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

Neuronal Communication01:28

Neuronal Communication

816
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
816
Neural Circuits01:25

Neural Circuits

1.1K
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...
1.1K
Organization of the Brain01:30

Organization of the Brain

749
The brain is an integral component of the nervous system and serves as the center for processing sensory inputs, making decisions, and directing bodily actions. This complex organ is organized into three primary sections: the hindbrain, midbrain, and forebrain, each responsible for a range of vital functions.
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...
749
Neuroplasticity01:01

Neuroplasticity

315
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.
315
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

453
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
453
Neurons as Communicators of the Brain01:22

Neurons as Communicators of the Brain

1.1K
Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
Cell Body
The cell body, also known...
1.1K

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

Stress-Lensed Electrochemical Sintering Enables Fast and Stable Lithium-Silicon Alloy Chemistry in All-Solid-State Batteries.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Navigating chemical-linguistic sharing space with heterogeneous molecular encoding.

Nature communications·2026
Same author

LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Breathing New Life into Small Object Detection with Detection-Oriented Rectification.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.

Science advances·2026
Same author

Interpretable graph deep learning framework for drug synergy prediction by integrating functional and clinical similarities.

NPJ digital medicine·2026

相关实验视频

Updated: Jun 16, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

具有内部复杂性的网络模型将人工智能和神经科学连接起来.

Linxuan He1,2,3, Yunhui Xu1,4, Weihua He5

  • 1Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China.

Nature computational science
|August 16, 2024
PubMed
概括

本研究介绍了一种具有AI内部复杂性方法的小型模型,挑战了大模型范式. 它表明,复杂的神经元可以达到与较大的网络可比的性能,优化AI模型的效率.

更多相关视频

Perspectives on Neuroscience
00:26

Perspectives on Neuroscience

Published on: July 31, 2007

4.9K
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

10.3K

相关实验视频

Last Updated: Jun 16, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K
Perspectives on Neuroscience
00:26

Perspectives on Neuroscience

Published on: July 31, 2007

4.9K
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

10.3K

科学领域:

  • 人工智能的人工智能
  • 计算神经科学是一种神经科学.
  • 神经网络的神经网络的神经网络

背景情况:

  • 目前的人工智能研究重点是"大型人工智能模型",其深度,大小和宽度增加.
  • 这种"具有外部复杂性的大模型"方法主导了一般问题解决的领域.
  • 为更高效的人工智能模型提出了一种替代方法",具有内部复杂性的小型模型".

研究的目的:

  • 探索构建通用AI模型的替代方法.
  • 为了研究将丰富的特性纳入神经元以提高AI效率的潜力.
  • 为了证明内部复杂性可以与神经网络中的外部扩展相匹配.

主要方法:

  • 开发了一个霍奇金-哈克斯利 (HH) 网络,每个神经元都具有丰富的内部复杂性.
  • 将HH网络的动态特性和性能与更大的漏洞集成与火 (LIF) 网络进行了比较.
  • 利用计算建模来模拟和分析神经网络行为.

主要成果:

  • 表明一个具有复杂神经元的小HH网络可以复制更大的LIF网络的动态特性.
  • 证明外部增加网络规模并不是实现所需动态属性的唯一途径.
  • 突出了内部复杂的神经元模型可能带来的效率增长.

结论:

  • "具有内部复杂性的小型模型"方法为开发高效和通用的AI模型提供了可行的替代方案.
  • 可以利用内部神经元的复杂性来实现与较大,外部扩展的网络相匹配的性能.
  • 这项研究为设计更高效和更强大的AI架构开辟了新的途径.