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

通过自然语言处理测量记忆来了解海马表征的变化

• 批准号: 10826461
• 负责人: Anisha Babu
• 金额: $ 4.7万
• 依托单位: UNIVERSITY OF OREGON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-16 至 2026-09-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10826461
• 关键词: Address; Algorithms; Behavioral; Brain; Code; Computer Analysis; Computing Methodologies; Confusion; Data; Dimensions; Episodic memory; Event; Functional Magnetic Resonance Imaging; Goals; Hippocampus; Human; Image; Link; Location; Measures; Memory; Methodological Studies; Methods; Movement; Natural Language Processing; Pattern; Persons; Play; Research; Role; Semantics; Shapes; Stimulus; Structure; System; Techniques; Testing; Text; Training; cognitive neuroscience; experience; forgetting; high dimensionality; innovation; insight; member; neural; neuroimaging; novel; novel strategies; prevent; skills; vector; verbal

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.

项目摘要 海马在编码长期情节记忆中起着至关重要的作用。但是，因为 我们编码的许多经验共享相似的功能（人，位置，对象），这是一个关键的挑战 对于情节，记忆系统是防止这些记忆之间的干扰或混乱。最近的 人类神经影像学研究表明，高度相似的事件可以触发 海马内的相应表示形式，使得几乎相同的事件与 明显不同的活动模式。至关重要的是，有证据表明海马排斥在适应性 它与减少的内存干扰有关。但是，一个基本的开放问题是是否 或海马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微妙差异的方法 内存内容。在此建议中，我将利用自然语言处理（NLP）算法 将口头回忆的测量转换为多维的文本嵌入（即数值向量） 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，我将获得高级fMRI方法和计算的新培训 可以使我表征并将内存的行为表达与相应的分析 海马内的表示。我的中心假设是海马的排斥 表示形式将与相似场景刺激之间差异的夸大有关 他们被口头召回。我的方法的这种假设和可行性得到了初步的支持 研究我已经进行了验证NLP方法对微妙的扭曲敏感的情况下的研究 场景图像被记住。在AIM 1中，使用NLP方法和行为内存范式，我将 检验以下假设，即记忆内容中的扭曲是通过竞争的目标“运动”来解释的 在高维语义空间中，回忆彼此相距。在AIM 2中，我将检验假设 记忆内容的变化（通过NLP方法衡量）通过排斥程度来预测 海马表示。除了支持我的新神经影像学培训和 计算方法，该项目将对海马如何消除的重要新见解 类似记忆之间的干扰。此外，技术和方法的具体组合 我将利用在情节记忆领域开放新的研究途径。在 总而言之，这项研究将支持我开发创新方法以了解的长期目标 海马如何支持情节记忆的有效存储。