{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T11:05:12Z","timestamp":1788347112867,"version":"build-2803163510"},"posted":{"date-parts":[[2023,4,7]]},"group-title":"Neuroscience","reference-count":170,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,4,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Influential views of systems memory consolidation posit that the hippocampus rapidly forms representations of specific events, while neocortical networks extract regularities across events, forming the basis of schemas and semantic knowledge. Neocortical extraction of schematic memory representations is thought to occur on a protracted timescale of months, especially for information that is unrelated to prior knowledge. However, this theorized evolution of memory representations across extended timescales, and differences in the temporal dynamics of consolidation across brain regions, lack reliable empirical support. To examine the temporal dynamics of memory representations, we repeatedly exposed human participants to structured information via sequences of fractals, while undergoing longitudinal fMRI for three months. Sequence-specific activation patterns emerged in the hippocampus during the first 1-2 weeks of learning, followed one week later by high-level visual cortex, and subsequently the medial prefrontal and parietal cortices. Schematic, sequence-general representations emerged in the prefrontal cortex after 3 weeks of learning, followed by the medial temporal lobe and anterior temporal cortex. Moreover, hippocampal and most neocortical representations showed sustained rather than time-limited dynamics, suggesting that representations tend to persist across learning. These results show that specific hippocampal representations emerge early, followed by both specific and schematic representations at a gradient of timescales across hippocampal-cortical networks as learning unfolds. Thus, memory representations do not exist only in specific brain regions at a given point in time, but are simultaneously present at multiple levels of abstraction across hippocampal-cortical networks.<\/jats:p>","DOI":"10.1101\/2023.04.06.535935","type":"posted-content","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T04:45:20Z","timestamp":1680842720000},"source":"Crossref","is-referenced-by-count":3,"title":["Structured memory representations develop at multiple time scales in hippocampal-cortical networks"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9200-9165","authenticated-orcid":false,"given":"Arielle","family":"Tambini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Miller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke","family":"Ehlert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasia","family":"Kiyonaga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"D\u2019Esposito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2023041111400652000_2023.04.06.535935v2.1","first-page":"14","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","year":"2014","journal-title":"Frontiers in Neuroinformatics"},{"key":"2023041111400652000_2023.04.06.535935v2.2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.91.15.7041"},{"key":"2023041111400652000_2023.04.06.535935v2.3","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.19-03-01106.1999"},{"key":"2023041111400652000_2023.04.06.535935v2.4","first-page":"1","article-title":"Retrieval as a fast route to memory consolidation","volume":"xx","year":"2017","journal-title":"Trends in Cognitive Sciences"},{"key":"2023041111400652000_2023.04.06.535935v2.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2017.12.002"},{"issue":"1","key":"2023041111400652000_2023.04.06.535935v2.6","doi-asserted-by":"crossref","first-page":"5795","DOI":"10.1038\/s41467-022-33517-0","article-title":"Schemas provide a scaffold for neocortical integration of new memories over time","volume":"13","year":"2022","journal-title":"Nature Communications"},{"key":"2023041111400652000_2023.04.06.535935v2.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"key":"2023041111400652000_2023.04.06.535935v2.8","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0251-18.2018"},{"issue":"3","key":"2023041111400652000_2023.04.06.535935v2.9","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1111\/tops.12420","article-title":"Understanding the Neural Bases of Implicit and Statistical Learning","volume":"11","year":"2019","journal-title":"Topics in Cognitive Science"},{"key":"2023041111400652000_2023.04.06.535935v2.10","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.4262-06.2006"},{"key":"2023041111400652000_2023.04.06.535935v2.11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.04.042"},{"key":"2023041111400652000_2023.04.06.535935v2.12","doi-asserted-by":"crossref","unstructured":"Bellmund, J. 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