Neuromorphic photonics/electronics is the future of ultralow energy intelligent computing and artificial intelligence (AI). In recent years, inspired by the human brain, artificial neuromorphic devices have attracted extensive attention, especially in simulating visual perception and memory storage. Because of its advantages of high bandwidth, high interference immunity, ultrafast signal transmission and lower energy consumption, neuromorphic photonic devices are expected to realize real-time response to input data. In addition, photonic synapses can realize non-contact writing strategy, which contributes to the development of wireless communication. The use of low-dimensional materials provides an opportunity to develop complex brain-like systems and low-power memory logic computers.

Scientists have recently designed an artificial flexible sensory nerve capable of neural coding, tactile sensing, and performing synaptic processing functions. Interestingly, this device does not depend on algorithms or computing resources. The study is available in Advanced Science. In humans, tactile recognition and perception have been associated with the determination of strength and dynamics of sensory stimulations, which are subjected to the skin via touch (active or passive). The external stimuli or touch is perceived by sensory receptors, which are present on the skin, and are encoded as neural spikes.

IBM Research, T.J. Watson Research Center, Yorktown Heights, NY Abstract The computational performance of the biological brain has long attracted significant interest and has led to inspirations in operating principles, algorithms, and architectures for computing and signal processing. In this work, we focus on hardware implementation of brain-like learning in a brain-inspired architecture. We demonstrate, in hardware, that 2-D crossbar arrays of phase change synaptic devices can achieve associative learning and perform pattern recognition. Device and array-level studies using an experimental 10 10 array of phase change synaptic devices have shown that pattern recognition is robust against synaptic resistance variations and large variations can be tolerated by increasing the number of training iterations. Our measurements show that increase in initial variation from 9 % to 60 % causes required training iterations to increase from 1 to 11. I. Introduction Synaptic electronics is an emerging field of research aiming to build electronic systems that mimic computational energyefficiency and fault tolerance of biological brain in a compact space [1]. Figure 1: Left figure is a DSI (diffusion spectrum imaging) scan showing a fabric-like 3-D grid structure of connections in the monkey brain (Credit: Van Wedeen, M.D., Martinos Center and Dept. of Radiology, Massachusetts General Hospital and Harvard University Medical School) [6].