Suppr超能文献

长期学习过程中的不同类型的神经重组。

Distinct types of neural reorganization during long-term learning.

机构信息

Department of Biomedical Engineering, Carnegie Mellon University , Pittsburgh, Pennsylvania.

Center for the Neural Basis of Cognition, Carnegie Mellon University , Pittsburgh, Pennsylvania.

出版信息

J Neurophysiol. 2019 Apr 1;121(4):1329-1341. doi: 10.1152/jn.00466.2018. Epub 2019 Feb 6.

Abstract

What are the neural mechanisms of skill acquisition? Many studies find that long-term practice is associated with a functional reorganization of cortical neural activity. However, the link between these changes in neural activity and the behavioral improvements that occur is not well understood, especially for long-term learning that takes place over several weeks. To probe this link in detail, we leveraged a brain-computer interface (BCI) paradigm in which rhesus monkeys learned to master nonintuitive mappings between neural spiking in primary motor cortex and computer cursor movement. Critically, these BCI mappings were designed to disambiguate several different possible types of neural reorganization. We found that during the initial phase of learning, lasting minutes to hours, rapid changes in neural activity common to all neurons led to a fast suppression of motor error. In parallel, local changes to individual neurons gradually accrued over several weeks of training. This slower timescale cortical reorganization persisted long after the movement errors had decreased to asymptote and was associated with more efficient control of movement. We conclude that long-term practice evokes two distinct neural reorganization processes with vastly different timescales, leading to different aspects of improvement in motor behavior. NEW & NOTEWORTHY We leveraged a brain-computer interface learning paradigm to track the neural reorganization occurring throughout the full time course of motor skill learning lasting several weeks. We report on two distinct types of neural reorganization that mirror distinct phases of behavioral improvement: a fast phase, in which global reorganization of neural recruitment leads to a quick suppression of motor error, and a slow phase, in which local changes in individual tuning lead to improvements in movement efficiency.

摘要

技能获取的神经机制是什么?许多研究发现,长期实践与皮质神经活动的功能重组有关。然而,这些神经活动变化与行为改善之间的联系还不太清楚,尤其是对于需要数周时间才能完成的长期学习。为了详细探究这种联系,我们利用了一种脑机接口(BCI)范式,在该范式中,恒河猴学会了掌握初级运动皮层中神经尖峰与计算机光标运动之间非直观的映射关系。关键是,这些 BCI 映射旨在消除几种不同的可能的神经重组类型。我们发现,在学习的初始阶段,持续数分钟到数小时,所有神经元中常见的快速神经活动变化导致运动误差的快速抑制。同时,个体神经元的局部变化在数周的训练中逐渐累积。这种较慢的皮质重组时间尺度在运动误差减小到渐近线后仍持续很长时间,并与运动控制的效率提高有关。我们得出的结论是,长期实践引发了两种具有截然不同时间尺度的独特神经重组过程,从而导致运动行为改善的不同方面。 新的和值得注意的是,我们利用脑机接口学习范式来跟踪整个运动技能学习的全过程中的神经重组,该过程持续数周。我们报告了两种截然不同的神经重组类型,它们反映了行为改善的不同阶段:快速阶段,其中神经募集的全局重组导致运动误差的快速抑制,以及缓慢阶段,其中个体调谐的局部变化导致运动效率的提高。

相似文献

1
Distinct types of neural reorganization during long-term learning.长期学习过程中的不同类型的神经重组。
J Neurophysiol. 2019 Apr 1;121(4):1329-1341. doi: 10.1152/jn.00466.2018. Epub 2019 Feb 6.
3
Functional network reorganization during learning in a brain-computer interface paradigm.脑机接口范式下学习过程中的功能网络重组
Proc Natl Acad Sci U S A. 2008 Dec 9;105(49):19486-91. doi: 10.1073/pnas.0808113105. Epub 2008 Dec 1.
4
Learning by neural reassociation.神经再关联学习。
Nat Neurosci. 2018 Apr;21(4):607-616. doi: 10.1038/s41593-018-0095-3. Epub 2018 Mar 12.
5
New neural activity patterns emerge with long-term learning.新的神经活动模式随着长期学习而出现。
Proc Natl Acad Sci U S A. 2019 Jul 23;116(30):15210-15215. doi: 10.1073/pnas.1820296116. Epub 2019 Jun 10.
9
Neural constraints on learning.学习中的神经限制
Nature. 2014 Aug 28;512(7515):423-6. doi: 10.1038/nature13665.

引用本文的文献

2
Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.非侵入式脑机接口:现状与趋势
IEEE Rev Biomed Eng. 2025;18:26-49. doi: 10.1109/RBME.2024.3449790. Epub 2025 Jan 28.

本文引用的文献

1
Feedforward and Feedback Control Share an Internal Model of the Arm's Dynamics.前馈和反馈控制共享手臂动力学的内部模型。
J Neurosci. 2018 Dec 5;38(49):10505-10514. doi: 10.1523/JNEUROSCI.1709-18.2018. Epub 2018 Oct 24.
2
A Neural Population Mechanism for Rapid Learning.一种用于快速学习的神经群体机制。
Neuron. 2018 Nov 21;100(4):964-976.e7. doi: 10.1016/j.neuron.2018.09.030. Epub 2018 Oct 18.
3
Constraints on neural redundancy.神经冗余的约束。
Elife. 2018 Aug 15;7:e36774. doi: 10.7554/eLife.36774.
4
Learning by neural reassociation.神经再关联学习。
Nat Neurosci. 2018 Apr;21(4):607-616. doi: 10.1038/s41593-018-0095-3. Epub 2018 Mar 12.
5
Neural Population Dynamics Underlying Motor Learning Transfer.神经群体动力学在运动学习迁移中的作用。
Neuron. 2018 Mar 7;97(5):1177-1186.e3. doi: 10.1016/j.neuron.2018.01.040. Epub 2018 Feb 15.
7
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex.顶叶皮层神经元活动模式的动态重组
Cell. 2017 Aug 24;170(5):986-999.e16. doi: 10.1016/j.cell.2017.07.021. Epub 2017 Aug 17.
9
Neuromotor Noise Is Malleable by Amplifying Perceived Errors.神经运动噪声可通过放大感知误差来调节。
PLoS Comput Biol. 2016 Aug 4;12(8):e1005044. doi: 10.1371/journal.pcbi.1005044. eCollection 2016 Aug.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验