Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany.
Institute for Systems Neuroscience, Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251, Hamburg, Germany.
Brain Struct Funct. 2024 Apr;229(3):513-529. doi: 10.1007/s00429-023-02636-9. Epub 2023 Apr 6.
Neural representations are internal brain states that constitute the brain's model of the external world or some of its features. In the presence of sensory input, a representation may reflect various properties of this input. When perceptual information is no longer available, the brain can still activate representations of previously experienced episodes due to the formation of memory traces. In this review, we aim at characterizing the nature of neural memory representations and how they can be assessed with cognitive neuroscience methods, mainly focusing on neuroimaging. We discuss how multivariate analysis techniques such as representational similarity analysis (RSA) and deep neural networks (DNNs) can be leveraged to gain insights into the structure of neural representations and their different representational formats. We provide several examples of recent studies which demonstrate that we are able to not only measure memory representations using RSA but are also able to investigate their multiple formats using DNNs. We demonstrate that in addition to slow generalization during consolidation, memory representations are subject to semantization already during short-term memory, by revealing a shift from visual to semantic format. In addition to perceptual and conceptual formats, we describe the impact of affective evaluations as an additional dimension of episodic memories. Overall, these studies illustrate how the analysis of neural representations may help us gain a deeper understanding of the nature of human memory.
神经表象是构成大脑对外界或其某些特征模型的内部脑状态。在存在感官输入的情况下，表象可能反映输入的各种特性。当不再有感知信息时，由于记忆痕迹的形成，大脑仍可以激活先前经历过的事件的表象。在这篇综述中，我们旨在描述神经记忆表象的性质，以及如何使用认知神经科学方法，主要是神经影像学来评估它们。我们讨论了如何利用代表相似性分析（RSA）和深度神经网络（DNN）等多元分析技术来深入了解神经表象的结构及其不同的表示形式。我们提供了几个最近的研究示例，证明我们不仅可以使用 RSA 来衡量记忆表象，还可以使用 DNN 来研究它们的多种形式。我们证明，除了在巩固过程中的缓慢泛化外，记忆表象在短期记忆中就已经经历语义化，表现为从视觉格式到语义格式的转变。除了感知和概念格式外，我们还描述了情感评价作为情景记忆的另一个维度的影响。总的来说，这些研究说明了对神经表象的分析如何帮助我们更深入地了解人类记忆的本质。
