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Unsupervised detection of cell-assembly sequences with edit similarity score

View ORCID ProfileKeita Watanabe, Tatsuya Haga, David R Euston, Masami Tatsuno, Tomoki Fukai
doi: https://doi.org/10.1101/202655
Keita Watanabe
1Department of Complexity Science and Engineering, The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8561, Japan
2RIKEN Brain Science Institute, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
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  • ORCID record for Keita Watanabe
Tatsuya Haga
2RIKEN Brain Science Institute, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
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David R Euston
3Univ Lethbridge, 4401 University Drive, Lethbridge, Alberta T1K 3M4, Canada
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Masami Tatsuno
3Univ Lethbridge, 4401 University Drive, Lethbridge, Alberta T1K 3M4, Canada
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Tomoki Fukai
1Department of Complexity Science and Engineering, The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8561, Japan
2RIKEN Brain Science Institute, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
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  • For correspondence: tfukai@riken.jp
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SUMMARY

Cell assembly is a hypothetical functional unit of information processing in the brain. While technologies for recording large-scale neural activity have been advanced, mathematical methods to analyze sequential activity patterns of cell-assembly are severely limited. Here, we propose a method to extract cell-assembly sequences repeated at multiple time scales and various precisions from irregular neural population activity. The key technology is to combine “edit similarity” in computer science with machine-learning clustering algorithms, where the former defines a “distance” between two strings as the minimal number of operations required to transform one string to the other. Our method requires no external references for pattern detection, and is tolerant of spike timing jitters and length irregularity in assembly sequences. These virtues enabled simultaneous automatic detections of hippocampal place-cell sequences during locomotion and their time-compressed replays during resting states. Furthermore, our method revealed previously undetected cell-assembly structure in the rat prefrontal cortex during goal-directed behavior. Thus, our method expands the horizon of cell-assembly analysis.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 30, 2017.
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Unsupervised detection of cell-assembly sequences with edit similarity score
Keita Watanabe, Tatsuya Haga, David R Euston, Masami Tatsuno, Tomoki Fukai
bioRxiv 202655; doi: https://doi.org/10.1101/202655
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Unsupervised detection of cell-assembly sequences with edit similarity score
Keita Watanabe, Tatsuya Haga, David R Euston, Masami Tatsuno, Tomoki Fukai
bioRxiv 202655; doi: https://doi.org/10.1101/202655

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