Edge Computing in Nature: Minimal pre-processing of multi-muscle ensembles of spindle signals improves discriminability of limb movements

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

An α-MN collateral to γ-MNs can mitigate velocity-dependent stretch reflexes during voluntary movement: A computational study

The primary motor cortex does not uniquely or directly produce α -MN drive to muscles during voluntary movement. Rather, α -MN drive emerges from the synthesis and competition among excitatory and inhibitory inputs from multiple descending tracts, spinal interneurons, sensory inputs, and proprioceptive afferents. One such fundamental input is velocity-dependent stretch reflexes in lengthening (antagonist) muscles, which are thought to be inhibited by the shortening (agonist) muscles. It remains an open question, however, the extent to which velocity-dependent stretch reflexes disrupt voluntary movement, and whether and how they are inhibited in limbs with numerous monoand multi-articular muscles where agonist and antagonist roles become unclear and can switch during a movement. We used a computational model of a Rhesus Macaque arm to simulate movements with feedforward α -MN commands only, and with added velocity-dependent stretch reflex feedback. We found that velocity-dependent stretch reflex caused movement-specific, typically large and variable disruptions to the arm endpoint trajectories. In contrast, these disruptions became small when the velocity-dependent stretch reflexes were simply scaled by the α -MN drive to each muscle (equivalent to an α -MN excitatory collateral to its homologous γ -MNs, but distinct from α - γ co-activation. We argue this circuitry is more neuroanatomically tenable, generalizable, and scalable than α - γ co-activation or movement-specific reciprocal inhibition. We propose that this mechanism at the homologous propriospinal level, by locally and automatically regulating the highly nonlinear neuro-musculo-skeletal mechanics of the limb, could be a critical low-level enabler of learning, adaptation, and performance via cerebellar and cortical mechanisms.