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Zinc dry electrodes were fabricated and investigated for wearable electrophysiology recording. Results from electrochemical impedance spectroscopy and electromyography functionality testing show that zinc electrodes are suitable for use in electrophysiology. Two electrode configurations were tested: a standard disc and a custom tripolar concentric ring configuration. However, no functional benefit was observed with the tripolar concentric ring electrodes as compared to the disc electrodes.Zinc, Electrodes, Concentric Ring Electrodes, EMG, Biosensing 

Humans solving algorithmic (or) reasoning problems typically exhibit solution times that grow as a function of problem difficulty. Adaptive recurrent neural networks have been shown to exhibit this property for various language-processing tasks. However, little work has been performed to assess whether such adaptive computation can also enable vision models to extrapolate solutions beyond their training distribution’s difficulty level, with prior work focusing on very simple tasks. In this study, we investigate a critical functional role of such adaptive processing using recurrent neural networks: to dynamically scale computational resources conditional on input requirements that allow for zero-shot generalization to novel difficulty levels not seen during training using two challenging visual reasoning tasks: PathFinder and Mazes. We combine convolutional recurrent neural networks (ConvRNNs) with a learnable halting mechanism based on (Graves, 2016). We explore various implementations of such adaptive ConvRNNs (AdRNNs) rang- ing from tying weights across layers to more sophisticated biologically inspired recurrent networks that possess lateral connections and gating. We show that 1) AdRNNs learn to dynamically halt processing early (or late) to solve easier (or harder) problems, 2) these RNNs zero-shot generalize to more difficult problem settings not shown during training by dynamically increasing the number of recur- rent iterations at test time. Our study provides modeling evidence supporting the hypothesis that recurrent processing enables the functional advantage of adaptively allocating compute resources conditional on input requirements and hence allowing generalization to harder difficulty levels of a visual reasoning problem without training.