Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Neural Circuits01:25

Neural Circuits

2.7K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.7K
Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Fracture Behavior and Energy Conversion of Concrete-Rock Composites Subjected to Fatigue Disturbance: Experimental and Numerical Approaches.

Materials (Basel, Switzerland)·2026
Same author

An asynchronous production line of meiotic prophase I in the mouse fetal ovary.

Experimental cell research·2026
Same author

The whole-course of supervised home-based multi-modal prehabilitation to improve clinical outcome in patients undergoing neoadjuvant chemotherapy prior to gastrectomy: a single-center randomized controlled trial.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association·2026
Same author

A High-Quality and Robust Intravascular Electromyography (iEMG) Acquisition Method for Locomotor Tasks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

LukS-PV targeting C5aR inhibits EMT in hepatocellular carcinoma via the BCL6/HDAC6/HSPD1 axis.

Communications biology·2026
Same author

Large-area non-stoichiometric phase transition in transition metal chalcogenide films.

Nature materials·2026
Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026
Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026
Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026
Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026
Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026
Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

基于三元组的深度散列增量学习用于大脑网络分类.

Yaqin Zhang, Junzhong Ji, Gan Liu

    IEEE journal of biomedical and health informatics
    |June 5, 2025
    PubMed
    概括
    此摘要是机器生成的。

    一种新的基于三重组的深度哈希增量学习 (Tri-DHIL) 方法通过逐步学习数据来改善大脑网络的分类. 这种方法克服了多站点数据带来的挑战,提高了诊断准确度.

    更多相关视频

    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    605

    相关实验视频

    Last Updated: Jan 18, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.0K
    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    605

    科学领域:

    • 神经科学是一个神经科学.
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 公共大脑网络数据集经常结合来自多个站点的数据,导致由于数据异质性而导致性能问题.
    • 在不同地点收集数据的变化会对大脑网络分类模型的准确性产生负面影响.

    研究的目的:

    • 引入一种基于三元组的新型深度哈希增量学习 (Tri-DHIL) 方法,用于强大的大脑网络分类.
    • 通过从单个站点实现增量学习来解决脑成像数据集中多源数据异质性的挑战.

    主要方法:

    • 三DHIL方法涉及三个阶段:站点队列生成,基于三元组的深度哈希学习和增量学习.
    • 网站排名是基于样本数量和标签信息. 样品使用诊断标签进行聚类,以形成三胞胎 (,同一个群,不同的群).
    • 深度哈希学习提取特征并将它们映射到哈希代码中. 增量学习调整模型参数,使用累积的三重组损失来防止灾难性遗忘.

    主要成果:

    • 在ABIDE I,ABIDE II和ADHD-200数据集上的实验结果证明了Tri-DHIL方法的有效性.
    • 尽管来自多个站点的数据异质,但拟议的方法实现了竞争性分类性能.
    • 增量学习成功地防止模型在整合新站点数据时忘记先前学习的特征.

    结论:

    • 三DHIL方法提供了一个有前途的解决方案,用于用多站点数据集对大脑网络进行分类.
    • 增量学习对于将模型适应异质数据流而不会损害先前获得的知识至关重要.
    • 这种方法通过处理现实世界的数据复杂性,提高了机器学习模型在神经成像研究中的实际应用性.