Understanding Changes in Hippocampal Representations by Measuring Memories with Natural Language Processing

项目总结 海马体在编码长期情节记忆方面起着至关重要的作用。然而，因为 我们编码的许多体验共享相似的特征(人、位置、对象)，这是一个关键的挑战 对于情节记忆系统来说，是为了防止这些记忆之间的干扰或混淆。近期 人类神经成像研究表明，高度相似的事件可以引发对 在海马体内的对应表示，使得几乎相同的事件与 明显不同的活动模式。关键的是，有证据表明，海马区的排斥是适应的 它与减少的内存干扰有关。然而，一个根本的开放问题是， 或者海马体的排斥如何影响记忆的实际内容。解决这个问题需要 精确表征行为和神经表达的潜在细微差异的方法 内存内容。在本提案中，我将利用自然语言处理(NLP)算法来 将言语回忆的度量转换为多维空间中的文本嵌入(即数字向量 语义空间。这些文本嵌入将使我能够量化高度相似的记忆的相似性 自然场景图像。此外，我还将接受高级功能磁共振成像方法和计算方面的新培训 这些分析将使我能够刻画记忆的行为表现并将其与相应的 在海马体内的表现。我的中心假设是海马区的排斥力 当出现以下情况时，表征将与相似场景刺激之间的差异的夸大相关 他们会被口头召回。这一假设和我的方法的可行性得到了一个初步的 我进行的一项研究证实，NLP方法对相似程度上的细微扭曲很敏感 场景图像会被记住。在目标1中，使用NLP方法和行为记忆范式，我将 检验这样一种假设，即记忆内容的扭曲是通过竞争的有针对性的“动作”来解释的 在高维的语义空间中，彼此远离的记忆。在目标2中，我将检验假设 记忆内容的变化(通过NLP方法测量)是通过对 海马区的表现。除了用新的神经成像技术支持我的训练 计算方法，这个项目将对海马体如何分解产生重要的新见解 相似记忆之间的干扰。此外，技术和方法的具体结合 我将采用的这项技术有可能在情节记忆领域开辟新的研究途径。在……里面 综上所述，这项研究将支持我开发创新方法的长期目标 海马体如何支持情节记忆的有效存储。

PROJECT SUMMARY The hippocampus plays an essential role in encoding long-term episodic memories. However, because many of the experiences we encode share similar features (people, locations, objects), a critical challenge for the episodic memory system is to prevent interference or confusion between these memories. Recent human neuroimaging studies have revealed that highly similar events can trigger a “repulsion” of corresponding representations within the hippocampus such that nearly identical events are associated with markedly different activity patterns. Critically, there is evidence that hippocampal repulsion is adaptive in that it is associated with reduced memory interference. However, a fundamental open question is whether or how hippocampal repulsion impacts the actual contents of memories. Addressing this question requires methods for precisely characterizing potentially subtle differences in behavioral and neural expressions of memory content. In this proposal, I will leverage Natural Language Processing (NLP) algorithms to transform measures of verbal recall into text embeddings (i.e., numerical vectors) within a multidimensional semantic space. These text embeddings will allow me to quantify the similarity of memories for highly similar natural scene images. Additionally, I will gain new training in advanced fMRI methods and computational analyses that will allow me to characterize and relate behavioral expressions of memory to corresponding representations within the hippocampus. My central hypothesis is that repulsion of hippocampal representations will be associated with the exaggeration of differences between similar scene stimuli when they are verbally recalled. This hypothesis and the feasibility of my approach is supported by a preliminary study I have conducted which validates that NLP methods are sensitive to subtle distortions in how similar scene images are remembered. In Aim 1, using NLP methods and a behavioral memory paradigm, I will test the hypothesis that distortions in memory content are explained by a targeted “movement” of competing memories away from each other in a high-dimensional semantic space. In Aim 2, I will test the hypothesis that changes in memory content (measured by NLP methods) are predicted by the degree of repulsion of hippocampal representations. In addition to supporting my training with new neuroimaging and computational methods, this project will yield important new insight into how the hippocampus resolves interference between similar memories. Moreover, the specific combination of techniques and approaches that I will employ have the potential to open up new avenues of research in the field of episodic memory. In summary, this research will support my long-term objective of developing innovative methods to understand how the hippocampus supports the efficient storage of episodic memories.