Methods for Assessment of Memory Reactivation.
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| Title: | Methods for Assessment of Memory Reactivation. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 08997667 |
| DOI: | 10.1162/neco_a_01090 |
