Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws

Abstract How can we reconcile the massive fluctuations in neural connections with a stable long-term memory? Two-photon microscopy studies have revealed that large portions of neural connections (spines, synapses) are unexpectedly active, changing unpredictably over time. This appears to invalidate...

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Main Author: Jaap M. J. Murre
Format: Article
Language:English
Published: Nature Portfolio 2022-08-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-17639-5
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author Jaap M. J. Murre
author_facet Jaap M. J. Murre
author_sort Jaap M. J. Murre
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description Abstract How can we reconcile the massive fluctuations in neural connections with a stable long-term memory? Two-photon microscopy studies have revealed that large portions of neural connections (spines, synapses) are unexpectedly active, changing unpredictably over time. This appears to invalidate the main assumption underlying the majority of memory models in cognitive neuroscience, which rely on stable connections that retain information over time. Here, we show that such random fluctuations may in fact implement a type of memory consolidation mechanism with a stable very long-term memory that offers novel explanations for several classic memory ‘laws’, namely Jost’s Law (1897: superiority of spaced learning) and Ribot’s Law (1881: loss of recent memories in retrograde amnesia), for which a common neural basis has been postulated but not established, as well as other general ‘laws’ of learning and forgetting. We show how these phenomena emerge naturally from massively fluctuating neural connections.
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spelling doaj.art-fb92e3866c9e469fa71646e7e323b8d32022-12-22T02:32:07ZengNature PortfolioScientific Reports2045-23222022-08-011211810.1038/s41598-022-17639-5Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory lawsJaap M. J. Murre0Brain and Cognition Unit, Psychology Department, University of AmsterdamAbstract How can we reconcile the massive fluctuations in neural connections with a stable long-term memory? Two-photon microscopy studies have revealed that large portions of neural connections (spines, synapses) are unexpectedly active, changing unpredictably over time. This appears to invalidate the main assumption underlying the majority of memory models in cognitive neuroscience, which rely on stable connections that retain information over time. Here, we show that such random fluctuations may in fact implement a type of memory consolidation mechanism with a stable very long-term memory that offers novel explanations for several classic memory ‘laws’, namely Jost’s Law (1897: superiority of spaced learning) and Ribot’s Law (1881: loss of recent memories in retrograde amnesia), for which a common neural basis has been postulated but not established, as well as other general ‘laws’ of learning and forgetting. We show how these phenomena emerge naturally from massively fluctuating neural connections.https://doi.org/10.1038/s41598-022-17639-5
spellingShingle Jaap M. J. Murre
Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws
Scientific Reports
title Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws
title_full Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws
title_fullStr Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws
title_full_unstemmed Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws
title_short Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws
title_sort randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws
url https://doi.org/10.1038/s41598-022-17639-5
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