A dynamic attractor network model of memory formation, reinforcement and forgetting.
A dynamic attractor network model of memory formation, reinforcement and forgetting.
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DOI：
10.1371/journal.pcbi.1011727
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发表时间：
2023-12
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
经验证据表明，经常重温的记忆很容易回忆起来，熟悉的项目比不熟悉的项目涉及更大的海马表征。根据这些观察结果，在这里，我们开发了一种建模方法，以提供一个机械的理解海马神经组件如何不同地演变，这取决于刺激的呈现频率。为此，我们在速率吸引子网络模型中添加了在线赫布学习规则，背景放电活动，神经适应和异突触可塑性，从而创建了动态记忆表示，可以根据相应记忆模式的呈现频率持续，增加或减弱。具体来说，我们表明，赫布学习和背景放电活动之间的动态相互作用可以解释的内存组件的大小和刺激频率之间的关系。频繁刺激的组件彼此独立地增加它们的大小（即创建不共享神经元的正交表示，从而避免干扰）。重要的是，没有被进一步刺激的集合体的神经元之间的连接变得不稳定，使得这些神经元可以被其他集合体招募，从而提供了遗忘的神经元机制。实验证据表明，熟悉的项目是由更大的海马神经元组件比不熟悉的。根据这一发现，我们的计算模型表明，记忆组件的大小取决于其回忆的频率（即频率越高，组件越大），这可以通过在线学习和背景放电活动的相互作用来解释。此外，我们发现，代表不相关记忆的组件在保持正交的同时增加了它们的大小，这与单细胞记录的结果一致。为了模拟这些实证研究结果，我们建议超越标准的吸引子网络记忆模型，而是使用动态模型来研究记忆编码。
Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic understanding of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting. Experimental evidence suggests that familiar items are represented by larger hippocampal neuronal assemblies than less familiar ones. In line with this finding, our computational model shows that the size of memory assemblies depends on the frequency of their recall (i.e. the higher the frequency, the larger the assembly), which can be explained by the interplay of online learning and background firing activity. Furthermore, we find that assemblies representing uncorrelated memories increase their sizes while remaining orthogonal, in line with findings with single-cell recordings. To model these empirical findings, we propose to go beyond the standard attractor network memory models and use instead a dynamic model to study memory coding.
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影响因子：
16.6
作者：
De Falco, Emanuela;Ison, Matias J.;Fried, Itzhak;Quiroga, Rodrigo Quian
通讯作者：
Quiroga, Rodrigo Quian
DOI：
10.1126/science.aao0862
发表时间：
2018-06-22
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
El-Boustani S;Ip JPK;Breton-Provencher V;Knott GW;Okuno H;Bito H;Sur M
通讯作者：
Sur M
影响因子：
24.8
作者：
Leuner B;Gould E
通讯作者：
Gould E
影响因子：
7.7
作者：
Fauth, Michael Jan;van Rossum, Mark C. W.
通讯作者：
van Rossum, Mark C. W.
影响因子：
1.2
作者：
Jolivet, Renaud;Rauch, Alexander;Gerstner, Wulfram
通讯作者：
Gerstner, Wulfram