Modeling the Neural Basis of Episodic Memory

描述（由申请人提供）：本研究的目标是开发和测试大脑如何产生情景记忆的机制明确的理论：我们回忆以前经历的事件的能力，并识别以前经历过的事件。多年来，研究人员已经知道海马体是回忆的关键神经基质;最近，几项研究发现，在局灶性海马损伤后，内侧颞叶皮层（MTLC）可以自己支持一定程度的备用识别性能。为了探索海马和MTLC的贡献如何不同，本文提出的研究使用这些结构的神经网络模型来模拟正常和脑损伤受试者的记忆表现模式。第一个具体的目标是测试模型的预测，当项目和联想识别性能将受到影响敲除海马的贡献，“敲除”将通过使用患者与局灶性海马损伤，也正常受试者被迫迅速作出反应。其他实验将测试模型预测，关于上下文变化和干扰操作如何影响这两个系统中的识别。第二个具体目标是使用组合的皮质-海马模型来运行模拟，以解决这两个过程如何共同确定识别性能（例如，在将这两个过程对立起来的范例中），并在健忘症患者和对照组中进行实验来测试这些预测。现存的识别记忆的正式模型不与潜在的神经生物学联系，因此，这项研究构成了识别记忆建模的重要一步。这种与神经生物学的联系提供了额外的限制，可以利用这些限制来获得对记忆文献中基本难题的新见解。关于健康福利：该模型处理病变数据的能力将加强我们对不同类型脑损伤后哪些学习得以保留的理解，这（反过来）将有助于医生开发更有效的康复方案。此外，随着研究人员开始开发改变大脑学习基本参数的疗法，我们将需要某种方法来评估这些变化如何扩大并影响行为记忆表现;这里提出的研究非常适合满足这种不断增长的需求。

DESCRIPTION (provided by applicant): The goal of this research is to develop and test a mechanistically explicit theory of how the brain gives rise to episodic memory: our ability to recall previously experienced events, and to recognize events as having been experienced previously. Researchers have known for years that the hippocampus is the key neural substrate of recall; more recently, several studies have found that -- after focal hippocampal damage -- medial temporal lobe cortex (MTLC) can support some degree of spared recognition performance on its own. To explore how the contributions of hippocampus and MTLC differ, the research proposed here uses neural network models of these structures to simulate patterns of memory performance from normal and brain-damaged subjects. The first specific aim is to test the model's predictions regarding when item and associative recognition performance will be affected by knocking out the hippocampal contribution; "knockout" will be operationalized by using patients with focal hippocampal damage, and also normal subjects who are forced to respond quickly. Other experiments will test model predictions regarding how context change and interference manipulations affect recognition in the two systems. The second specific aim is to run simulations using a combined cortico-hippocampal model to address how the two processes conjointly determine recognition performance (e.g., in paradigms that place the two processes in opposition), and to run experiments in amnesic patients and controls to test these predictions. Extant formal models of recognition memory do not make contact with the underlying neurobiology; as such, this research constitutes a major step forward in recognition memory modeling. This link to neurobiology provides extra constraints that can be leveraged to gain new insights into fundamental puzzles in the memory literature. Regarding health benefits: The model's ability to address lesion data will bolster our understanding of what kinds of learning are spared after different kinds of brain damage, which (in turn) will help doctors develop more effective rehabilitation regimes. Furthermore, as researchers start to develop therapies that change the underlying parameters of learning in the brain, we will need some way of assessing how these changes scale up and affect behavioral memory performance; the research proposed here is ideally positioned to meet this growing need.