Brain-computer interface (BCI) systems allow users to interact with their environment by bypassing muscular control to tap directly into the users’ thoughts. In the present study, we investigate the role of prior experience with yoga and meditation, examples of formalized mind-body awareness training (MBAT), in learning to use a one-dimensional sensorimotor rhythm based BCI. Thirty-six human subjects volunteered to participate in two different cohorts based on past experience with MBAT — experienced MBAT practitioners and controls. All subjects participated in three BCI experiments to achieve competency in controlling the BCI system. The MBAT cohort achieved BCI competency significantly faster than the control cohort. In addition, the MBAT cohort demonstrated enhanced ability to control the system on various measures of BCI performance and improved significantly more over time when compared to control. Our work provides insight into valuable strategies for reducing barriers to BCI fluency that limit the more widespread use of these systems.

Learning to control a brain-machine interface (BMI) is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-rated changes in cortical population dynamics within these groups compare. To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this coordination was primarily driven by changes in the direct subpopulation while the indirect subpopulation remained relatively stable. These findings indicate that motor cortex refines cortical dynamics throughout the entire network during learning, with a more pronounced effect in ensembles causally linked to behavior.

Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.

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Human languages' semantics and structure constantly change over time through mediums such as culturally significant events. By viewing the semantic changes of words during notable events, contexts of existing and novel words can be predicted for similar, current events. By studying the initial outbreak of a disease and the associated semantic shifts of select words, we hope to be able to spot social media trends to prevent future outbreaks faster than traditional methods. To explore this idea, we generate a temporal word embedding model that allows us to study word semantics evolving over time. Using these temporal word embeddings, we use machine learning models to predict words associated with the disease outbreak.

23One hallmark of natural motor control is the brain's ability to adapt to perturbations ranging 24 from temporary visual-motor rotations to paresis caused by stroke. These adaptations require 25 modifications of learned neural patterns that can span the time-course of minutes to months. 26Previous work has shown that populations of neurons fire on coordinated low-dimensional 27 subspaces that are resistant to changes, and perturbations requiring neural activity to move 28 outside of these subspaces are difficult to learn. Subsequently, perturbations that remain within 29 the neural subspace are easier to adapt to. However, it is unclear how motor cortex might 30 respond to perturbations whilst still learning. To answer this question, five nonhuman primates 31 were used in three brain-machine interface (BMI) experiments, which allowed us to track how 32 2 specific populations of neurons changed firing patterns as task performance improved. In each 33 experiment, neural intentions were estimated with biomimetic decoders that were periodically 34 refit, creating perturbations throughout learning. We found that decoder perturbations caused 35 neurons to increase exploratory patterns on within-day timescales without hindering previously 36 consolidated patterns regardless of task performance. The flexible modulation of these 37 exploratory patterns in contrast to relatively stable consolidated activity suggests a concomitant 38 exploration-exploitation strategy that adapts existing neural patterns during learning. 39 40 42can adapt depends on the similarity to previously learned behaviors and the time allowed for 43