State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
Cameron Higgins
Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
Hector Penagos
Center for Brains, Minds and Machines, Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
Mark W Woolrich
Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
H Freyja Ólafsdóttir
Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
Caswell Barry
Research Department of Cell and Developmental Biology, University College London, London, United Kingdom
Zeb Kurth-Nelson
Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; DeepMind, London, United Kingdom
Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
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.
