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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. 

Large volumes of used electronics are often collected in remanufacturing plants, which requires disassembly before harvesting parts for reuse. Disassembly is mainly conducted manually with low productivity. Recently, human-robot collaboration is considered as a solution. For robots to assist effectively, they should observe work environments and recognize human actions accurately. Rich activity video recording and supervised learning can be used to extract insights; however, supervised learning does not allow robots to self-accomplish the learning process. This study proposes an unsupervised learning framework for achieving video-based human activity recognition. The framework consists of two main elements: a variational autoencoder-based architecture for unlabeled data representation learning, and a hidden Markov model for activity state division. The complete explicit activity classification is validated against ground truth labels; here, we use a case study of disassembling a hard disk drive. The framework shows an average recognition accuracy of 91.52% , higher than competing methods. 

Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed. 

Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes such as attention, decision-making and action selection. However, techniques that allow cellular resolution measurements are expensive and require a high degree of technical expertise, which limits their broad use. Wide-field imaging of genetically encoded indicators is a high-throughput, cost-effective and flexible approach to measure activity of specific cell populations with high temporal resolution and a cortex-wide field of view. Here we outline our protocol for assembling a wide-field macroscope setup, performing surgery to prepare the intact skull and imaging neural activity chronically in behaving, transgenic mice. Further, we highlight a processing pipeline that leverages novel, cloud-based methods to analyze large-scale imaging datasets. The protocol targets laboratories that are seeking to build macroscopes, optimize surgical procedures for long-term chronic imaging and/or analyze cortex-wide neuronal recordings. The entire protocol, including steps for assembly and calibration of the macroscope, surgical preparation, imaging and data analysis, requires a total of 8 h. It is designed to be accessible to laboratories with limited expertise in imaging methods or interest in high-throughput imaging during behavior.