Decoding the neural dynamics of emotion-related human memory optimization using AI-informed multivariate techniques

Episodic memory, our fascinating ability to encode and mentally relive past experiences, lies at the core of human cognition. It allows individuals not only to recall past events, but it is crucial in planning and guiding future behavior. However, among all of our daily-life experiences, only some events will be transformed into lasting memories, particularly because of their emotional salience. From the brain perspective’s, emotions are thought to sustainably recruit the amygdala, thus facilitating memory processes occurring in the hippocampus. In addition, while the first step in forming durable memories is the initial learning, it has become increasingly clear that memory reinstatement of encoding neural patterns at retrieval also plays an important role in remembering. Yet, we know very little about the precise brain mechanisms supporting emotion-related memory enhancement via neural pattern reinstatement. Here, we propose to use machine learning to establish an AI-informed multivariate approach to isolate the neural processes that are boosted by emotions and which mediate learning and retrieval enhancement.

Faculty Supervisor:

Karim Jerbi

Student:

Partner:

Universidad Politécnica de Madrid

Discipline:

Life Sciences

Sector:

Education

University:

Université de Montréal

Program: