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Sleep Science Podcast

Penny Lewis
Sleep Science Podcast
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  • S3E5 - Anna Schapiro - Can we model the brain?
    How are our memories represented in the brain? We interview Professor Anna Schapiro from University of Pennsylvania on her work covering computational models, memory and sleep. We discuss neural network models, the Complementary Learning Systems theory and Anna's recent work on testing predictions of neural network models with TMR/EEG.Find out more about Anna and her research group.Why there are complementary learning systems in the hippocampus and neocortex, 1995Methods for reducing interference in the complementary learning systems model, 2005A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation, 2022A neural network model of hippocampal contributions to category learning, 2023Memory reactivation during sleep does not act holistically on object memory, 2024A unifying account of replay as context-driven memory reactivation, 2024Self-recovery of memory via generative replay, 2023Host: Penny LewisEditor: Sophie SmithCheck out our NaPS website to find out more about our team, our research and events. This recording is the property of the Sleep Science Podcast and not for resale.
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Om Sleep Science Podcast

We spend a third of our lives sleeping, and this podcast is all about understanding sleep. We know a lot about what the brain does in sleep, but we are just starting to understand why it does some of these things, and even more excitingly, how we can take full advantage of sleep and also manipulate it for our own ends. In each episode, neuroscientist Penny Lewis interviews a different sleep researcher, talking about a various aspect of sleep science. Topics will include sleep physiology and medicine, circadian rhythm, how sleep impacts on our memories and creativity, Sleep Engineering for enhanced health and cognition, and the most recent technologies to promote sleep.©SleepSciencePodcast 2021. These materials may be downloaded for personal use only. They may not be shared, distributed or reproduced in any form or for any reason without express permission
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