Dendritic Spines in Learning and Memory: From First Discoveries to Current Insights

树突棘 神经科学 长时程增强 脊柱(分子生物学) 突触可塑性 神经可塑性 生物 变质塑性 认知 心理学 生物化学 受体 海马结构 分子生物学
作者
Nicolas Heck,Marc Dos Santos
出处
期刊:Advances in neurobiology 卷期号:: 311-348 被引量:2
标识
DOI:10.1007/978-3-031-36159-3_7
摘要

The central nervous system is composed of neural ensembles, and their activity patterns are neural correlates of cognitive functions. Those ensembles are networks of neurons connected to each other by synapses. Most neurons integrate synaptic signal through a remarkable subcellular structure called spine. Dendritic spines are protrusions whose diverse shapes make them appear as a specific neuronal compartment, and they have been the focus of studies for more than a century. Soon after their first description by Ramón y Cajal, it has been hypothesized that spine morphological changes could modify neuronal connectivity and sustain cognitive abilities. Later studies demonstrated that changes in spine density and morphology occurred in experience-dependent plasticity during development, and in clinical cases of mental retardation. This gave ground for the assumption that dendritic spines are the particular locus of cerebral plasticity. With the discovery of synaptic long-term potentiation, a research program emerged with the aim to establish whether dendritic spine plasticity could explain learning and memory. The development of live imaging methods revealed on the one hand that dendritic spine remodeling is compatible with learning process and, on the other hand, that their long-term stability is compatible with lifelong memories. Furthermore, the study of the mechanisms of spine growth and maintenance shed new light on the rules of plasticity. In behavioral paradigms of memory, spine formation or elimination and morphological changes were found to correlate with learning. In a last critical step, recent experiments have provided evidence that dendritic spines play a causal role in learning and memory.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
华冰夏完成签到,获得积分20
1秒前
二世小卒发布了新的文献求助10
3秒前
3秒前
4秒前
布鲁爱思完成签到,获得积分10
4秒前
4秒前
拾七发布了新的文献求助10
8秒前
10秒前
很Cool的CC发布了新的文献求助30
10秒前
13秒前
14秒前
思源应助咔嚓采纳,获得10
19秒前
执着中道发布了新的文献求助10
22秒前
25秒前
26秒前
独角兽完成签到 ,获得积分10
26秒前
个性思真发布了新的文献求助10
28秒前
华东小可爱完成签到,获得积分10
29秒前
会飞的猪完成签到,获得积分10
30秒前
吴晨曦发布了新的文献求助10
30秒前
张瀚元完成签到,获得积分10
33秒前
威威完成签到,获得积分10
35秒前
朱荧荧发布了新的文献求助10
37秒前
拼搏蝴蝶完成签到,获得积分10
38秒前
丘比特应助执着中道采纳,获得10
38秒前
40秒前
ZYRui完成签到 ,获得积分10
44秒前
46秒前
谦让成协发布了新的文献求助20
48秒前
JamesPei应助科研通管家采纳,获得10
50秒前
50秒前
Lucas应助顺利映天采纳,获得10
50秒前
Owen应助科研通管家采纳,获得10
50秒前
Owen应助科研通管家采纳,获得30
50秒前
香蕉觅云应助科研通管家采纳,获得10
50秒前
bkagyin应助科研通管家采纳,获得10
50秒前
科研通AI2S应助科研通管家采纳,获得10
50秒前
乐乐应助科研通管家采纳,获得10
50秒前
小马甲应助科研通管家采纳,获得10
50秒前
丘比特应助科研通管家采纳,获得10
51秒前
高分求助中
Sustainable Land Management: Strategies to Cope with the Marginalisation of Agriculture 1000
Corrosion and Oxygen Control 600
Python Programming for Linguistics and Digital Humanities: Applications for Text-Focused Fields 500
Heterocyclic Stilbene and Bibenzyl Derivatives in Liverworts: Distribution, Structures, Total Synthesis and Biological Activity 500
重庆市新能源汽车产业大数据招商指南(两链两图两池两库两平台两清单两报告) 400
Division and square root. Digit-recurrence algorithms and implementations 400
行動データの計算論モデリング 強化学習モデルを例として 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2548055
求助须知:如何正确求助?哪些是违规求助? 2176407
关于积分的说明 5604404
捐赠科研通 1897247
什么是DOI,文献DOI怎么找? 946780
版权声明 565419
科研通“疑难数据库(出版商)”最低求助积分说明 503913