Michael A. Hahn , Janna D. Lendner , Matthias Anwander , Katarina S.J. Slama , Robert T. Knight , Jack J. Lin , Randolph F. Helfrich
{"title":"人类和啮齿动物睡眠时海马-皮层记忆网络的效率和稳健性之间的权衡。","authors":"Michael A. Hahn , Janna D. Lendner , Matthias Anwander , Katarina S.J. Slama , Robert T. Knight , Jack J. Lin , Randolph F. Helfrich","doi":"10.1016/j.pneurobio.2024.102672","DOIUrl":null,"url":null,"abstract":"<div><div>Sleep constitutes a brain state of disengagement from the external world that supports memory consolidation and restores cognitive resources. The precise mechanisms how sleep and its varied stages support information processing remain largely unknown. Synaptic scaling models imply that daytime learning accumulates neural information, which is then consolidated and downregulated during sleep. Currently, there is a lack of in-vivo data from humans and rodents that elucidate if, and how, sleep renormalizes information processing capacities. From an information-theoretical perspective, a consolidation process should entail a reduction in neural pattern variability over the course of a night. Here, in a cross-species intracranial study, we identify a tradeoff in the neural population code during sleep where information coding efficiency is higher in the neocortex than in hippocampal archicortex in humans than in rodents as well as during wakefulness compared to sleep. Critically, non-REM sleep selectively reduces information coding efficiency through pattern repetition in the neocortex in both species, indicating a transition to a more robust information coding regime. Conversely, the coding regime in the hippocampus remained consistent from wakefulness to non-REM sleep. These findings suggest that new information could be imprinted to the long-term mnemonic storage in the neocortex through pattern repetition during sleep. Lastly, our results show that task engagement increased coding efficiency, while medically-induced unconsciousness disrupted the population code. In sum, these findings suggest that neural pattern variability could constitute a fundamental principle underlying cognitive engagement and memory formation, while pattern repetition reflects robust coding, possibly underlying the consolidation process.</div></div>","PeriodicalId":20851,"journal":{"name":"Progress in Neurobiology","volume":"242 ","pages":"Article 102672"},"PeriodicalIF":6.7000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A tradeoff between efficiency and robustness in the hippocampal-neocortical memory network during human and rodent sleep\",\"authors\":\"Michael A. Hahn , Janna D. Lendner , Matthias Anwander , Katarina S.J. Slama , Robert T. Knight , Jack J. Lin , Randolph F. Helfrich\",\"doi\":\"10.1016/j.pneurobio.2024.102672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Sleep constitutes a brain state of disengagement from the external world that supports memory consolidation and restores cognitive resources. The precise mechanisms how sleep and its varied stages support information processing remain largely unknown. Synaptic scaling models imply that daytime learning accumulates neural information, which is then consolidated and downregulated during sleep. Currently, there is a lack of in-vivo data from humans and rodents that elucidate if, and how, sleep renormalizes information processing capacities. From an information-theoretical perspective, a consolidation process should entail a reduction in neural pattern variability over the course of a night. Here, in a cross-species intracranial study, we identify a tradeoff in the neural population code during sleep where information coding efficiency is higher in the neocortex than in hippocampal archicortex in humans than in rodents as well as during wakefulness compared to sleep. Critically, non-REM sleep selectively reduces information coding efficiency through pattern repetition in the neocortex in both species, indicating a transition to a more robust information coding regime. Conversely, the coding regime in the hippocampus remained consistent from wakefulness to non-REM sleep. These findings suggest that new information could be imprinted to the long-term mnemonic storage in the neocortex through pattern repetition during sleep. Lastly, our results show that task engagement increased coding efficiency, while medically-induced unconsciousness disrupted the population code. In sum, these findings suggest that neural pattern variability could constitute a fundamental principle underlying cognitive engagement and memory formation, while pattern repetition reflects robust coding, possibly underlying the consolidation process.</div></div>\",\"PeriodicalId\":20851,\"journal\":{\"name\":\"Progress in Neurobiology\",\"volume\":\"242 \",\"pages\":\"Article 102672\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Progress in Neurobiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0301008224001084\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Neurobiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0301008224001084","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
A tradeoff between efficiency and robustness in the hippocampal-neocortical memory network during human and rodent sleep
Sleep constitutes a brain state of disengagement from the external world that supports memory consolidation and restores cognitive resources. The precise mechanisms how sleep and its varied stages support information processing remain largely unknown. Synaptic scaling models imply that daytime learning accumulates neural information, which is then consolidated and downregulated during sleep. Currently, there is a lack of in-vivo data from humans and rodents that elucidate if, and how, sleep renormalizes information processing capacities. From an information-theoretical perspective, a consolidation process should entail a reduction in neural pattern variability over the course of a night. Here, in a cross-species intracranial study, we identify a tradeoff in the neural population code during sleep where information coding efficiency is higher in the neocortex than in hippocampal archicortex in humans than in rodents as well as during wakefulness compared to sleep. Critically, non-REM sleep selectively reduces information coding efficiency through pattern repetition in the neocortex in both species, indicating a transition to a more robust information coding regime. Conversely, the coding regime in the hippocampus remained consistent from wakefulness to non-REM sleep. These findings suggest that new information could be imprinted to the long-term mnemonic storage in the neocortex through pattern repetition during sleep. Lastly, our results show that task engagement increased coding efficiency, while medically-induced unconsciousness disrupted the population code. In sum, these findings suggest that neural pattern variability could constitute a fundamental principle underlying cognitive engagement and memory formation, while pattern repetition reflects robust coding, possibly underlying the consolidation process.
期刊介绍:
Progress in Neurobiology is an international journal that publishes groundbreaking original research, comprehensive review articles and opinion pieces written by leading researchers. The journal welcomes contributions from the broad field of neuroscience that apply neurophysiological, biochemical, pharmacological, molecular biological, anatomical, computational and behavioral analyses to problems of molecular, cellular, developmental, systems, and clinical neuroscience.