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

2.6K
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...
2.6K
Long-term Potentiation01:35

Long-term Potentiation

58.3K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
58.3K
Long-term Potentiation01:25

Long-term Potentiation

3.4K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
3.4K

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

Dopamine in the ventral and tail of striatum supports global and local evaluation in reward-threat conflict.

bioRxiv : the preprint server for biology·2026
Same author

Spectral envelopes of facial movements predict intention, cortical representations, and neural prosthetic control.

bioRxiv : the preprint server for biology·2026
Same author

Cadmium bioavailability and accumulation in rice grains: A comprehensive assessment of soil properties and rhizobacterial influences using in vitro-in vivo assays and multivariate modeling.

Ecotoxicology and environmental safety·2026
Same author

Phasic dopamine drives conditioned responding beyond its role in learning.

bioRxiv : the preprint server for biology·2026
Same author

Development and validation of a preoperative nomogram for predicting venous thromboembolism risk after gynecologic oncology surgery.

Discover oncology·2026
Same author

Context-specific configuration of orthogonal integrator dynamics for flexible foraging decisions.

bioRxiv : the preprint server for biology·2025
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
查看所有相关文章

相关实验视频

Updated: Jan 16, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.5K

一个硬连接的神经电路用于时间差学习.

Malcolm G Campbell1,2, Yongsoo Ra1,2,3, Zhiqin Chen1,2,3

  • 1Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.

bioRxiv : the preprint server for biology
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

大脑中的多巴胺神经元实施时间差 (TD) 学习,这是基于奖励的学习的关键过程. 这项研究揭示了多巴胺电路如何计算TD错误,并设置大脑.

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
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.9K

相关实验视频

Last Updated: Jan 16, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.5K
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
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.9K

科学领域:

  • 神经科学是一个神经科学.
  • 计算神经科学是一种神经科学.
  • 强化学习是一种强化学习.

背景情况:

  • 多巴胺对学习至关重要,作为奖励预测的教学信号.
  • 理论表明多巴胺功能就像强化学习中的时间差异 (TD) 错误.
  • 作为多巴胺激应TD学习的基础的神经机制尚未完全理解.

研究的目的:

  • 研究多巴胺神经元实施的TD学习的电路级机制.
  • 确定多巴胺神经元及其目标如何完成TD学习的关键步骤.
  • 探索时间折扣的神经生物学基础.

主要方法:

  • 结合大规模的神经记录与有模式的光遗传刺激.
  • 作为奖励替代品,使用了核中多巴胺轴突的光遗传刺激 (NAc).
  • 研究了D1多巴胺受体表达神经元 (D1神经元) 在NAc中的作用.

主要成果:

  • 通过改变NAc D1神经元活动,光遗传性多巴胺刺激诱导了多巴胺神经元中的TD错误类活性.
  • 刺激NAc D1神经元驱动多巴胺神经元根据刺激模式的TD错误发射.
  • 一个双相线性过器 (正负相) 描述了D1神经元到多巴胺神经元的转变,计算时间差异.
  • 这种过器的相位平衡表明了设置时间折扣因子的机制.

结论:

  • TD计算被硬连接到多巴胺-NAc电路中.
  • 已经确定了一种由双相过器控制的时间折扣的电路级机制.
  • 这为理解神经生物学组件如何产生学习计算和参数提供了一个框架.