Region-based conversion of neural activity across sessions

A common way to advance our understanding of brain processing is to decode behavior from recorded neural signals. In order to study the neural correlates of learning a task, we would like to decode behavior across the entire timespan of learning, which can take multiple recording sessions across many days. However, decoding across sessions is hindered due to a high amount of session-to-session variability in neural recordings. Here, we propose utilizing multidimensional neural signals from Localized semi-non negative matrix factorization processing (LocaNMF) with high behavioral correlations across sessions, as well as a novel data augmentation method and region-based converter, to optimally align neural recordings. We apply our method to widefield calcium activity across many sessions while a mouse learns a decision-making task. We first decompose each session's neural activity into region-based spatial and temporal components that can reconstruct the data with high variance. Next, we perform data augmentation of the neural data to smooth the variability across trials. Finally, we design a region-based neural converter across sessions that transforms one session's neural signals into another while preserving its dimensionality. We test our approach by decoding the mouse's behavior in the decision-making task, and find that our method outperforms approaches that use purely anatomical information while analyzing neural activity across sessions. By preserving the high dimensionality in the neural data while converting neural activity across sessions, our method can be used towards further analyses of neural data across sessions and the neural correlates of learning.