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

Storage01:23

Storage

476
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
476
Neural Circuits01:25

Neural Circuits

3.1K
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...
3.1K
System of Memory01:23

System of Memory

7.7K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
7.7K
Understanding Memory01:19

Understanding Memory

1.7K
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
1.7K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

2.2K
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...
2.2K
Neuroplasticity01:01

Neuroplasticity

2.2K
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.
2.2K

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

Distinct roles of hippocampus and neocortex in symbolic compositional generalization.

Neuron·2026
Same author

Accelerating scientific discovery with Co-Scientist.

Nature·2026
Same author

Human curriculum learning of a cue combination task.

Nature human behaviour·2026
Same author

Technological <i>folie à deux</i>: feedback loops between AI chatbots and mental health.

Nature. Mental health·2026
Same author

Understanding human metacontrol and its pathologies using deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Hybrid neural-cognitive models reveal how memory shapes human reward learning.

Nature human behaviour·2026
Same journal

Six ways to put the public at the heart of science and policy.

Nature·2026
Same journal

The complex truth about trust in science.

Nature·2026
Same journal

Have people stopped trusting science? The data tell a surprising story.

Nature·2026
Same journal

How FAIR data are helping to build trust in science.

Nature·2026
Same journal

Scientists should recognize their own political biases to build public trust.

Nature·2026
Same journal

Harmonizing standards and resources for the medical genome.

Nature·2026
查看所有相关文章

相关实验视频

Updated: Mar 13, 2026

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

10.9K

使用具有动态外部内存的神经网络的混合计算

Alex Graves1, Greg Wayne1, Malcolm Reynolds1

  • 1Google DeepMind, 5 New Street Square, London EC4A 3TW, UK.

Nature
|October 13, 2016
PubMed
概括
此摘要是机器生成的。

一个新的可差分神经计算机 (DNC) 模型将神经网络与外部内存集成,使复杂的数据操纵和学习成为可能. 这种人工智能的进步克服了传统神经网络在结构化推理和长期数据存储方面的局限性.

更多相关视频

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.4K

相关实验视频

Last Updated: Mar 13, 2026

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

10.9K
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.4K

科学领域:

  • 人工智能
  • 机器学习
  • 计算机科学

背景情况:

  • 人工神经网络在传感和序列处理方面表现出色,但由于缺乏外部记忆,难以处理复杂的数据结构和长期记忆.
  • 现有的神经网络模型在需要长时间变量表示和数据操纵的任务上是有限的.

研究的目的:

  • 引入一种新的机器学习模型,即可区分的神经计算机 (DNC),能够与外部内存进行交互.
  • 通过利用其外部内存能力来展示DNC学习和执行复杂的推理和结构化任务的能力.

主要方法:

  • 开发了一个可区分的神经计算机 (DNC) 模型,将神经网络与读写外部内存矩阵结合起来.
  • 通过监督学习来训练DNC进行推理和推断任务,并通过强化学习来执行目标导向的序列任务.
  • 在基于合成和现实世界的图表问题和符号序列驱动的拼图上评估了DNC.

主要成果:

  • 通过模仿自然语言推理和推理,
  • 证明了最短路径的学习和图形链接推断,将其推广到运输网络和家族树.
  • 通过符号序列指定改变目标的移动块拼图成功完成.

结论:

  • 可区分神经计算机 (DNC) 通过结合外部内存来弥合神经网络和传统计算机之间的差距.
  • DNC表现出以前无法通过标准神经网络解决的复杂,结构化的任务的能力.
  • 这种进步为人工智能在需要复杂推理,数据操纵和长期记忆的领域开辟了新的可能性.