Generation of multi-channel chaotic signals with time delay signature concealment and ultrafast photonic decision making based on a globally-coupled semiconductor laser network

We propose and demonstrate experimentally and numerically a network of three globally coupled semiconductor lasers(SLs)that generate triple-channel chaotic signals with time delayed signature(TDS)concealment.The effects of the coupling strength and bias current on the concealment of the TDS are investigated.The generated chaotic signals are further applied to reinforcement learning,and a parallel scheme is proposed to solve the multiarmed bandit(MAB)problem.The influences of mutual correlation between signals from different channels,the sampling interval of signals,and the TDS concealment on the performance of decision making are analyzed.Comparisons between the proposed scheme and two existing schemes show that,with a simplified algorithm,the proposed scheme can perform as well as the previous schemes or even better.Moreover,we also consider the robustness of decision making performance against a dynamically changing environment and verify the scalability for MAB problems with different sizes.This proposed globally coupled SL network for a multi-channel chaotic source is simple in structure and easy to implement.The attempt to solve the MAB problem in parallel can provide potential values in the realm of the application of ultrafast photonics intelligence.

Delay-weight plasticity-based supervised learning in optical spiking neural networks

We propose a modified supervised learning algorithm for optical spiking neural networks,which introduces synaptic time-delay plasticity on the basis of traditional weight training.Delay learning is combined with the remote supervised method that is incorporated with photonic spike-timing-dependent plasticity.A spike sequence learning task implemented via the proposed algorithm is found to have better performance than via the traditional weight-based method.Moreover,the proposed algorithm is also applied to two benchmark data sets for classification.In a simple network structure with only a few optical neurons,the classification accuracy based on the delay-weight learning algorithm is significantly improved compared with weight-based learning.The introduction of delay adjusting improves the learning efficiency and performance of the algorithm,which is helpful for photonic neuromorphic computing and is also important specifically for understanding information processing in the biological brain.

Frontier research of ultra-high-speed ultra-large-capacity and ultra-long-haul optical transmission

Ultra-high-speed, ultra-large-capacity and ultra-long-haul （3U） are the forever pursuit of optical communication. As a new mode of optical communication, 3U transmission can greatly promote next generation optical internet and broadband mobile communication network development and technological progress, therefore it has become the focus of international high-tech intellectual property competition ground. This paper introduces the scientific problems, key technologies and important achievements in 3U transmission research.

Experimental demonstration of pyramidal neuron-like dynamics dominated by dendritic action potentials based on a VCSEL for all-optical XOR classification task

We experimentally and numerically demonstrate an approach to optically reproduce a pyramidal neuron-like dynamics dominated by dendritic Ca^(2+) action potentials(dCaAPs)based on a vertical-cavity surface-emitting laser(VCSEL)for the first time.The biological pyramidal neural dynamics dominated by dCaAPs indicates that the dendritic electrode evoked somatic spikes with current near threshold but failed to evoke(or evoked less)somatic spikes for higher current intensity.The emulating neuron-like dynamics is performed optically based on the injection locking,spiking dynamics,and damped oscillations in the optically injected VCSEL.In addition,the exclusive OR(XOR)classification task is examined in the VCSEL neuron equipped with the pyramidal neuronlike dynamics dominated by dCaAPs.Furthermore,a single spike or multiple periodic spikes are suggested to express the result of the XOR classification task for enhancing the processing rate or accuracy.The experimental and numerical results show that the XOR classification task is achieved successfully in the VCSEL neuron enabled to mimic the pyramidal neuron-like dynamics dominated by dCaAPs.This work reveals valuable pyramidal neuron-like dynamics in a VCSEL and offers a novel approach to solve XOR classification task with a fast and simple all-optical spiking neural network,and hence shows great potentials for future photonic spiking neural networks and photonic neuromorphic computing.