Journal article
Temporally delayed linear modelling (TDLM) measures replay in both animals and humans
- Abstract:
- There are rich structures in off-task neural activity which are hypothesised to reflect fundamental computations across a broad spectrum of cognitive functions. Here, we develop an analysis toolkit – Temporal Delayed Linear Modelling (TDLM) for analysing such activity. TDLM is a domain-general method for finding neural sequences that respect a pre-specified transition graph. It combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of task-related reactivations. TDLM is developed on the non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. Notably, as a linear framework, TDLM can be easily extended, without loss of generality, to capture rodent replay in electrophysiology, including in continuous spaces, as well as addressing second-order inference questions, e.g., its temporal and spatial varying pattern. We hope TDLM will advance a deeper understanding of neural computation and promote a richer convergence between animal and human neuroscience.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.8MB, Terms of use)
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- Publisher copy:
- 10.7554/elife.66917
Authors
+ Wellcome Trust
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- Grant:
- 215573/Z/19/Z
- 104765/Z/14/Z
- 203139/Z/16/Z
- 219525/Z/19/Z
- 106183/Z/14/Z
- Publisher:
- eLife Sciences Publications
- Journal:
- eLife More from this journal
- Volume:
- 10
- Article number:
- e66917
- Publication date:
- 2021-06-07
- Acceptance date:
- 2021-06-06
- DOI:
- EISSN:
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2050-084X
- Language:
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English
- Keywords:
- Pubs id:
-
1180793
- Local pid:
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pubs:1180793
- Deposit date:
-
2021-06-07
- ARK identifier:
Terms of use
- Copyright holder:
- Liu et al.
- Copyright date:
- 2021
- Rights statement:
- © 2021, Liu et al. This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
- Licence:
- CC Attribution (CC BY)
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