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Optimizing extubation success: a comparative analysis of time series algorithms and activation functions. 优化拔管成功率:时间序列算法和激活函数的比较分析。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-04 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1456771
Kuo-Yang Huang, Ching-Hsiung Lin, Shu-Hua Chi, Ying-Lin Hsu, Jia-Lang Xu

Background: The success and failure of extubation of patients with acute respiratory failure is a very important issue for clinicians, and the failure of the ventilator often leads to possible complications, which in turn leads to a lot of doubts about the medical treatment in the minds of the people, so in order to increase the success of extubation success of the doctors to prevent the possible complications, the present study compared different time series algorithms and different activation functions for the training and prediction of extubation success or failure models.

Methods: This study compared different time series algorithms and different activation functions for training and predicting the success or failure of the extubation model.

Results: The results of this study using four validation methods show that the GRU model and Tanh's model have a better predictive model for predicting the success or failure of the extubation and better predictive result of 94.44% can be obtained using Holdout cross-validation validation method.

Conclusion: This study proposes a prediction method using GRU on the topic of extubation, and it can provide the doctors with the clinical application of extubation to give advice for reference.

背景:对于临床医生来说,急性呼吸衰竭患者的拔管成功与否是一个非常重要的问题,而呼吸机的失灵往往会导致可能出现的并发症,进而导致人们心中对医疗产生诸多疑虑,因此为了提高医生的拔管成功率,防止可能出现的并发症,本研究比较了不同时间序列算法和不同激活函数对拔管成功或失败模型的训练和预测:本研究比较了用于训练和预测拔管成功或失败模型的不同时间序列算法和不同激活函数:本研究使用四种验证方法的结果表明,GRU 模型和 Tanh's 模型在预测拔管成败方面具有较好的预测模型,使用 Holdout 交叉验证验证方法可获得 94.44% 的较好预测结果:本研究提出了一种以拔管为主题的GRU预测方法,可为医生提供拔管的临床应用建议,以供参考。
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引用次数: 0
Decoding the application of deep learning in neuroscience: a bibliometric analysis. 解码深度学习在神经科学中的应用:文献计量分析。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-04 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1402689
Yin Li, Zilong Zhong

The application of deep learning in neuroscience holds unprecedented potential for unraveling the complex dynamics of the brain. Our bibliometric analysis, spanning from 2012 to 2023, delves into the integration of deep learning in neuroscience, shedding light on the evolutionary trends and identifying pivotal research hotspots. Through the examination of 421 articles, this study unveils a significant growth in interdisciplinary research, marked by the burgeoning application of deep learning techniques in understanding neural mechanisms and addressing neurological disorders. Central to our findings is the critical role of classification algorithms, models, and neural networks in advancing neuroscience, highlighting their efficacy in interpreting complex neural data, simulating brain functions, and translating theoretical insights into practical diagnostics and therapeutic interventions. Additionally, our analysis delineates a thematic evolution, showcasing a shift from foundational methodologies toward more specialized and nuanced approaches, particularly in areas like EEG analysis and convolutional neural networks. This evolution reflects the field's maturation and its adaptation to technological advancements. The study further emphasizes the importance of interdisciplinary collaborations and the adoption of cutting-edge technologies to foster innovation in decoding the cerebral code. The current study provides a strategic roadmap for future explorations, urging the scientific community toward areas ripe for breakthrough discoveries and practical applications. This analysis not only charts the past and present landscape of deep learning in neuroscience but also illuminates pathways for future research, underscoring the transformative impact of deep learning on our understanding of the brain.

深度学习在神经科学中的应用为揭示大脑的复杂动力学提供了前所未有的潜力。我们的文献计量分析跨越 2012 年至 2023 年,深入探讨了深度学习与神经科学的结合,揭示了演变趋势,并确定了关键的研究热点。通过对 421 篇文章的研究,本研究揭示了跨学科研究的显著增长,其标志是深度学习技术在理解神经机制和解决神经系统疾病方面的蓬勃应用。我们研究结果的核心是分类算法、模型和神经网络在推动神经科学发展方面的关键作用,突出了它们在解释复杂神经数据、模拟大脑功能以及将理论见解转化为实际诊断和治疗干预措施方面的功效。此外,我们的分析还勾勒出一个主题演变过程,展示了从基础方法到更专业、更细致的方法的转变,尤其是在脑电图分析和卷积神经网络等领域。这种演变反映了该领域的成熟及其对技术进步的适应。研究进一步强调了跨学科合作和采用尖端技术的重要性,以促进解码大脑密码的创新。当前的研究为未来的探索提供了一个战略路线图,敦促科学界朝着突破性发现和实际应用成熟的领域迈进。这项分析不仅描绘了神经科学领域深度学习的过去和现在,还为未来研究指明了道路,强调了深度学习对我们理解大脑的变革性影响。
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引用次数: 0
Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II. 社论:理解和弥合神经形态计算与机器学习之间的差距》,第二卷。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-10-03 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1455530
Lei Deng, Huajin Tang, Kaushik Roy
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引用次数: 0
Multi-label remote sensing classification with self-supervised gated multi-modal transformers. 利用自监督门控多模式转换器进行多标签遥感分类。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-24 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1404623
Na Liu, Ye Yuan, Guodong Wu, Sai Zhang, Jie Leng, Lihong Wan

Introduction: With the great success of Transformers in the field of machine learning, it is also gradually attracting widespread interest in the field of remote sensing (RS). However, the research in the field of remote sensing has been hampered by the lack of large labeled data sets and the inconsistency of data modes caused by the diversity of RS platforms. With the rise of self-supervised learning (SSL) algorithms in recent years, RS researchers began to pay attention to the application of "pre-training and fine-tuning" paradigm in RS. However, there are few researches on multi-modal data fusion in remote sensing field. Most of them choose to use only one of the modal data or simply splice multiple modal data roughly.

Method: In order to study a more efficient multi-modal data fusion scheme, we propose a multi-modal fusion mechanism based on gated unit control (MGSViT). In this paper, we pretrain the ViT model based on BigEarthNet dataset by combining two commonly used SSL algorithms, and propose an intra-modal and inter-modal gated fusion unit for feature learning by combining multispectral (MS) and synthetic aperture radar (SAR). Our method can effectively combine different modal data to extract key feature information.

Results and discussion: After fine-tuning and comparison experiments, we outperform the most advanced algorithms in all downstream classification tasks. The validity of our proposed method is verified.

导言:随着变形金刚在机器学习领域的巨大成功,它也逐渐引起了遥感(RS)领域的广泛关注。然而,遥感领域的研究一直受制于缺乏大型标注数据集以及遥感平台多样性导致的数据模式不一致。近年来,随着自监督学习(SSL)算法的兴起,RS 研究人员开始关注 "预训练和微调 "范式在 RS 中的应用。然而,遥感领域的多模态数据融合研究还很少。他们大多选择只使用其中一种模态数据或简单地将多种模态数据粗略拼接的方法:为了研究一种更有效的多模态数据融合方案,我们提出了一种基于门控单元控制的多模态融合机制(MGSViT)。本文结合两种常用的 SSL 算法,基于 BigEarthNet 数据集对 ViT 模型进行预训练,并结合多光谱(MS)和合成孔径雷达(SAR),提出了用于特征学习的模内和模间门控融合单元。我们的方法可以有效地结合不同模态数据来提取关键特征信息:经过微调和对比实验,我们在所有下游分类任务中的表现都优于最先进的算法。我们提出的方法的有效性得到了验证。
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引用次数: 0
Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach. 在伽马振荡背景下分析自上而下的视觉注意力:一种依赖层的网络方法。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-23 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1439632
Tianyi Zheng, Masato Sugino, Yasuhiko Jimbo, G Bard Ermentrout, Kiyoshi Kotani

Top-down visual attention is a fundamental cognitive process that allows individuals to selectively attend to salient visual stimuli in the environment. Recent empirical findings have revealed that gamma oscillations participate in the modulation of visual attention. However, computational studies face challenges when analyzing the attentional process in the context of gamma oscillation due to the unstable nature of gamma oscillations and the complexity induced by the layered fashion in the visual cortex. In this study, we propose a layer-dependent network-of-networks approach to analyze such attention with gamma oscillations. The model is validated by reproducing empirical findings on orientation preference and the enhancement of neuronal response due to top-down attention. We perform parameter plane analysis to classify neuronal responses into several patterns and find that the neuronal response to sensory and attention signals was modulated by the heterogeneity of the neuronal population. Furthermore, we revealed a counter-intuitive scenario that the excitatory populations in layer 2/3 and layer 5 exhibit opposite responses to the attentional input. By modification of the original model, we confirmed layer 6 plays an indispensable role in such cases. Our findings uncover the layer-dependent dynamics in the cortical processing of visual attention and open up new possibilities for further research on layer-dependent properties in the cerebral cortex.

自上而下的视觉注意是一种基本的认知过程,它能让人有选择地注意环境中的显著视觉刺激。最近的实证研究发现,伽马振荡参与了视觉注意力的调节。然而,由于伽马振荡的不稳定性和视觉皮层分层方式的复杂性,计算研究在分析伽马振荡背景下的注意过程时面临挑战。在本研究中,我们提出了一种层依赖网络(network-of-networks)方法来分析伽马振荡下的注意力。该模型通过再现方位偏好和自上而下注意引起的神经元反应增强的经验发现得到了验证。我们进行了参数平面分析,将神经元反应分为几种模式,并发现神经元对感觉和注意力信号的反应受神经元群异质性的调节。此外,我们还发现了一种与直觉相反的情况,即第 2/3 层和第 5 层的兴奋神经元群对注意输入的反应相反。通过修改原始模型,我们证实第 6 层在这种情况下发挥着不可或缺的作用。我们的发现揭示了大脑皮层处理视觉注意力过程中的层依赖动态,为进一步研究大脑皮层的层依赖特性提供了新的可能性。
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引用次数: 0
Dynamical predictive coding with reservoir computing performs noise-robust multi-sensory speech recognition. 动态预测编码与蓄水池计算实现了噪声稳健的多感官语音识别。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-23 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1464603
Yoshihiro Yonemura, Yuichi Katori

Multi-sensory integration is a perceptual process through which the brain synthesizes a unified perception by integrating inputs from multiple sensory modalities. A key issue is understanding how the brain performs multi-sensory integrations using a common neural basis in the cortex. A cortical model based on reservoir computing has been proposed to elucidate the role of recurrent connectivity among cortical neurons in this process. Reservoir computing is well-suited for time series processing, such as speech recognition. This inquiry focuses on extending a reservoir computing-based cortical model to encompass multi-sensory integration within the cortex. This research introduces a dynamical model of multi-sensory speech recognition, leveraging predictive coding combined with reservoir computing. Predictive coding offers a framework for the hierarchical structure of the cortex. The model integrates reliability weighting, derived from the computational theory of multi-sensory integration, to adapt to multi-sensory time series processing. The model addresses a multi-sensory speech recognition task, necessitating the management of complex time series. We observed that the reservoir effectively recognizes speech by extracting time-contextual information and weighting sensory inputs according to sensory noise. These findings indicate that the dynamic properties of recurrent networks are applicable to multi-sensory time series processing, positioning reservoir computing as a suitable model for multi-sensory integration.

多感觉统合是一个感知过程,大脑通过整合多种感觉模式的输入来合成统一的感知。一个关键问题是了解大脑如何利用皮层中的共同神经基础进行多感官整合。有人提出了一个基于储库计算的大脑皮层模型,以阐明大脑皮层神经元之间的循环连接在这一过程中的作用。水库计算非常适合语音识别等时间序列处理。本研究的重点是扩展基于水库计算的皮层模型,以涵盖皮层内的多感官整合。这项研究引入了一个多感官语音识别动态模型,利用预测编码与水库计算相结合。预测编码为大脑皮层的层次结构提供了一个框架。该模型整合了从多感官整合计算理论中得出的可靠性加权,以适应多感官时间序列处理。该模型针对的是需要管理复杂时间序列的多感官语音识别任务。我们观察到,通过提取时间上下文信息并根据感官噪声对感官输入进行加权,水库能有效识别语音。这些研究结果表明,递归网络的动态特性适用于多感官时间序列处理,从而将水库计算定位为多感官整合的合适模型。
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引用次数: 0
Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices. 基于深度学习的阿尔茨海默病检测:可重复性和建模选择的影响。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-20 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1360095
Rosanna Turrisi, Alessandro Verri, Annalisa Barla

Introduction: Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance.

Methods: We utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately.

Results: The combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set.

Discussions: Our work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth-often overlooked factors- can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies.

简介:机器学习(ML)已成为医疗保健领域一种前景广阔的方法,其性能优于传统的统计技术。然而,要将机器学习作为临床实践中的可靠工具,遵守数据处理、建模设计和评估方面的最佳实践至关重要。在这项工作中,我们总结并严格遵守这些做法,以确保 ML 的可重复性和可靠性。具体来说,我们将重点放在阿尔茨海默病(AD)的检测上,这是医疗保健领域的一个挑战性问题。此外,我们还研究了建模选择(包括不同的数据增强技术和模型复杂性)对总体性能的影响:我们利用 ADNI 语料库中的磁共振成像(MRI)数据,使用三维卷积神经网络(CNN)解决二元分类问题。数据处理和建模是专门为解决数据稀缺和最大限度减少计算开销而定制的。在此框架内,我们训练了 15 个预测模型,考虑了三种不同的数据增强策略和五种具有不同卷积层数的三维卷积神经网络架构。增强策略涉及仿射变换,如缩放、移位和旋转,可同时或单独应用:结果:数据增强和模型复杂性的综合影响导致预测准确率的变化高达 10%。值得注意的是,当仿射变换单独应用时,无论选择何种架构,模型都能达到更高的准确度。在所有策略中,随着卷积层数的增加,模型的准确性呈现出凹凸行为,并在中间值达到峰值。最佳模型在内部测试集和额外的外部测试集上都达到了极佳的性能:我们的工作强调了在应用于医疗保健的人工智能领域坚持严格实验实践的重要性。研究结果清楚地表明了数据扩充和模型深度--这些经常被忽视的因素--如果不进行深入研究,会如何极大地影响最终性能。这既强调了探索被忽视的建模方面的必要性,也强调了全面报告所有建模选择的必要性,以确保可重复性并促进不同研究之间进行有意义的比较。
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引用次数: 0
Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review. 基于脑电图的自适应闭环脑机接口在神经康复中的应用:综述。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-20 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1431815
Wenjie Jin, XinXin Zhu, Lifeng Qian, Cunshu Wu, Fan Yang, Daowei Zhan, Zhaoyin Kang, Kaitao Luo, Dianhuai Meng, Guangxu Xu

Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.

脑机接口(BCI)是一种突破性的方法,它可以绕过传统的神经和肌肉通路,让有严重运动障碍的人直接进行交流。在种类繁多的 BCI 技术中,基于脑电图(EEG)的系统因其非侵入性、操作简便和成本效益高而备受青睐。最近的进步促进了自适应双向闭环生物识别(BCI)技术的发展,该技术可根据用户的大脑活动进行动态调整,从而提高神经康复的响应速度和疗效。这些系统支持实时调节和持续反馈,可根据用户的神经和行为反应进行个性化治疗干预。通过结合机器学习算法,这些 BCI 可优化用户互动,并通过依赖活动的神经可塑性机制促进康复效果。本文回顾了基于脑电图的自适应双向闭环 BCI 目前的发展状况,研究了它们在运动和感觉功能恢复方面的应用,以及在实际应用中遇到的挑战。研究结果强调了这些技术在显著提高患者生活质量和社会交往方面的潜力,同时也指出了未来研究的关键领域,旨在提高系统的适应性和性能。随着人工智能的不断进步,先进的生物识别(BCI)系统有望改变神经康复的现状,并扩大在各个领域的应用。
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引用次数: 0
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
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Frontiers in Computational Neuroscience
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