Temporally delayed linear modelling (TDLM) measures replay in both animals and humans
- University College London, United Kingdom
- University of Oxford, United Kingdom
- Massachusetts Institute of Technology, United States
- Donders Institute for Brain Cognition and Behaviour, Radboud University, Netherlands
- DeepMind, United Kingdom
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.
Data availability
No new data is used or generated in the current paper.Data relevant for current paper is available at https://github.com/YunzheLiu/TDLM. This dataset is from "Ólafsdóttir, H. F., Carpenter, F., & Barry, C. (2016). Coordinated grid and place cell replay during rest. Nature neuroscience, 19(6), 792-794."
Article and author information
Author details
Funding
Wellcome (098362/Z/12/Z)
- Raymond J Dolan
Wellcome (104765/Z/14/Z)
- Timothy E Behrens
Wellcome (219525/Z/19/Z)
- Timothy E Behrens
James S. McDonnell Foundation (JSMF220020372)
- Timothy E Behrens
Wellcome (212281/Z/18/Z)
- Caswell Barry
max planck
- Yunzhe Liu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The human dataset used in this study was reported in Liu et al 2019. All participants were recruited from the UCL Institute of Cognitive Neuroscience subject pool, had a normal or corrected-to-normal vision, no history of psychiatric or neurological disorders, and had provided written informed consent prior to the start of the experiment, which was approved by the Research Ethics Committee at University College London (UK), under ethics number 9929/002.
Copyright
© 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.
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Temporally delayed linear modelling (TDLM) measures replay in both animals and humans
eLife 10:e66917.
https://doi.org/10.7554/eLife.66917
Further reading
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Real-world actions often comprise a series of movements that cannot be entirely planned before initiation. When these actions are executed rapidly, the planning of multiple future movements needs to occur simultaneously with the ongoing action. How the brain solves this task remains unknown. Here, we address this question with a new sequential arm reaching paradigm that manipulates how many future reaches are available for planning while controlling execution of the ongoing reach. We show that participants plan at least two future reaches simultaneously with an ongoing reach. Further, the planning processes of the two future reaches are not independent of one another. Evidence that the planning processes interact is twofold. First, correcting for a visual perturbation of the ongoing reach target is slower when more future reaches are planned. Second, the curvature of the current reach is modified based on the next reach only when their planning processes temporally overlap. These interactions between future planning processes may enable smooth production of sequential actions by linking individual segments of a long sequence at the level of motor planning.
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When carrying out a sequence of movements, humans can plan several steps in advance to make the movement smooth.
