Cargando…
Temporally delayed linear modelling (TDLM) measures replay in both animals and humans
There are rich structures in off-task neural activity which are hypothesized 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 met...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318595/ https://www.ncbi.nlm.nih.gov/pubmed/34096501 http://dx.doi.org/10.7554/eLife.66917 |
_version_ | 1783730275505668096 |
---|---|
author | Liu, Yunzhe Dolan, Raymond J Higgins, Cameron Penagos, Hector Woolrich, Mark W Ólafsdóttir, H Freyja Barry, Caswell Kurth-Nelson, Zeb Behrens, Timothy E |
author_facet | Liu, Yunzhe Dolan, Raymond J Higgins, Cameron Penagos, Hector Woolrich, Mark W Ólafsdóttir, H Freyja Barry, Caswell Kurth-Nelson, Zeb Behrens, Timothy E |
author_sort | Liu, Yunzhe |
collection | PubMed |
description | There are rich structures in off-task neural activity which are hypothesized 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, for example, 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. |
format | Online Article Text |
id | pubmed-8318595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-83185952021-07-30 Temporally delayed linear modelling (TDLM) measures replay in both animals and humans Liu, Yunzhe Dolan, Raymond J Higgins, Cameron Penagos, Hector Woolrich, Mark W Ólafsdóttir, H Freyja Barry, Caswell Kurth-Nelson, Zeb Behrens, Timothy E eLife Neuroscience There are rich structures in off-task neural activity which are hypothesized 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, for example, 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. eLife Sciences Publications, Ltd 2021-06-07 /pmc/articles/PMC8318595/ /pubmed/34096501 http://dx.doi.org/10.7554/eLife.66917 Text en © 2021, Liu et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Liu, Yunzhe Dolan, Raymond J Higgins, Cameron Penagos, Hector Woolrich, Mark W Ólafsdóttir, H Freyja Barry, Caswell Kurth-Nelson, Zeb Behrens, Timothy E Temporally delayed linear modelling (TDLM) measures replay in both animals and humans |
title | Temporally delayed linear modelling (TDLM) measures replay in both animals and humans |
title_full | Temporally delayed linear modelling (TDLM) measures replay in both animals and humans |
title_fullStr | Temporally delayed linear modelling (TDLM) measures replay in both animals and humans |
title_full_unstemmed | Temporally delayed linear modelling (TDLM) measures replay in both animals and humans |
title_short | Temporally delayed linear modelling (TDLM) measures replay in both animals and humans |
title_sort | temporally delayed linear modelling (tdlm) measures replay in both animals and humans |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318595/ https://www.ncbi.nlm.nih.gov/pubmed/34096501 http://dx.doi.org/10.7554/eLife.66917 |
work_keys_str_mv | AT liuyunzhe temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans AT dolanraymondj temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans AT higginscameron temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans AT penagoshector temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans AT woolrichmarkw temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans AT olafsdottirhfreyja temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans AT barrycaswell temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans AT kurthnelsonzeb temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans AT behrenstimothye temporallydelayedlinearmodellingtdlmmeasuresreplayinbothanimalsandhumans |