ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network.

Recent developments in artificial neural networks and their learning algorithms have enabled new research directions in computer vision, language modeling, and neuroscience. Among various neural network algorithms, spiking neural networks (SNNs) are well-suited for understanding the behavior of biological neural circuits. In this work, we propose to guide the training of a sparse SNN in order to replace a sub-region of a cultured hippocampal network with limited hardware resources.

Inkjet-Printed Polyelectrolyte Seed Layer-Based, Customizable, Transparent, Ultrathin Gold Electrodes and Facile Implementation of Photothermal Effect.

Recently, interest in transparent electrodes has been increasing in biomedical engineering applications for such as electro-optical hybrid neuro-technologies. However, conventional photolithography-based electrode fabrication methods have limited design customization and large-area applicability. For biomedical engineering applications, it is crucial that we can easily customize the electrode design for different patients over a large body area.

Coverage and accuracy of ethnicity data on three Asian ethnic groups in New Zealand.

Objective: Detecting and eliminating ethnic disparities in access to and outcomes of healthcare relies on accurate ethnicity recording. Studies have shown that there are inaccuracies in ethnicity data in New Zealand and elsewhere. This study examined coverage and accuracy of ethnicity data for three Asian ethnic groups.