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

Cognitive Learning01:21

Cognitive Learning

222
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...
222
Instinctive Drift01:05

Instinctive Drift

193
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
193

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

Neural timescales from a computational perspective.

Nature neuroscience·2026
Same author

Bridging the Scales via Personalized Cellular Modeling and Deep Phenotyping in Schizophrenia.

JAMA psychiatry·2026
Same author

Brain-wide organization of intrinsic timescales at single-neuron resolution.

bioRxiv : the preprint server for biology·2025
Same author

Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning.

PLoS computational biology·2024
Same author

Signatures of criticality in efficient coding networks.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Spatial and temporal correlations in neural networks with structured connectivity.

Physical review research·2024

相关实验视频

Updated: Jun 8, 2025

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication
03:53

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication

Published on: November 17, 2023

1.1K

网络瓶和任务结构控制了食代理中可解释的学习规则的演变.

Emmanouil Giannakakis1, Sina Khajehabdollahi2, Anna Levina3

  • 1University of Tbingen, Department of Computer Science, Max Planck Institute for Biological Cybernetics. giannakakismanos@gmail.com.

Artificial life
|November 1, 2024
PubMed
概括

超学习优化了可塑性规则,用于人工和生物系统中持续的本地学习. 约束减少了规则的变化,产生了可解释的机制,潜在地反映了生物学习.

关键词:
塑性是一种可塑性.进化算法是指进化的算法.自主组织的自我组织.

更多相关视频

Foraging Path-length Protocol for Drosophila melanogaster Larvae
07:26

Foraging Path-length Protocol for Drosophila melanogaster Larvae

Published on: April 23, 2016

9.3K
The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
09:01

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Published on: July 8, 2015

12.5K

相关实验视频

Last Updated: Jun 8, 2025

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication
03:53

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication

Published on: November 17, 2023

1.1K
Foraging Path-length Protocol for Drosophila melanogaster Larvae
07:26

Foraging Path-length Protocol for Drosophila melanogaster Larvae

Published on: April 23, 2016

9.3K
The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
09:01

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Published on: July 8, 2015

12.5K

科学领域:

  • 计算神经科学是一种计算神经科学.
  • 人工智能的人工智能是人工智能.
  • 进化计算的演变

背景情况:

  • 持续的本地学习对生物和人工系统至关重要.
  • 最佳可塑性机制受到环境因素和网络约束的影响.
  • 了解这些依赖关系是开发强大的学习系统的关键.

研究的目的:

  • 研究环境因素和结构约束如何影响最佳可塑性机制.
  • 用进化优化阐明塑性规则的元学习中的依赖关系.
  • 将发现与生物学学习规则进行比较.

主要方法:

  • 通过奖励调制的可塑性规则的进化优化进行超级学习.
  • 具体的代理人执行一个食任务.
  • 分析不同约束条件下的规则多样性 (规范化,瓶).

主要成果:

  • 无约束的元学习产生了各种各样的可塑性规则.
  • 规范化和瓶减少了规则的变化,导致了可解释的规则.
  • 塑性规则的超学习显示了高参数灵敏度.

结论:

  • 塑性规则元学习对参数敏感,可能反映生物网络学习.
  • 约束对于发现可解释和潜在的生物学相关的学习规则至关重要.
  • 这种方法可以帮助发现生物学习的客观功能和细节.