Structured-light modes have emerged as a promising information carrier for both optical communications and optical computing because they provide high-dimensional spatial degrees of freedom (DOFs) for multiplexing and modal transformation. Although numerous demonstrations have reported increasing transmission capacities and computational functionalities, their practical significance is often evaluated by nominal modal dimensionality rather than system-level usability. In this commentary, we argue that structured-light communications and computing share a common framework of modal transformation and should therefore be assessed using unified system-level criteria. We compare representative structured-light mode groups, discuss their roles in communications and computing, and highlight the practical challenges associated with modal fidelity, modal crosstalk, and scalability. We further propose that the key figure of merit for future structured-light systems should be the usable modal dimensionality achievable under realistic operating conditions, providing a practical perspective for the development of scalable photonic information processing.

In recent years, Alzheimer’s dementia (AD) is the most common neurological condition caused by electrical activity changes in the human brain. The diagnosis of AD can be provided by using medical devices such as electroencephalography (EEG). In this study, EEG signals of AD patients and healthy control subjects were analyzed. Advanced signal decomposition methods, which are empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD), were used to further investigate EEG signals. The first three intrinsic mode functions (IMFs) were obtained using the EMD and EEMD methods. Spectral and time-domain features were extracted from IMFs and raw EEG signals. Then, topographical heat maps were generated from these features. Topographic Feature Map (Topo-map) were classified using a two-dimensional convolutional neural network (2D-CNN). Different CNN architectures were compared in terms of performance, including EfficientNet-b0, Resnet-50, and Resnet-18. The experimental results demonstrate that the proposed approach effectively captures the spatial and spectral characteristics of EEG signals associated with Alzheimer’s disease. 95.98% classification accuracy was achieved with the EfficienNet-b0 architecture.

Neuromorphic computing is one of the most promising technologies to solve the von Neumann bottleneck, which has the advantages of fast processing speed and low energy consumption in performing complex tasks. The development of neuromorphic computing is currently driven by several kinds of novel devices. Magnetic tunnel junctions (MTJs) are rich in nonlinear properties and can be regulated by multiple physical fields such as magnetic field, current and temperature. Meanwhile, MTJ has the advantages of good stability and low power consumption, which makes it an ideal device for neuromorphic computing. This paper starts by examining individual MTJ devices and then extends the discussion to full neural networks. First of all, we sorted out the various properties of MTJ, from the structure to physical mechanism and response characteristics. Secondly, the biological neuron model, synaptic properties and related studies on simulating neurons and synapses based on MTJs are introduced. Then, we review the neural network system-level architectures that have been explored with MTJ devices. Finally, the challenges and the future development trend are summarized for advancing MTJ-enabled neuromorphic computing.