Neural mechanisms for reducing interference during episodic memory formation

描述（由申请人提供）：成功记忆的最大挑战之一是相似记忆之间的混淆或干扰。对于我们存储在内存中的每个密码、名称或停车位，还有许多我们已经学习或将来将要学习的其他密码、名称或停车位。虽然干扰是相对良性但有益的“正常”遗忘例子中的一个因素，但它阿索是衰老和/或痴呆症发生的临床显著遗忘例子中的一个主要因素。因此，有一个基本的需要，以了解神经机制，支持收购/检索类似的记忆，同时尽量减少干扰和相应的遗忘。情景记忆的计算模型提出了两个核心机制，被认为是减少干扰相关的遗忘：整合和模式分离。整合包括将重叠的记忆“融合”成一个共同的表征，这样这些记忆之间的关系更像是互补而不是竞争。模式分离涉及相似存储器的正交化，使得存储器之间的差异被夸大并且干扰的可能性被最小化。虽然人们普遍认为这些机制在理论上是有吸引力的，并提供了明确的计算优势，明确的证据，这些学习机制是如何以及何时调用-特别是在人类-仍然令人惊讶的有限？特别是，就（a）每种机制可能被招募的学习环境，（B）每种机制的相应神经签名是什么，以及（c）与每种机制的参与相关的特定行为后果而言，仍然存在模糊性。我们提出了一个系统的调查的背景下，整合和模式分离发生的目标是使用复杂的，尖端的神经成像（fMRI）技术，以确定分布式模式的神经活动，诊断每种机制。重要的是，我们还计划使用这些观察到的神经活动模式，即整合与分离的神经证据来预测行为记忆现象，包括与干扰相关的遗忘。该研究代表了心理学和神经科学问题的强有力的综合，重点是受机器学习和数据挖掘领域的计算模型和分析方法启发的学习机制。

DESCRIPTION (provided by applicant): 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 aso 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 proposed two core mechanisms that are thought to reduce interference-related forgetting: integration and pattern separation. Integration involves 'fusing' overlapping memories into a common representation such that the relationship between these memories is more complementary than competitive. Pattern separation involves the orthogonalization of similar memories such that differences between memories are exaggerated and the potential for interference minimized. While there is general agreement that these mechanisms are theoretically appealing and offer clear computational advantages, clear evidence for how and when these learning mechanisms are invoked- particularly in humans-remains surprisingly limited? 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.