A bidirectional in vitro brain–computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.

Terahertz waves can interact with the nervous system of organisms under certain conditions. Compared to common optical modulation methods, terahertz waves have the advantages of low photon energy and low risk; therefore, the use of terahertz waves to regulate the nervous system is a promising new method of neuromodulation. However, most of the research has focused on the use of terahertz technology for biodetection, while relatively little research has been carried out on the biological effects of terahertz radiation on the nervous system, and there are almost no review papers on this topic. In the present article, we begin by reviewing principles and objects of research regarding the biological effects of terahertz radiation and summarizing the current state of related research from a variety of aspects, including the bioeffects of terahertz radiation on neurons in vivo and in vitro, novel regulation and detection methods with terahertz radiation devices and neural microelectrode arrays, and theoretical simulations of neural information encoding and decoding. In addition, we discuss the main problems and their possible causes and give some recommendations on possible future breakthroughs. This paper will provide insight and assistance to researchers in the fields of neuroscience, terahertz technology and biomedicine.

In situ physiological signals of in vitro neural disease models are essential for studying pathogenesis and drug screening. Currently, an increasing number of in vitro neural disease models are established using human‐induced pluripotent stem cell (hiPSC) derived neurons (hiPSC‐DNs) to overcome interspecific gene expression differences. Microelectrode arrays (MEAs) can be readily interfaced with two‐dimensional (2D), and more recently, three‐dimensional (3D) neural stem cell‐derived in vitro models of the human brain to monitor their physiological activity in real time. Therefore, MEAs are emerging and useful tools to model neurological disorders and disease in vitro using human iPSCs. This is enabling a real‐time window into neuronal signaling at the network scale from patient derived. This paper provides a comprehensive review of MEA's role in analyzing neural disease models established by hiPSC‐DNs. It covers the significance of MEA fabrication, surface structure and modification schemes for hiPSC‐DNs culturing and signal detection. Additionally, this review discusses advances in the development and use of MEA technology to study in vitro neural disease models, including epilepsy, autism spectrum developmental disorder (ASD), and others established using hiPSC‐DNs. The paper also highlights the application of MEAs combined with hiPSC‐DNs in detecting in vitro neurotoxic substances. Finally, the future development and outlook of multifunctional and integrated devices for in vitro medical diagnostics and treatment are discussed.

Many methods based on radiative-transfer models and empirical approaches with prior knowledge have been developed for the retrieval of hyperspectral surface reflectance. In this paper, we propose a novel approach for atmospheric correction of hyperspectral images based on machine learning. A support vector machine (SVM) is used for learning to predict the surface reflectance from the preprocessed at-sensor radiance image. The preprocessed spectra of each pixel are considered as the spectral feature and hypercolumn based on convolutional neural networks (CNNs) is utilized for spatial feature extraction. After training, the surface reflectance of images from totally different spatial positions and atmospheric conditions can be quickly predicted with the at-sensor radiance image and the models trained before, and no additional metadata is required. On an AVIRIS hyperspectral data set, the performances of our method, based on spectral and spatial features, respectively, are compared. Furthermore, our method outperforms QUAC, and the retrieved spectra have good agreement with FLAASH and AVIRIS reflectance products.

Sparsity-driven synthetic aperture radar (SAR) imaging technique for frequency modulation continuous-wave (FMCW) has already shown the superiority in terms of performance improvement in imaging and recovery from down-sampled data. However, restricted by the computational cost, conventional FMCW SAR sparse imaging method based on observation matrix is not able to achieve the large-scale scene reconstruction, not to mention real-time processing. To solve this problem, the FMCW SAR sparse imaging theory based on approximated observation is proposed by using an echo simulation operator to replace typical observation matrix, and recovering the scene via two-dimensional (2-D) regularization operation. This new technology can achieve high-resolution sparse imaging of the scene with a computational cost close to that of traditional matched filtering (MF) algorithms, which makes several applications, such as early-warning and battlefield monitoring, possible by using FMCW SAR sparse imaging system. In this paper, we present the recent research progress on approximated observation based FMCW SAR sparse imaging to deal with a few key issues for practical radar systems. In particular, we describe 1) Lq-norm regularization based imaging technique that makes sparse reconstruction of large-scale scene possible; 2) L2,q-norm regularization based imaging technique that minimizes the azimuth ambiguities in high-resolution sparse imaging; 3) a sparse imaging technique that supports real-time applications of FMCW SAR imaging.

The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are more complex and more blurry than ordinary images, most of the existing methods are not good enough for remote sensing image restoration. To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality images. We build local and global memory connections to combine image detail with global information. To further reduce parameters and ease time consumption, we propose Downsampling Units, shrinking the spatial size of feature maps. We verify its capability on two representative applications, Gaussian image denoising and single image super-resolution (SR). DMCN is tested on three remote sensing datasets with various spatial resolution. Experimental results indicate that our method yields promising improvements and better visual performance over the current state-of-the-art. The PSNR and SSIM improvements over the second best method are up to 0.3 dB.