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Methods for Assessment of Memory Reactivation.

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Title: Methods for Assessment of Memory Reactivation.
Authors: Liu, Shizhao1 lsz14@mails.tsinghua.edu.cn, Grosmark, Andres D.2 ag3633@cumc.columbia.edu, Chen, Zhe3 zhe.chen3@nyumc.org
Source: Neural Computation. Aug2018, Vol. 30 Issue 8, p2175-2209. 35p. 2 Charts, 11 Graphs.
Subjects: Recovered memory, Cerebral cortex, Neocortex, Hippocampus (Brain), Limbic system
Abstract: It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Shizhao%22">Liu, Shizhao</searchLink><relatesTo>1</relatesTo><i> lsz14@mails.tsinghua.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Grosmark%2C+Andres+D%2E%22">Grosmark, Andres D.</searchLink><relatesTo>2</relatesTo><i> ag3633@cumc.columbia.edu</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhe%22">Chen, Zhe</searchLink><relatesTo>3</relatesTo><i> zhe.chen3@nyumc.org</i>
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Aug2018, Vol. 30 Issue 8, p2175-2209. 35p. 2 Charts, 11 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Recovered+memory%22">Recovered memory</searchLink><br /><searchLink fieldCode="DE" term="%22Cerebral+cortex%22">Cerebral cortex</searchLink><br /><searchLink fieldCode="DE" term="%22Neocortex%22">Neocortex</searchLink><br /><searchLink fieldCode="DE" term="%22Hippocampus+%28Brain%29%22">Hippocampus (Brain)</searchLink><br /><searchLink fieldCode="DE" term="%22Limbic+system%22">Limbic system</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1162/neco_a_01090
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      – Code: eng
        Text: English
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        PageCount: 35
        StartPage: 2175
    Subjects:
      – SubjectFull: Recovered memory
        Type: general
      – SubjectFull: Cerebral cortex
        Type: general
      – SubjectFull: Neocortex
        Type: general
      – SubjectFull: Hippocampus (Brain)
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      – SubjectFull: Limbic system
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      – TitleFull: Methods for Assessment of Memory Reactivation.
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            NameFull: Liu, Shizhao
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            NameFull: Chen, Zhe
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            – D: 01
              M: 08
              Text: Aug2018
              Type: published
              Y: 2018
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              Value: 30
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