Reinstatement and transformation of memory traces for recognition

IF 12.5 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Science Advances Pub Date : 2025-02-19
Elias M. B. Rau, Marie-Christin Fellner, Rebekka Heinen, Hui Zhang, Qin Yin, Parisa Vahidi, Malte Kobelt, Eishi Asano, Olivia Kim-McManus, Shifteh Sattar, Jack J. Lin, Kurtis I. Auguste, Edward F. Chang, David King-Stephens, Peter B. Weber, Kenneth D. Laxer, Robert T. Knight, Elizabeth L. Johnson, Noa Ofen, Nikolai Axmacher
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引用次数: 0

Abstract

Episodic memory relies on the formation and retrieval of content-specific memory traces. In addition to their veridical reactivation, previous studies have indicated that traces may undergo substantial transformations. However, the exact time course and regional distribution of reinstatement and transformation during recognition memory have remained unclear. We applied representational similarity analysis to human intracranial electroencephalography to track the spatiotemporal dynamics underlying the reinstatement and transformation of memory traces. Specifically, we examined how reinstatement and transformation of item-specific representations across occipital, ventral visual, and lateral parietal cortices contribute to successful memory formation and recognition. Our findings suggest that reinstatement in temporal cortex and transformation in parietal cortex coexist and provide complementary strategies for recognition. Further, we find that generalization and differentiation of neural representations contribute to memory and probe memory-specific correspondence with deep neural network (DNN) model features. Our results suggest that memory formation is particularly supported by generalized and mnemonic representational formats beyond the visual features of a DNN.

Abstract Image

用于识别的记忆痕迹的恢复和转化
情景记忆依赖于特定内容记忆痕迹的形成和检索。除了它们的真实再激活,以前的研究表明,痕迹可能经历实质性的转变。然而,识别记忆过程中恢复和转化的确切时间过程和区域分布尚不清楚。我们运用表征相似性分析方法对人颅内脑电图进行分析,以追踪记忆痕迹恢复和转化的时空动态。具体来说,我们研究了枕叶、腹侧视觉和外侧顶叶皮层中特定项目表征的恢复和转化如何有助于成功的记忆形成和识别。我们的研究结果表明,颞叶皮层的恢复和顶叶皮层的转化是共存的,并为识别提供了互补的策略。此外,我们发现神经表征的泛化和分化有助于记忆,并探索与深度神经网络(DNN)模型特征的记忆特定对应关系。我们的研究结果表明,除了深度神经网络的视觉特征之外,记忆形成特别受到广义和助记的表征格式的支持。
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来源期刊
Science Advances
Science Advances 综合性期刊-综合性期刊
CiteScore
21.40
自引率
1.50%
发文量
1937
审稿时长
29 weeks
期刊介绍: Science Advances, an open-access journal by AAAS, publishes impactful research in diverse scientific areas. It aims for fair, fast, and expert peer review, providing freely accessible research to readers. Led by distinguished scientists, the journal supports AAAS's mission by extending Science magazine's capacity to identify and promote significant advances. Evolving digital publishing technologies play a crucial role in advancing AAAS's global mission for science communication and benefitting humankind.
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