Electrochemical modulation enhances the selectivity of peripheral neurostimulation in vivo.

Electrical nerve stimulation serves an expanding list of clinical applications, but it faces persistent challenges in selectively activating bundled nerve fibers. In this study, we investigated electrochemical modulation with an ion-selective membrane (ISM) and whether it, used together with electrical stimulation, may provide an approach for selective control of peripheral nerves. Guided by theoretical transport modeling and direct concentration measurements, we developed an implantable, multimodal ISM cuff capable of simultaneous electrical stimulation and focused Ca2+ depletion.

Rapid and Low Cost Manufacturing of Cuff Electrodes.

For many peripheral neuro-modulation applications, the cuff electrode has become a preferred technology for delivering electrical current into targeted volumes of tissue. While basic cuffs with low spatial selectivity, having longitudinally arranged contacts, can be produced from relatively straightforward processes, the fabrication of more complex electrode configurations typically requires iterative design and clean-room fabrication with skilled technicians. Although facile methods for fabricating cuff electrodes exist, their inconsistent products have limited their adoption for rapid manufacturing.

From motor planning to execution: a sensorimotor loop perspective.

How is an evanescent wish to move translated into a concrete action? This simple question and puzzling miracle remains a focal point of motor systems neuroscience. Where does the difficulty lie? A great deal has been known about biomechanics for quite some time. More recently, there have been significant advances in our understanding of how the spinal system is organized into modules corresponding to spinal synergies, which are fixed patterns of multimuscle recruitment.

Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence.

A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains of human intelligence as executable, neurally mechanistic models. Years of research have led to models that capture experimental results in individual behavioral tasks and individual brain regions. We here advocate for taking the next step: integrating experimental results from many laboratories into suites of benchmarks that, when considered together, push mechanistic models toward explaining entire domains of intelligence, such as vision, language, and motor control.