• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

纹状体网络在学习过程中的编码动态。

Coding Dynamics of the Striatal Networks During Learning.

机构信息

Université Côte d'Azur, CNRS, INSERM, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne 06560, France.

Université Côte d'Azur, CNRS, LJAD and NeuroMod, Nice 0600, France

出版信息

eNeuro. 2024 Oct 29;11(10). doi: 10.1523/ENEURO.0436-23.2024. Print 2024 Oct.

DOI:10.1523/ENEURO.0436-23.2024
PMID:39349057
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11521795/
Abstract

The rat dorsomedial (DMS) and dorsolateral striatum (DLS), equivalent to caudate nucleus and putamen in primates, are required for goal-directed and habit behaviour, respectively. However, it is still unclear whether and how this functional dichotomy emerges in the course of learning. In this study, we investigated this issue by recording DMS and DLS single neuron activity in rats performing a continuous spatial alternation task, from the acquisition to optimized performance. We first applied a classical analytical approach to identify task-related activity based on the modifications of single neuron firing rate in relation to specific task events or maze trajectories. We then used an innovative approach based on Hawkes process to reconstruct a directed connectivity graph of simultaneously recorded neurons, that was used to decode animal behavior. This approach enabled us to better unravel the role of DMS and DLS neural networks across learning stages. We showed that DMS and DLS display different task-related activity throughout learning stages, and the proportion of coding neurons over time decreases in the DMS and increases in the DLS. Despite these major differences, the decoding power of both networks increases during learning. These results suggest that DMS and DLS neural networks gradually reorganize in different ways in order to progressively increase their control over the behavioral performance.

摘要

大鼠背侧纹状体的背内侧(DMS)和背外侧(DLS)分别对应灵长类动物的尾状核和壳核,分别负责目标导向行为和习惯行为。然而,在学习过程中,这种功能二分法是否以及如何出现仍不清楚。在这项研究中,我们通过记录大鼠在执行连续空间交替任务时 DMS 和 DLS 的单个神经元活动,从获得优化表现的过程中,研究了这个问题。我们首先应用经典的分析方法,根据单个神经元的放电率与特定任务事件或迷宫轨迹的关系,确定与任务相关的活动。然后,我们使用基于 Hawkes 过程的创新方法来重建同时记录的神经元的有向连接图,该方法用于解码动物行为。这种方法使我们能够更好地揭示 DMS 和 DLS 神经网络在学习阶段的作用。我们表明,DMS 和 DLS 在整个学习阶段显示出不同的与任务相关的活动,并且编码神经元的比例随时间的推移在 DMS 中减少,在 DLS 中增加。尽管存在这些主要差异,但两个网络的解码能力在学习过程中都会增加。这些结果表明,DMS 和 DLS 神经网络以不同的方式逐渐重组,以便逐渐增强它们对行为表现的控制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/6b8aefcb86f9/eneuro-11-ENEURO.0436-23.2024-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/ca8e41c64214/eneuro-11-ENEURO.0436-23.2024-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/1f9d51371cb7/eneuro-11-ENEURO.0436-23.2024-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/eba04d0f7967/eneuro-11-ENEURO.0436-23.2024-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/15b2ea173e97/eneuro-11-ENEURO.0436-23.2024-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/6b8aefcb86f9/eneuro-11-ENEURO.0436-23.2024-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/ca8e41c64214/eneuro-11-ENEURO.0436-23.2024-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/1f9d51371cb7/eneuro-11-ENEURO.0436-23.2024-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/eba04d0f7967/eneuro-11-ENEURO.0436-23.2024-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/15b2ea173e97/eneuro-11-ENEURO.0436-23.2024-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4876/11521795/6b8aefcb86f9/eneuro-11-ENEURO.0436-23.2024-g005.jpg

相似文献

1
Coding Dynamics of the Striatal Networks During Learning.纹状体网络在学习过程中的编码动态。
eNeuro. 2024 Oct 29;11(10). doi: 10.1523/ENEURO.0436-23.2024. Print 2024 Oct.
2
Contributions of dorsal striatal subregions to spatial alternation behavior.背侧纹状体亚区对空间交替行为的贡献。
Learn Mem. 2011 Jun 17;18(7):444-51. doi: 10.1101/lm.2123811. Print 2011 Jul.
3
Dorsolateral Striatal Task-initiation Bursts Represent Past Experiences More than Future Action Plans.背外侧纹状体任务启动爆发代表的是过去的经历,而不是未来的行动计划。
J Neurosci. 2021 Sep 22;41(38):8051-8064. doi: 10.1523/JNEUROSCI.3080-20.2021. Epub 2021 Aug 10.
4
Distinct recruitment of dorsomedial and dorsolateral striatum erodes with extended training.经过长时间的训练,背侧纹状体的背内侧和背外侧区域的募集能力逐渐减弱。
Elife. 2019 Oct 17;8:e49536. doi: 10.7554/eLife.49536.
5
Functional relationships between the hippocampus and dorsomedial striatum in learning a visual scene-based memory task in rats.大鼠在基于视觉场景的记忆任务中学习时海马体和背内侧纹状体之间的功能关系。
J Neurosci. 2014 Nov 19;34(47):15534-47. doi: 10.1523/JNEUROSCI.0622-14.2014.
6
Dopamine depletion in either the dorsomedial or dorsolateral striatum impairs egocentric Cincinnati water maze performance while sparing allocentric Morris water maze learning.背内侧或背外侧纹状体中的多巴胺耗竭会损害以自我为中心的辛辛那提水迷宫任务表现,同时不影响以环境为中心的莫里斯水迷宫学习。
Neurobiol Learn Mem. 2015 Feb;118:55-63. doi: 10.1016/j.nlm.2014.10.009. Epub 2014 Nov 13.
7
Decreased firing of striatal neurons related to licking during acquisition and overtraining of a licking task.在舔舐任务的习得和过度训练过程中,与舔舐相关的纹状体神经元放电减少。
J Neurosci. 2009 Nov 4;29(44):13952-61. doi: 10.1523/JNEUROSCI.2824-09.2009.
8
Neuronal activity in dorsomedial and dorsolateral striatum under the requirement for temporal credit assignment.在时间信用分配需求下背内侧和背外侧纹状体中的神经元活动。
Sci Rep. 2016 Jun 1;6:27056. doi: 10.1038/srep27056.
9
Dorsomedial and dorsolateral striatum exhibit distinct phasic neuronal activity during alcohol self-administration in rats.大鼠在酒精自我给药期间,背侧纹状体的腹内侧和腹外侧区域表现出明显的相位神经元活动。
Eur J Neurosci. 2013 Aug;38(4):2637-48. doi: 10.1111/ejn.12271. Epub 2013 Jun 13.
10
Distinct neural representation in the dorsolateral, dorsomedial, and ventral parts of the striatum during fixed- and free-choice tasks.在固定选择和自由选择任务期间,纹状体背外侧、背内侧和腹侧部分存在不同的神经表征。
J Neurosci. 2015 Feb 25;35(8):3499-514. doi: 10.1523/JNEUROSCI.1962-14.2015.

本文引用的文献

1
Strategy inference during learning via cognitive activity-based credit assignment models.通过基于认知活动的信用分配模型进行学习期间的策略推断。
Sci Rep. 2023 Jun 9;13(1):9408. doi: 10.1038/s41598-023-33604-2.
2
Spatiotemporal reorganization of corticostriatal networks encodes motor skill learning.皮质纹状体网络的时空重组编码运动技能学习。
Cell Rep. 2022 Apr 5;39(1):110623. doi: 10.1016/j.celrep.2022.110623.
3
Dorsolateral Striatal Task-initiation Bursts Represent Past Experiences More than Future Action Plans.背外侧纹状体任务启动爆发代表的是过去的经历,而不是未来的行动计划。
J Neurosci. 2021 Sep 22;41(38):8051-8064. doi: 10.1523/JNEUROSCI.3080-20.2021. Epub 2021 Aug 10.
4
Distinct recruitment of dorsomedial and dorsolateral striatum erodes with extended training.经过长时间的训练,背侧纹状体的背内侧和背外侧区域的募集能力逐渐减弱。
Elife. 2019 Oct 17;8:e49536. doi: 10.7554/eLife.49536.
5
No Discrete Start/Stop Signals in the Dorsal Striatum of Mice Performing a Learned Action.小鼠进行习得性动作时,背侧纹状体中没有离散的起始/停止信号。
Curr Biol. 2018 Oct 8;28(19):3044-3055.e5. doi: 10.1016/j.cub.2018.07.038. Epub 2018 Sep 27.
6
Topographic precision in sensory and motor corticostriatal projections varies across cell type and cortical area.感觉运动皮质纹状体投射的地形精度因细胞类型和皮质区域而异。
Nat Commun. 2018 Sep 3;9(1):3549. doi: 10.1038/s41467-018-05780-7.
7
Fast-Spiking Interneurons Supply Feedforward Control of Bursting, Calcium, and Plasticity for Efficient Learning.快速棘突神经元提供爆发、钙和可塑性的前馈控制,以实现高效学习。
Cell. 2018 Feb 8;172(4):683-695.e15. doi: 10.1016/j.cell.2018.01.005.
8
Reconstructing the functional connectivity of multiple spike trains using Hawkes models.使用 Hawkes 模型重建多个尖峰序列的功能连接。
J Neurosci Methods. 2018 Mar 1;297:9-21. doi: 10.1016/j.jneumeth.2017.12.026. Epub 2017 Dec 30.
9
A comprehensive excitatory input map of the striatum reveals novel functional organization.一份全面的纹状体兴奋性输入图谱揭示了新的功能组织。
Elife. 2016 Nov 28;5:e19103. doi: 10.7554/eLife.19103.
10
Surrogate Data Methods Based on a Shuffling of the Trials for Synchrony Detection: The Centering Issue.基于试验洗牌的同步检测替代数据方法:中心化问题。
Neural Comput. 2016 Nov;28(11):2352-2392. doi: 10.1162/NECO_a_00839.