A method to find temporal structure of neuronal coactivity patterns with across-trial correlations.

Background: The across-trial correlation of neurons' coactivity patterns emerges to be important for information coding, but methods for finding their temporal structures remain largely unexplored.
New Method: In the present study, we propose a method to find time clusters in which coactivity patterns of neurons are correlated across trials. We transform the multidimensional neural activity at each timing into a coactivity pattern of binary states, and predict the coactivity patterns at different timings. We devise a method suitable for these coactivity pattern predictions, call general event prediction. Cross-temporal prediction accuracy is then used to estimate across-trial correlations between coactivity patterns at two timings. We extract time clusters from the cross-temporal prediction accuracy by a modified k-means algorithm.
Results: The feasibility of the proposed method is verified through simulations based on ground truth. We apply the proposed method to a calcium imaging dataset recorded from the motor cortex of mice, and demonstrate time clusters of motor cortical coactivity patterns during a motor task.
Comparison With Existing Methods: While the existing cosine similarity method, which does not account for across-trial correlation, shows temporal structures only for contralateral neural responses, the proposed method reveals those for both contralateral and ipsilateral neural responses, demonstrating the effect of across-trial correlations.
Conclusions: This study introduces a novel method for measuring the temporal structure of neuronal ensemble activity.
Competing Interests: Declaration of Competing Interest None of the authors have potential conflicts of interest to be disclosed.
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