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

Neuroplasticity01:01

Neuroplasticity

1.6K
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
1.6K
Introduction to Learning01:18

Introduction to Learning

961
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
961
Cognitive Learning01:21

Cognitive Learning

1.0K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
1.0K
Observational Learning01:12

Observational Learning

841
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
841
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

1.9K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
1.9K

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

Promoting Psychological Resilience and Well-Being in Youth With a Smartphone-Based Ecological Momentary mHealth Intervention: Secondary Analysis of a Microrandomized Trial.

Journal of medical Internet research·2026
Same author

dsLassoCov: a federated Lasso approach incorporating covariate control.

Scientific reports·2026
Same author

A multi-task learning approach combining regression and classification tasks for joint feature selection.

Scientific reports·2026
Same author

Discrete interneuron subsets participate in GluN1/GluN3A excitatory glycine receptor (eGlyR)-mediated regulation of hippocampal network activity throughout development and evolution.

Research square·2025
Same author

Computational network models for forecasting and control of mental health trajectories in digital applications.

NPJ digital medicine·2025
Same author

From progress to paralysis: bridging the translation gap in digital mental health care?

Psychological medicine·2025
Same journal

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes.

ArXiv·2026
Same journal

A Positron Range Correction with Texture Preservation Framework in PET Imaging.

ArXiv·2026
Same journal

Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian Inference of Conformational Populations.

ArXiv·2026
Same journal

Droplet Fusion as a Relaxation Process: Comparison with Shape Recovery of Newtonian and Viscoelastic Droplets.

ArXiv·2026
Same journal

Ridge-filter crosstalk in conformal proton FLASH planning: dependence on beamlet pitch and iterative mitigation.

ArXiv·2026
Same journal

Electrochemical DNA Hairpin Sensors for Differentiating Small Molecule Intercalation from Minor Groove Binding.

ArXiv·2026
查看所有相关文章

相关实验视频

Updated: Jan 17, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K

神经科学可以教导人工智能如何在不断变化的环境中学习.

Daniel Durstewitz, Bruno Averbeck, Georgia Koppe

    ArXiv
    |September 19, 2025
    PubMed
    概括
    此摘要是机器生成的。

    动物不断适应不断变化的环境,不像当前的人工智能模型训练过一次. 这一观点探讨了神经科学如何为人工智能提供信息.

    更多相关视频

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.8K

    相关实验视频

    Last Updated: Jan 17, 2026

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    8.0K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.8K

    科学领域:

    • 神经科学和人工智能 (AI)
    • 计算神经科学是一种神经科学.
    • 机器学习 机器学习

    背景情况:

    • 现代人工智能模型,包括大型语言模型,在庞大的数据集上进行昂贵的一次性训练,从而产生固定的参数.
    • 动物表现出了显著的适应能力,不断学习和适应动态的环境条件,特别是在社会环境中.
    • 动物的这种适应能力以快速的行为转变和神经群体活动的突然变化为特征.

    研究的目的:

    • 探索AI从神经科学中学习的潜力,特别是在持续学习和适应领域.
    • 弥合AI的静态学习范式与动物行为中观察到的动态,自适应式学习之间的差距.
    • 促进协同关系,神经科学为人工智能发展提供信息,人工智能工具推进神经科学研究.

    主要方法:

    • 在AI中整合关于持续和上下文学习的文献.
    • 对神经科学研究的回顾动物学习在任务中改变规则和奖励概率.
    • 对人工智能和动物模型的学习基础上的计算过程进行比较分析.

    主要成果:

    • 确定了人工智能的静态训练和动物的持续适应之间的显著对比.
    • 强调了适应性AI对现实世界的应用,如机器人技术和人类-AI交互等越来越重要的重要性.
    • 建立了人工智能和神经科学之间相互学习的框架.

    结论:

    • 神经科学为开发更具适应性和持续学习的人工智能系统提供了宝贵的见解.
    • 人工智能的进步可以为理解生物学习机制提供新的工具和视角.
    • 这种跨学科的方法对于推进新兴的NeuroAI领域至关重要.