Decoding memory reprocessing during sleep

• Applicant: Professorin Dr. Monika Schönauer
• Subject Area: General, Cognitive and Mathematical Psychology
• Term: from 2018 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 403173771

After encoding, new memories have to be transformed to last for the long-term. During the course of this consolidation, the neural substrate supporting new memories shifts from hippocampal to neocortical regions, a process that has been termed systems memory consolidation. Sleep has been proposed to play an important role in this transition. By a reactivation of learning-related neuronal activity, which emanates from the hippocampus and spreads across the cortex, information may be transferred to neural systems that are less plastic than the network that initially encoded the memory, but consequently offer more stable memory storage. Until recently, it has been difficult to study the covert processes that support memory consolidation during sleep in humans. With the introduction of machine learning methods to cognitive neuroscience, it has become possible to detect the hidden patterns in brain activity that reflect processing of learning-related information. In the proposed project, I will examine in more detail the properties of reactivation as a key mechanism underlying the beneficial effects of sleep on memory. Together with my host Prof. Kenneth Norman of the Princeton Computational Memory Lab, who is expert in multivariate pattern analysis, I will develop machine learning approaches to decode memory reprocessing during sleep. A main objective will be to identify promising data transformations that can be used as features for pattern classification analysis. These should keep nuisance variance between individuals and recording sessions minimal, while maximizing the information related to the content of memory processing. These methods will then be applied to study the properties of both spontaneous memory reactivation and targeted memory reactivation using external reactivation cues. In particular, we will (1) investigate oscillatory activity that contributes to memory consolidation, (2) examine at what times of the night different regions of the sleeping brain process learning-related information, and (3) describe how different regions in the sleeping brain interact when processing learning-related information. Our results can help to clarify the covert processes the lead to stable memory storage in the human brain. By identifying which oscillatory events are involved in reprocessing mnemonic material and how this activity interacts across different brain regions, we can get a better understanding of how sleep contributes to systems memory consolidation. In the future, this may help to effectively target these mechanisms to facilitate long-term memory formation.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Dr. Kenneth Andrew Norman