Liu, Y;
Dolan, RJ;
Higgins, C;
Penagos, H;
Woolrich, MW;
Ólafsdóttir, HF;
Barry, C;
... Behrens, TE; + view all
(2021)
Temporally delayed linear modelling (TDLM) measures replay in both animals and humans.
eLife
, 10
, Article e66917. 10.7554/eLife.66917.
(In press).
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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.
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