Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.30.066407v1?rss=1 Authors: Liu, Y., Dolan, R., Kurth-Nelson, Z., Behrens, T. E. J. Abstract: There are rich structures in off-task neural activity. For example, task related neural codes are thought to be reactivated in a systematic way during rest. This reactivation is hypothesised to reflect a fundamental computation that supports a variety of cognitive functions. Here, we introduce an analysis toolkit (TDLM) for analysing this activity. TDLM combines nonlinear classification and linear temporal modelling to testing for statistical regularities in sequences of neural representations. It is developed using non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. The method can be extended to rodent electrophysiological recordings. We outline how TDLM can successfully reveal human replay during rest, based upon non-invasive magnetoencephalography (MEG) measurements, with strong parallels to rodent hippocampal replay. TDLM can, therefore, advance our understanding of sequential computation and promote a richer convergence between animal and human neuroscience research. Copy rights belong to original authors. Visit the link for more info