Medical imaging methods are often affected by background noise. To solve this, some researchers have drawn inspiration from quantum mechanics, which describes how matter and energy behave at the atomic scale. Their studies draw an analogy between how particles vibrate and how pixel intensity spreads out in images and causes noise. Now authors apply the same mathematics to decipher the localization of pixel intensity in images. In this way, they can separate the noise-free “signal” of the anatomical structures in the image from the visual noise of stray pixels.

Here, researchers from Beijing Institute of Nanoenergy and Nanosystems (Chinese Academy of Sciences) and Yonsei University present the latest progress in neuromorphic computing by integrating various neural networks, including SVM, ANN, CNN, RNN, and RC. Starting from the structure of synapses and neurons, they explore how these networks can be combined with neuromorphic devices to replicate more complex brain-like computations. They also propose future development directions for neuromorphic devices, focusing on advancements in their structures, materials, and applications across diverse fields such as vision, touch, hearing, smell, pain and other senses.