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While the neural commonalities as subjects perform similar task-related behaviors has been previously examined, it is very difficult to ascertain the neural commonalities for spontaneous, task-unrelated behaviors such as grooming. As our ability to record high-dimensional naturalistic behavioral and corresponding neural data increases, we can now try to understand the relationship between different subjects performing spontaneous behaviors that occur rarely in time. Here, we first apply novel machine learning techniques to behavioral video data from four head-fixed mice as they perform a self-initiated decision-making task while their neural activity is recorded using widefield calcium imaging. Across mice, we automatically identify spontaneous behaviors such as grooming and task-related behaviors such as lever pulls. Next, we explore the commonalities between the neural activity of different mice as they perform these tasks by transforming the neural activity into a common subspace, using Multidimensional Canonical Correlation Analysis (MCCA). Finally, we compare the commonalities across different trials in the same subject to those across subjects for different types of behaviors, and find that many recorded brain regions display high levels of correlation for spontaneous behaviors such as grooming. The combined behavioral and neural analysis methods in this paper provide an understanding of how similarly different animals perform innate behaviors. 

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.