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学习率的对称性在突触可塑性中调节灵活和稳定记忆的形成。

Symmetry of learning rate in synaptic plasticity modulates formation of flexible and stable memories.

机构信息

Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

出版信息

Sci Rep. 2017 Jul 18;7(1):5671. doi: 10.1038/s41598-017-05929-2.

DOI:10.1038/s41598-017-05929-2
PMID:28720795
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5516032/
Abstract

Spike-timing-dependent plasticity (STDP) is considered critical to learning and memory functions in the human brain. Across various types of synapse, STDP is observed as different profiles of Hebbian and anti-Hebbian learning rules. However, the specific roles of diverse STDP profiles in memory formation still remain elusive. Here, we show that the symmetry of the learning rate profile in STDP is crucial to determining the character of stored memory. Using computer simulations, we found that an asymmetric learning rate generates flexible memory that is volatile and easily overwritten by newly appended information. Moreover, a symmetric learning rate generates stable memory that can coexist with newly appended information. In addition, by combining these two conditions, we could realize a hybrid memory type that operates in a way intermediate between stable and flexible memory. Our results demonstrate that various attributes of memory functions may originate from differences in the synaptic stability.

摘要

译文: 时程依赖型可塑性(STDP)被认为对人类大脑的学习和记忆功能至关重要。在各种类型的突触中,STDP 表现为不同的赫布和反赫布学习规则的模式。然而,多样化的 STDP 模式在记忆形成中的具体作用仍然难以捉摸。在这里,我们表明 STDP 中学习率曲线的对称性对于确定存储记忆的特征至关重要。通过计算机模拟,我们发现非对称的学习率产生灵活的记忆,这种记忆易变,容易被新附加的信息覆盖。此外,对称的学习率产生稳定的记忆,它可以与新附加的信息共存。此外,通过结合这两种情况,我们可以实现一种混合记忆类型,其工作方式介于稳定记忆和灵活记忆之间。我们的结果表明,记忆功能的各种属性可能源于突触稳定性的差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/399aa7561aea/41598_2017_5929_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/f79486fb2272/41598_2017_5929_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/6c36eb3e45b1/41598_2017_5929_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/3eec2682afd4/41598_2017_5929_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/8e21f826d22b/41598_2017_5929_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/399aa7561aea/41598_2017_5929_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/f79486fb2272/41598_2017_5929_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/6c36eb3e45b1/41598_2017_5929_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/3eec2682afd4/41598_2017_5929_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/8e21f826d22b/41598_2017_5929_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe8/5516032/399aa7561aea/41598_2017_5929_Fig5_HTML.jpg

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2
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Nat Commun. 2016 May 13;7:11552. doi: 10.1038/ncomms11552.
3
Gamma and Beta Bursts Underlie Working Memory.
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Brain Behav. 2021 Aug;11(8):e2266. doi: 10.1002/brb3.2266. Epub 2021 Jun 22.
4
Visual number sense in untrained deep neural networks.未训练的深度神经网络中的视觉数字感知。
Sci Adv. 2021 Jan 1;7(1). doi: 10.1126/sciadv.abd6127. Print 2021 Jan.
5
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J Neurosci. 2020 Aug 19;40(34):6584-6599. doi: 10.1523/JNEUROSCI.0649-20.2020. Epub 2020 Jul 17.
6
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Elife. 2020 Apr 21;9:e56053. doi: 10.7554/eLife.56053.
7
Learning spatiotemporal signals using a recurrent spiking network that discretizes time.利用对时间进行离散化的循环尖峰网络来学习时空信号。
PLoS Comput Biol. 2020 Jan 21;16(1):e1007606. doi: 10.1371/journal.pcbi.1007606. eCollection 2020 Jan.
8
Biological learning curves outperform existing ones in artificial intelligence algorithms.生物学习曲线在人工智能算法中优于现有算法。
Sci Rep. 2019 Aug 9;9(1):11558. doi: 10.1038/s41598-019-48016-4.
9
Stationary log-normal distribution of weights stems from spontaneous ordering in adaptive node networks.权重的固定对数正态分布源于自适应节点网络中的自发有序。
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10
Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.自适应节点通过链接丰富了超越传统自适应的非线性协作学习。
Sci Rep. 2018 Mar 23;8(1):5100. doi: 10.1038/s41598-018-23471-7.
伽马暴和贝塔暴构成工作记忆的基础。
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4
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5
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6
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Nature. 2015 Sep 17;525(7569):333-8. doi: 10.1038/nature15257. Epub 2015 Sep 9.
7
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Nat Rev Neurosci. 2015 Sep;16(9):521-34. doi: 10.1038/nrn4000.
8
Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks.多种突触可塑性机制协同作用,在脉冲神经网络中形成和检索记忆。
Nat Commun. 2015 Apr 21;6:6922. doi: 10.1038/ncomms7922.
9
Bidirectional switch of the valence associated with a hippocampal contextual memory engram.与海马体情境记忆印迹相关的效价双向转换
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10
Neurons are recruited to a memory trace based on relative neuronal excitability immediately before training.神经元是根据训练前的相对神经元兴奋性被募集到记忆痕迹中的。
Neuron. 2014 Aug 6;83(3):722-35. doi: 10.1016/j.neuron.2014.07.017.