Biology subjects

Mamidanna, P.

Publications and source records attributed to Mamidanna, P..

4 recordsLinked to original sources

bioRxiv2023

Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs

ObjectiveClosed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process. ApproachBuilding on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill - as opposed to merely tracking changes in task success across training - facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen able-bodied and one amputee participated in a 3-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined. Main resultsWe found that the participants accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill. SignificanceWe comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control.

bioengineering↗

bioRxiv2022

Estimating Speed-Accuracy Trade-offs to Evaluate and Understand Closed-Loop Prosthesis Interfaces

ObjectiveClosed-loop prosthesis interfaces, combining electromyography (EMG)-based control with non-invasive supplementary feedback, represent a promising direction to develop the next generation of user prosthesis interfaces. However, we still lack an understanding of how users make use of these interfaces, and how to evaluate competing interfaces. In this study we use the framework of speed accuracy trade-off functions (SAF) to understand, evaluate and compare the performance afforded by two closed-loop user-prosthesis interfaces. ApproachTen able-bodied participants and one amputee performed a force matching task in a functional box-and-blocks setup at 3 different speeds. All participants were subject to both interfaces in a crossover fashion with a one-week washout period. Importantly, both interfaces used (identical) direct proportional control but differed in the feedback provided to the participant - EMG feedback vs force feedback. We thereby estimated the SAFs afforded by the two interfaces, and additionally sought to understand how participants planned and executed the task in the various conditions. Main resultsWe found that execution speed significantly influenced the performance, and that EMG feedback afforded better performance overall. Notably, we found that there was a difference in SAF between the two interfaces, with EMG feedback enabling participants to attain higher accuracies faster than Force feedback. Further, both interfaces enabled participants to develop flexible control policies, while EMG feedback also afforded participants to generate smoother more repeatable EMG commands. SignificanceOverall, the results indicate that closed-loop prosthesis interfaces afford subjects to exhibit a wide range of performance, which is affected both by the interface and the execution speed. Thereby, we argue that it is important to consider the speed accuracy trade-offs to rigorously evaluate and compare (closed-loop) user-prosthesis interfaces.

bioengineering↗

bioRxiv2020

Task-driven hierarchical deep neural networkmodels of the proprioceptive pathway

Biological motor control is versatile and efficient. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms. How proprioception solves this problem in the brain is unknown. The canonical role of proprioception is representing the body state, yet we hypothesize that the proprioceptive system can decode high-level, multi-feature actions. To test this theory, we pursue a task-driven modeling approach.We generated a large synthetic dataset of human arm trajectories tracing the alphabet in 3D space and use a musculoskeletal model plus modeled muscle spindle inputs to extract muscle activity. We then contrast two tasks, one character trajectory-decoding and another action recognition task that allows training of hierarchical models to decode position, or classify the character identity from the spindle firing patterns. Artificial neural networks could robustly solve these tasks, and the networks units show tuning properties akin to neurons in the primate somatosensory cortex and the brainstem. Remarkably, only the action-recognition trained, and not the trajectory decoding trained, models possess directional selective units (which are also uniformly distributed), as in the primate brain. Taken together, our model is the first to link tuning properties in the proprioceptive system at multiple levels to the behavioral level. We find that action-recognition, rather than the canonical trajectory-decoding hypothesis, better explains what is known about the proprioceptive system.

neuroscience↗

bioRxiv2019

Action representation in the mouse parieto-frontal network

The posterior parietal cortex (PPC), along with anatomically linked frontal areas, form a cortical network which mediates several functions that support goal-directed behavior, including sensorimotor transformations and decision making. In primates, this network also links performed and observed actions via mirror neurons, which fire both when an individual performs an action and when they observe the same action performed by a conspecific. Mirror neurons are thought to be important for social learning and imitation, but it is not known whether mirror-like neurons occur in similar networks in other species that can learn socially, such as rodents. We therefore imaged Ca2+ responses in large neural ensembles in PPC and secondary motor cortex (M2) while mice performed and observed several actions in pellet reaching and wheel running tasks. In all animals, we found spatially overlapping neural ensembles in PPC and M2 that robustly encoded a variety of naturalistic behaviors, and that subsets of cells could stably encode multiple actions. However, neural responses to the same set of observed actions were absent in both brain areas, and across animals. Statistical modeling analyses also showed that performed actions, especially those that were task-specific, outperformed observed actions in predicting neural responses. Overall, these findings show that performed and observed actions do not drive the same cells in the parieto-frontal network in mice, and suggest that sensorimotor mirroring in the mammalian cortex may have evolved more recently, and only in certain species.

neuroscience↗