Neural mechanisms for reducing interference during episodic memory formation

项目摘要/摘要 成功记忆的最大挑战之一是可能会产生混淆或干扰 在相似的记忆之间。对于我们存储在内存中的每个密码、名称或停车位，都有 我们已经学过或将来会学到的许多其他密码、名字或停车位。 虽然干扰是相对良性但令人讨厌的遗忘的一个因素，但它也是一种 临床上因衰老和/或痴呆症而发生的重大健忘病例的主要因素。因此，在那里 是理解支持类似的获取/提取的神经机制的基本需要 记忆，同时最大限度地减少干扰和相应的遗忘。 情景记忆的计算模型强调了模式分离在记忆记忆中的关键作用 减少内存干扰。模式分离涉及对相似的记忆进行编码，以使差异 记忆之间的关系被夸大了，从而将混淆的可能性降至最低。趁有机会 普遍同意模式分离是由海马体实现的--以及那个模式 隔离对于减少内存干扰很重要--在我们的 了解模式分离发生的方式、时间和原因。特别是，仍然存在模棱两可的情况，因为 就(A)可在何种学习环境中招募每一种机制而言，(B)相应的 每种机制的神经特征是，以及(C)与 每个机构的接合。 我们建议对发生集成和模式分离的上下文进行系统调查 目标是使用复杂、尖端的神经成像(FMRI)技术来识别分布模式 神经活动是每种机制的诊断。重要的是，我们还计划使用这些观察到的 预测行为的神经活动模式--即整合与分离的神经证据 记忆现象，包括与干扰有关的遗忘。这项研究代表了一项强有力的综合 心理学和神经科学的问题，重点是学习机制 借鉴机器学习和数据领域的计算模型和分析方法 采矿。

Project Summary/Abstract One of the biggest challenges to successful remembering is the potential for confusion or interference between similar memories. For every password, name, or parking space that we store in memory, there are many other passwords, names or parking spaces that we have already learned or will learn in the future. While interference is a factor in relatively benign—yet annoying—examples of ‘normal’ forgetting, it is also a major factor in clinically significant examples of forgetting that occur with aging and/or dementia. Thus, there is a fundamental need to understand the neural mechanisms that support the acquisition/retrieval of similar memories while minimizing interference and corresponding forgetting. Computational models of episodic memory have emphasized the critical role of pattern separation in reducing memory interference. Pattern separation involves coding similar memories such that differences between memories are exaggerated and the potential for confusion is thereby minimized. While there is general agreement that pattern separation is implemented by the hippocampus—and that pattern separation is important for reducing memory interference—there remain several fundamental gaps in our understanding of how, when, and why pattern separation occurs. In particular, there remains ambiguity as far as (a) the learning contexts in which each mechanism might be recruited, (b) what the corresponding neural signatures of each mechanism are, and (c) the specific behavioral consequences associated with the engagement of each mechanism. We propose a systematic investigation of the contexts in which integration and pattern separation occur with the goal of using sophisticated, cutting-edge neuroimaging (fMRI) techniques to identify distributed patterns of neural activity that are diagnostic of each mechanism. Critically, we also plan to use these observed patterns of neural activity—that is, neural evidence for integration vs. separation—to predict behavioral memory phenomena, including interference-related forgetting. The research represents a strong synthesis of psychology and neuroscience questions with an emphasis on learning mechanisms inspired by computational models and analysis approaches that draw from the fields of machine learning and data mining.