Modeling the Neural Basis of Episodic Memory

描述（由申请人提供）：这项研究的目标是发展和测试大脑如何产生情景记忆的机械明确理论：我们回忆以前经历过的事件的能力，以及识别以前经历过的事件的能力。多年来，研究人员已经知道海马体是记忆的关键神经基质；最近，一些研究发现，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。为了探索海马体和MTLC的不同贡献，本研究提出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。第一个具体目标是测试模型的预测，即当剔除海马体的贡献时，项目和联想识别性能将受到影响；“敲除”将在局灶性海马损伤患者以及被迫快速反应的正常受试者中实施。其他实验将测试关于环境变化和干扰操作如何影响两个系统识别的模型预测。第二个具体目标是使用皮质-海马体联合模型进行模拟，以解决这两个过程如何共同决定识别性能（例如，在将两个过程置于对立的范式中），并在健忘症患者和对照组中进行实验以测试这些预测。现存的正式识别记忆模型与潜在的神经生物学没有联系；因此，本研究在识别记忆建模方面迈出了重要的一步。这种与神经生物学的联系提供了额外的限制，可以用来对记忆文献中的基本难题获得新的见解。关于健康益处：该模型处理损伤数据的能力将加强我们对不同类型脑损伤后哪些类型的学习得以保留的理解，这（反过来）将帮助医生制定更有效的康复方案。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，我们将需要一些方法来评估这些变化是如何扩大规模并影响行为记忆表现的；这里提出的研究是满足这一日益增长的需求的理想定位。

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.