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Neural Population-Level Memory Traces in the Mouse Hippocampus

Overview of attention for article published in PLOS ONE, December 2009
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Title
Neural Population-Level Memory Traces in the Mouse Hippocampus
Published in
PLOS ONE, December 2009
DOI 10.1371/journal.pone.0008256
Pubmed ID
Authors

Guifen Chen, L. Phillip Wang, Joe Z. Tsien

Abstract

One of the fundamental goals in neurosciences is to elucidate the formation and retrieval of brain's associative memory traces in real-time. Here, we describe real-time neural ensemble transient dynamics in the mouse hippocampal CA1 region and demonstrate their relationships with behavioral performances during both learning and recall. We employed the classic trace fear conditioning paradigm involving a neutral tone followed by a mild foot-shock 20 seconds later. Our large-scale recording and decoding methods revealed that conditioned tone responses and tone-shock association patterns were not present in CA1 during the first pairing, but emerged quickly after multiple pairings. These encoding patterns showed increased immediate-replay, correlating tightly with increased immediate-freezing during learning. Moreover, during contextual recall, these patterns reappeared in tandem six-to-fourteen times per minute, again correlating tightly with behavioral recall. Upon traced tone recall, while various fear memories were retrieved, the shock traces exhibited a unique recall-peak around the 20-second trace interval, further signifying the memory of time for the expected shock. Therefore, our study has revealed various real-time associative memory traces during learning and recall in CA1, and demonstrates that real-time memory traces can be decoded on a moment-to-moment basis over any single trial.

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Geographical breakdown

Country Count As %
Canada 2 2%
China 2 2%
France 2 2%
United Kingdom 2 2%
Austria 1 <1%
Colombia 1 <1%
Germany 1 <1%
Netherlands 1 <1%
Korea, Republic of 1 <1%
Other 2 2%
Unknown 89 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 31%
Researcher 28 27%
Student > Bachelor 9 9%
Student > Master 9 9%
Professor > Associate Professor 7 7%
Other 13 13%
Unknown 6 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 41%
Neuroscience 20 19%
Engineering 6 6%
Computer Science 5 5%
Psychology 5 5%
Other 15 14%
Unknown 10 10%