sha, Jian Wang, Richard B. Mailman, Yang Yang, Nikolay V. Dokholyan
The question of how consciousness and behavior arise from neural activity is fundamental to understanding the brain, and to improving the diagnosis and treatment of neurological and psychiatric disorders. There is significant murine and primate literature on how behavior is related to the electrophysiological activity of the medial prefrontal cortex and its role in working memory processes such as planning and decision-making. Existing experimental designs, specifically the rodent spike train and local field potential recordings during the T-maze alternation task, have insufficient statistical power to unravel the complex processes of the prefrontal cortex. We therefore examined the theoretical limitations of such experiments, providing concrete guidelines for robust and reproducible science. To approach these theoretical limits, we applied dynamic time warping and associated statistical tests to data from neuron spike trains and local field potentials. The goal was to quantify neural network synchronicity and the correlation of neuroelectrophysiology with rat behavior. The results show the statistical limitations of existing data, and the fact that making meaningful comparison between dynamic time warping with traditional Fourier and wavelet analysis is impossible until larger and cleaner datasets are available.
意识和行为是如何从神经活动中产生的,这个问题对于理解大脑、改善神经和精神疾病的诊断和治疗至关重要。关于行为如何与内侧前额叶皮层的电生理活动及其在计划和决策等工作记忆过程中的作用相关,已有大量关于小鼠和灵长类动物的文献。现有的实验设计,特别是啮齿类动物在 T 迷宫交替任务中的尖峰序列和局部场电位记录,没有足够的统计能力来揭示前额叶皮层的复杂过程。因此,我们研究了此类实验的理论限制,为稳健、可重复的科学研究提供了具体指导。为了接近这些理论限制,我们对神经元尖峰列车和局部场电位数据进行了动态时间扭曲和相关统计检验。目的是量化神经网络的同步性以及神经电生理学与大鼠行为的相关性。结果表明了现有数据在统计方面的局限性,以及在获得更大、更清晰的数据集之前,不可能将动态时间扭曲与传统的傅里叶和小波分析进行有意义的比较。
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引用次数: 0
Classification of epileptic seizures in EEG data based on iterative gated graph convolution network 基于迭代门控图卷积网络的脑电图数据中的癫痫发作分类
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-29 DOI: 10.3389/fncom.2024.1454529
Yue Hu, Jian Liu, Rencheng Sun, Yongqiang Yu, Yi Sui
IntroductionThe automatic and precise classification of epilepsy types using electroencephalogram (EEG) data promises significant advancements in diagnosing patients with epilepsy. However, the intricate interplay among multiple electrode signals in EEG data poses challenges. Recently, Graph Convolutional Neural Networks (GCN) have shown strength in analyzing EEG data due to their capability to describe complex relationships among different EEG regions. Nevertheless, several challenges remain: (1) GCN typically rely on predefined or prior graph topologies, which may not accurately reflect the complex correlations between brain regions. (2) GCN struggle to capture the long-temporal dependencies inherent in EEG signals, limiting their ability to effectively extract temporal features.MethodsTo address these challenges, we propose an innovative epileptic seizure classification model based on an Iterative Gated Graph Convolutional Network (IGGCN). For the epileptic seizure classification task, the original EEG graph structure is iteratively optimized using a multi-head attention mechanism during training, rather than relying on a static, predefined prior graph. We introduce Gated Graph Neural Networks (GGNN) to enhance the model's capacity to capture long-term dependencies in EEG series between brain regions. Additionally, Focal Loss is employed to alleviate the imbalance caused by the scarcity of epileptic EEG data.ResultsOur model was evaluated on the Temple University Hospital EEG Seizure Corpus (TUSZ) for classifying four types of epileptic seizures. The results are outstanding, achieving an average F1 score of 91.5% and an average Recall of 91.8%, showing a substantial improvement over current state-of-the-art models.DiscussionAblation experiments verified the efficacy of iterative graph optimization and gated graph convolution. The optimized graph structure significantly differs from the predefined EEG topology. Gated graph convolutions demonstrate superior performance in capturing the long-term dependencies in EEG series. Additionally, Focal Loss outperforms other commonly used loss functions in the TUSZ classification task.
导言:利用脑电图(EEG)数据对癫痫类型进行自动和精确的分类,有望在诊断癫痫患者方面取得重大进展。然而,脑电图数据中多个电极信号之间错综复杂的相互作用带来了挑战。最近,图卷积神经网络(Graph Convolutional Neural Networks,GCN)在分析脑电图数据方面显示出了优势,因为它能够描述不同脑电图区域之间的复杂关系。然而,仍然存在以下几个挑战:(1)GCN 通常依赖于预定义或先验的图拓扑结构,这可能无法准确反映大脑区域之间的复杂关联。(2) GCN 难以捕捉脑电信号固有的长时空依赖性,从而限制了其有效提取时空特征的能力。为了应对这些挑战,我们提出了一种基于迭代门控图卷积网络(IGGCN)的创新性癫痫发作分类模型。针对癫痫发作分类任务,我们在训练过程中使用多头注意机制迭代优化原始脑电图图结构,而不是依赖于静态、预定义的先验图。我们引入了门控图神经网络(GGNN),以增强模型捕捉脑区之间脑电图序列长期依赖关系的能力。此外,我们还采用了 "病灶损失"(Focal Loss)技术来缓解癫痫脑电图数据稀缺所造成的不平衡。结果非常出色,平均 F1 得分为 91.5%,平均 Recall 得分为 91.8%,与目前最先进的模型相比有了大幅提高。讨论消融实验验证了迭代图优化和门控图卷积的功效。优化后的图结构与预定义的脑电图拓扑结构有很大不同。门控图卷积在捕捉脑电图序列的长期依赖性方面表现出卓越的性能。此外,Focal Loss 在 TUSZ 分类任务中的表现优于其他常用损失函数。
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引用次数: 0
The significance of cerebellar contributions in early-life through aging. 从幼年到衰老,小脑的贡献意义重大。
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-27 DOI: 10.3389/fncom.2024.1449364
Jessica L Verpeut,Marlies Oostland
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引用次数: 0
Unified theory of alpha, mu, and tau rhythms via eigenmodes of brain activity 通过大脑活动的特征模式统一阿尔法、缪和陶氏节律理论
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-26 DOI: 10.3389/fncom.2024.1335130
Rawan El-Zghir, Natasha Gabay, Peter Robinson
A compact description of the frequency structure and topography of human alpha-band rhythms is obtained by use of the first four brain activity eigenmodes previously derived from corticothalamic neural field theory. Just two eigenmodes that overlap in frequency are found to reproduce the observed topography of the classical alpha rhythm for subjects with a single, occipitally concentrated alpha peak in their electroencephalograms. Alpha frequency splitting and relative amplitudes of double alpha peaks are explored analytically and numerically within this four-mode framework using eigenfunction expansion and perturbation methods. These effects are found to result primarily from the different eigenvalues and corticothalamic gains corresponding to the eigenmodes. Three modes with two non-overlapping frequencies suffice to reproduce the observed topography for subjects with a double alpha peak, where the appearance of a distinct second alpha peak requires an increase of the corticothalamic gain of higher eigenmodes relative to the first. Conversely, alpha blocking is inferred to be linked to a relatively small attention-dependent reduction of the gain of the relevant eigenmodes, whose effect is enhanced by the near-critical state of the brain and whose sign is consistent with inferences from neural field theory. The topographies and blocking of the mu and tau rhythms within the alpha-band are explained analogously via eigenmodes. Moreover, the observation of three rhythms in the alpha band is due to there being exactly three members of the first family of spatially nonuniform modes. These results thus provide a simple, unified description of alpha band rhythms and enable experimental observations of spectral structure and topography to be linked directly to theory and underlying physiology.
通过使用以前从皮质-丘脑神经场理论推导出的前四种大脑活动特征模式,对人类阿尔法波段节律的频率结构和拓扑结构进行了简洁的描述。研究发现,对于脑电图中只有一个枕部集中α峰的受试者来说,仅有两个频率重叠的特征模态就能再现所观察到的经典α节律拓扑结构。在此四模式框架内,使用特征函数展开和扰动方法对阿尔法频率分裂和双阿尔法峰的相对振幅进行了分析和数值探索。研究发现,这些效应主要源于与特征模式相对应的不同特征值和皮质-丘脑增益。具有两个非重叠频率的三个模式足以再现观察到的具有双阿尔法峰的受试者的地形图,其中第二个阿尔法峰的出现需要较高特征模式的皮质-丘脑增益相对于第一个特征模式有所增加。相反,α阻滞则被推断为与相关特征模态增益相对较小的注意力依赖性降低有关,其效果因大脑接近临界状态而增强,其符号与神经场理论的推断一致。α波段内的μ和tau节律的地形和阻滞也可以通过特征模式得到类似的解释。此外,在阿尔法波段观察到的三种节律是由于空间非均匀模式第一族恰好有三个成员。因此,这些结果为阿尔法波段节律提供了一个简单、统一的描述,并使光谱结构和地形的实验观察结果与理论和基本生理学直接联系起来。
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引用次数: 0
Deep learning for detecting prenatal alcohol exposure in pediatric brain MRI: a transfer learning approach with explainability insights 深度学习检测小儿脑磁共振成像中的产前酒精暴露:一种具有可解释性洞察力的迁移学习方法
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-26 DOI: 10.3389/fncom.2024.1434421
Anik Das, Kaue Duarte, Catherine Lebel, Mariana Bento
Prenatal alcohol exposure (PAE) refers to the exposure of the developing fetus due to alcohol consumption during pregnancy and can have life-long consequences for learning, behavior, and health. Understanding the impact of PAE on the developing brain manifests challenges due to its complex structural and functional attributes, which can be addressed by leveraging machine learning (ML) and deep learning (DL) approaches. While most ML and DL models have been tailored for adult-centric problems, this work focuses on applying DL to detect PAE in the pediatric population. This study integrates the pre-trained simple fully convolutional network (SFCN) as a transfer learning approach for extracting features and a newly trained classifier to distinguish between unexposed and PAE participants based on T1-weighted structural brain magnetic resonance (MR) scans of individuals aged 2–8 years. Among several varying dataset sizes and augmentation strategy during training, the classifier secured the highest sensitivity of 88.47% with 85.04% average accuracy on testing data when considering a balanced dataset with augmentation for both classes. Moreover, we also preliminarily performed explainability analysis using the Grad-CAM method, highlighting various brain regions such as corpus callosum, cerebellum, pons, and white matter as the most important features in the model's decision-making process. Despite the challenges of constructing DL models for pediatric populations due to the brain's rapid development, motion artifacts, and insufficient data, this work highlights the potential of transfer learning in situations where data is limited. Furthermore, this study underscores the importance of preserving a balanced dataset for fair classification and clarifying the rationale behind the model's prediction using explainability analysis.
产前酒精暴露(PAE)是指发育中的胎儿在怀孕期间因饮酒而暴露于酒精中,并可能对学习、行为和健康产生终身影响。由于 PAE 具有复杂的结构和功能属性,因此了解 PAE 对发育中大脑的影响是一项挑战,这可以通过利用机器学习(ML)和深度学习(DL)方法来解决。大多数 ML 和 DL 模型都是针对以成人为中心的问题定制的,而本研究则侧重于应用 DL 检测儿科人群中的 PAE。本研究整合了预先训练好的简单全卷积网络(SFCN),将其作为提取特征的迁移学习方法和新训练的分类器,根据 2 至 8 岁个体的 T1 加权脑结构磁共振(MR)扫描结果来区分未暴露和 PAE 参与者。在几种不同的数据集大小和训练过程中的增强策略中,当考虑对两个类别进行增强的平衡数据集时,分类器在测试数据上获得了最高的灵敏度(88.47%)和平均准确率(85.04%)。此外,我们还使用 Grad-CAM 方法初步进行了可解释性分析,强调了胼胝体、小脑、脑桥和白质等大脑区域是模型决策过程中最重要的特征。尽管由于大脑的快速发育、运动伪影和数据不足等原因,为儿科人群构建 DL 模型面临着挑战,但这项工作凸显了迁移学习在数据有限的情况下的潜力。此外,这项研究还强调了为进行公平分类而保留均衡数据集的重要性,以及利用可解释性分析阐明模型预测背后原理的重要性。
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引用次数: 0
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Frontiers in Computational Neuroscience
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