# Learning novel structure across time and sleep > **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $399,628 ## Abstract Project Summary Acting adaptively requires quickly picking up on structure in our environment (e.g., the layout of a city you are visiting for the first time) and storing the acquired knowledge for effective future use (efficient navigation on subsequent visits). Dominant theories of the hippocampus have focused on its ability to encode individual snapshots of experience, but we and others have found evidence that it is also crucial for finding structure across experiences (understanding the relationship between different views of the same distant building). The mechanisms of this essential form of learning have not been established. We have developed a neural network model of the hippocampus instantiating the theory that one of its subfields can quickly encode structure using distributed representations, a powerful form of representation in which populations of neurons become responsive to multiple related features of the environment. The first aim of this project is to test predictions of this model using high resolution functional magnetic resonance imaging (fMRI) in paradigms requiring integration of information across experiences. The results will clarify fundamental mechanisms of how we learn novel structure, adjudicating between existing models of this process, and informing further model development. There are also competing theories as to the eventual fate of new hippocampal representations. One view posits that during sleep, the hippocampus replays recent information to build longer-term distributed representations in neocortex. Another view claims that memories are directly and independently formed and consolidated within the hippocampus and neocortex. The second aim of this project is to test between these theories. We will assess changes in hippocampal and cortical representations over time by re-scanning participants and tracking changes in memory at a one-week delay. Any observed changes in the brain and behavior across time, however, may be due to generic effects of time or to active processing during sleep. The third aim is thus to assess the specific causal contributions of sleep to the consolidation of structured information. We will use real-time sleep electroencephalography (EEG) to detect the peaks of slow oscillations, when endogenous replay is known to occur, and play sound cues to bias memory reactivation. We will also expand our neural network model to examine how offline hippocampal replay of recent regularities can shape distributed representations in neocortex, providing a mechanistic account of offline consolidation of structured information. We expect that this work will clarify the anatomical substrates and, critically, the nature of the representations that support encoding and consolidation of novel structure in the environment. Having a robust, neurally grounded model of these processes will help connect research in this area across laboratories and provide a framework for evaluating what goes wrong in mental he... ## Key facts - **NIH application ID:** 10808107 - **Project number:** 5R01MH129436-02 - **Recipient organization:** UNIVERSITY OF PENNSYLVANIA - **Principal Investigator:** Anna C Schapiro - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $399,628 - **Award type:** 5 - **Project period:** 2023-03-15 → 2028-02-29 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10808107 ## Citation > US National Institutes of Health, RePORTER application 10808107, Learning novel structure across time and sleep (5R01MH129436-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10808107. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*