We introduce a biomimetic framework for human sensorimotor control, which features a biomechanically simulated human musculoskeletal model actuated by numerous muscles, with eyes whose retinas have nonuniformly distributed photoreceptors. The virtual human's sensorimotor control system comprises 20 trained deep neural networks (DNNs), half constituting the neuromuscular motor subsystem, while the other half compose the visual sensory subsystem. Directly from the photoreceptor responses, 2 vision DNNs drive eye and head movements, while 8 vision DNNs extract visual information required to direct arm and leg actions. Ten DNNs achieve neuromuscular control---2 DNNs control the 216 neck muscles that actuate the cervicocephalic musculoskeletal complex to produce natural head movements, and 2 DNNs control each limb; i.e., the 29 muscles of each arm and 39 muscles of each leg. By synthesizing its own training data, our virtual human automatically learns efficient, online, active visuomotor control of its eyes, head, and limbs in order to perform nontrivial tasks involving the foveation and visual pursuit of target objects coupled with visually-guided limb-reaching actions to intercept the moving targets, as well as to carry out drawing and writing tasks.

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Continued advancement of protein array, bioelectrode, and biosensor technologies will necessitate development of methods that allow for increased protein immobilization capacity and more control over protein orientation. Toward these ends, we developed a method involving modification of chitosan with nitrilotriacetic acid (NTA) to achieve immobilization of a larger amount of His-tagged protein than is possible with current methods. The immobilization capacity of our method was evaluated using His-tagged GFP (Green Fluorescent Protein) as a model protein. The average immobilization density on modified glass was about 32 ng/mm 2. Our method is suitable for use on a variety of solid surfaces, including glassy carbon, silicon wafers, polycarbonate, and beaten gold.

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