An experimental and computational framework for modeling multi-muscle responses to transcranial magnetic stimulation of the human motor cortex.
Abstract
Current knowledge of coordinated motor control of multiple muscles is derived primarily from invasive stimulation-recording techniques in animal models. Similar studies are not generally feasible in humans, so a modeling framework is needed to facilitate knowledge transfer from animal studies. We describe such a framework that uses a deep neural network model to map finite element simulation of transcranial magnetic stimulation induced electric fields (E-fields) in motor cortex to recordings of multi-muscle activation. Critically, we show that model generalization is improved when we incorporate empirically derived physiological models for E-field to neuron firing rate and low-dimensional control via muscle synergies.
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Scholars@Duke
Marc A. Sommer
We study circuits for cognition. Using a combination of neurophysiology and biomedical engineering, we focus on the interaction between brain areas during visual perception, decision-making, and motor planning. Specific projects include the role of frontal cortex in metacognition, the role of cerebellar-frontal circuits in action timing, the neural basis of "good enough" decision-making (satisficing), and the neural mechanisms of transcranial magnetic stimulation (TMS).
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