Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …

Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-
solving in many applications. Such approaches found fertile ground in optical …

Sentiment analysis (SA) detects people's opinions from text engaging natural language
processing (NLP) techniques. Recent research has shown that deep learning models, ie …

This paper studies the synchronization of a network with linear diffusive coupling, which
blinks between the variables periodically. The synchronization of the blinking network in the …

Artificial neural networks (ANNs) have gained considerable momentum in the past decade.
Although at first the main task of the ANN paradigm was to tune the connection weights in …

Highlights Carbon-based gradient resistance element structure is proposed for the
construction of multifunctional touch sensor, which will promote wide detection and …

Neuromorphic computing using photonic hardware is a promising route towards ultrafast
processing while maintaining low power consumption. Here we present and numerically …

Modern machine-learning applications require huge artificial networks demanding
computational power and memory. Light-based platforms promise ultrafast and energy …

Standfirst Among the existing machine learning frameworks, reservoir computing
demonstrates fast and low-cost training, and its suitability for implementation in various …

摘要光学神经网络是区别于冯· 诺依曼计算架构的一种高性能新型计算范式, 具有低延时,
低功耗, 大带宽以及并行信号处理等优势. 片上集成是光学神经网络微型化发展的一种典型方式 …