Neurophysiological circuits underlying episodic memory formation in the human brain

学习和记忆的形成在我们的日常生活中起着核心作用。我们获取新信息和记住以前事件的能力的关键性质表现在影响记忆的各种条件中，从儿童的学习缺陷一直到老年痴呆症和相关疾病。以往的研究表明，海马及内侧颞叶（MTL）的相关结构在位置、图像或词语记忆的形成中起着关键作用。关于时空序列和事件是如何被编码和回忆的，我们知道的很少。通过使用电影作为真实生活中情节事件记忆形成的代理。哈佛医学院儿童医院的Gabriel Kreiman博士将研究人类MTL中的神经元活动如何支持电影事件期间情节记忆的形成，以及海马体如何在学习期间与新皮层区域和杏仁核相互作用。计算机视觉和机器学习技术将用于开发电影事件的详细注释，以根据其低级属性，高级内容和情感方面来表征每个帧序列，并从视听和情感内容中预测情节事件的记忆形成。此外，研究人员将联合收割机这种机器学习方法与癫痫患者MTL中的神经生理学记录相结合，以了解短片事件的情景记忆是如何编码和回忆的。推进我们对协调记忆形成的神经回路和相互作用的理解，将为开发创新技术铺平道路，直接与人类MTL互动，以增强对日常情景信息的学习。 这里使用的机器学习方法来描述复杂视听事件的回忆，将提供系统的指标来预测记忆的形成，因此有望在教育环境中预测回忆。研究海马神经元如何参与情景事件的编码和回忆，可能对理解和开发神经系统疾病（包括儿童年龄相关性痴呆和学习障碍）的干预措施具有重要的临床意义。 该项目还将为本科生，研究生和博士后研究员提供宝贵的培训机会，以获得涉及计算机科学，认知科学，神经生理学和神经学的前沿跨学科研究的第一手经验。

Learning and memory formation play a central role in our everyday lives. The critical nature of our ability to acquire new information and to remember previous events is manifested in a variety of conditions that affect memory ranging from learning deficits in children all the way to Alzheimer's disease and related conditions in the elderly. Previous studies have shown that the hippocampus and related structures in the medial temporal lobe (MTL) play a critical role in memory formation for places, images or words. Less is known about how spatiotemporal sequences and events are encoded and recalled. By using movies as a proxy to real-life memory formation for episodic events. Dr. Gabriel Kreiman at the Children's Hospital, Harvard Medical School, will investigate how neuronal activity in the human MTL support the formation of episodic memories during movie events and how the hippocampus interacts with neocortical areas and the amygdala during learning. Computer vision and machine learning techniques will be used to develop detailed annotations of movie events to characterize each sequence of frames in terms of its low-level properties, high-level content and emotional aspects and to predict memory formation for episodic events from audiovisual and emotional content. Furthermore, the researchers will combine this machine learning approach with neurophysiological recordings in the MTL of patients with epilepsy to understand how episodic memories for short movie events are encoded and recalled. Advancing our understanding of the neural circuits and interactions that orchestrate memory formation will pave the way towards developing innovative technologies to directly interact with the human MTL to enhance learning of everyday episodic information. The machine learning approach used here to characterize recall of complex audio-visual events will provide systematic metrics to make predictions about memory formation, therefore holding the promise of predicting recall in educational settings. Studying how hippocampal neurons participate in the encoding and recall of episodic events is likely to have major clinical implications for understanding and developing interventions for neurological conditions including age-related dementias and learning disabilities in children. This project will also offer valuable training opportunities for undergraduate students, graduate students and postdoctoral fellows to gain first hand experience in a cutting edge interdisciplinary study involving computer science, cognitive science, neurophysiology and neurology.