Photonic implementation of the input and reservoir layers for a reservoir computing system based on a single VCSEL with two Mach-Zehnder modulators.

Hardware implementation of reservoir computing (RC), which could reduce the power consumption of machine learning and significantly enhance data processing speed, holds the potential to develop the next generation of machine learning hardware devices and chips. Due to the existing solution only implementing reservoir layers, the information processing speed of photonics RC system are limited. In this paper, a photonic implementation of a VMM-RC system based on single Vertical Cavity Surface Emitting Laser (VCSEL) with two Mach Zehnder modulators (MZMs) has been proposed. Unlike previous work, both the input and reservoir layers are realized in the optical domain. Additionally, the impact of various mask signals, such as Two-level mask, Six-level mask, and chaos mask signal, employed in system, has been investigated. The system's performance improves with the use of more complex mask(t). The minimum Normalized mean square error (NMSE) can reach 0.0020 (0.0456) for Santa-Fe chaotic time series prediction in simulation (experiment), while the minimum Word Error Rate (WER) can 0.0677 for handwritten digits recognition numerically. The VMM-RC proposed is instrumental in advancing the development of photonic RC by overcoming the long-standing limitations of photonic RC systems in reservoir implementation. Linear matrix computing units (the input layer) and nonlinear computing units (the reservoir layer) are simultaneously implemented in the optical domain, significantly enhancing the information processing speed of photonic RC systems.

Characterizing the aggregated encoding method utilizing bursts activated by a VCSEL-neuron with a feedback structure.

The rapid advancement of photonic technologies has facilitated the development of photonic neurons that emulate neuronal functionalities akin to those observed in the human brain. Neuronal bursts frequently occur in behaviors where information is encoded and transmitted. Here, we present the demonstration of the bursting response activated by an artificial photonic neuron. This neuron utilizes a single vertical-cavity surface-emitting laser (VCSEL) and encodes multiple stimuli effectively by varying the spike count during a burst based on the polarization competition in the VCSEL. By virtue of the modulated optical injection in the VCSEL employed to trigger the spiking response, we activate bursts output in the VCSEL with a feedback structure in this scheme. The bursting response activated by the VCSEL-neuron exhibits neural signal characteristics, promising an excitation threshold and the refractory period. Significantly, this marks the inaugural implementation of a controllable integrated encoding scheme predicated on bursts within photonic neurons. There are two remarkable merits; on the one hand, the interspike interval of bursts is distinctly diminished, amounting to merely one twenty-fourth compared to that observed in optoelectronic oscillators. Moreover, the interspike period of bursts is about 70.8% shorter than the period of spikes activated by a VCSEL neuron without optical feedback. Our results may shed light on the analogy between optical and biological neurons and open the door to fast burst encoding-based optical systems with a speed several orders of magnitude faster than their biological counterparts.

Hybrid photonic deep convolutional residual spiking neural networks for text classification.

Spiking neural networks (SNNs) offer powerful computation capability due to its event-driven nature and temporal processing. However, it is still limited to shallow structure and simple tasks due to the training difficulty. In this work, we propose a deep convolutional residual spiking neural network (DCRSNN) for text classification tasks. In the DCRSNN, the feature extraction is achieved via a convolution SNN with residual connection, using the surrogate gradient direct training technique. Classification is performed by a fully-connected network. We also suggest a hybrid photonic DCRSNN, in which photonic SNNs are used for classification with a converted training method. The accuracy of hard and soft reset methods, as well as three different surrogate functions, were evaluated and compared across four different datasets. Results indicated a maximum accuracy of 76.36% for MR, 91.03% for AG News, 88.06% for IMDB and 93.99% for Yelp review polarity. Soft reset methods used in the deep convolutional SNN yielded slightly better accuracy than their hard reset counterparts. We also considered the effects of different pooling methods and observation time windows and found that the convergence accuracy achieved by convolutional SNNs was comparable to that of convolutional neural networks under the same conditions. Moreover, the hybrid photonic DCRSNN also shows comparable testing accuracy. This work provides new insights into extending the SNN applications in the field of text classification and natural language processing, which is interesting for the resources-restrained scenarios.

Noisy image segmentation based on synchronous dynamics of coupled photonic spiking neurons.

The collective dynamics in neural networks is essential for information processing and has attracted much interest on the application in artificial intelligence. Synchronization is one of the most dominant phenomenon in the collective dynamics of neural network. Here, we propose to use the spiking dynamics and collective synchronization of coupled photonic spiking neurons for noisy image segmentation. Based on the synchronization mechanism and synchronization control, the noised pattern segmentation is demonstrated numerically. This work provides insight into the possible application based on the collective dynamics of large-scale photonic networks and opens a way for ultra-high speed image processing.

BP-based supervised learning algorithm for multilayer photonic spiking neural network and hardware implementation.

We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the information is encoded into spike trains with different strength, and the SNN is trained according to different patterns composed of different spike numbers of the output neurons. Furthermore, the classification task is performed numerically and experimentally based on the supervised learning algorithm in the SNN. The SNN is composed of photonic spiking neuron based on vertical-cavity surface-emitting laser which is functionally similar to leaky-integrate and fire neuron. The results prove the demonstration of the algorithm implementation on hardware. To seek ultra-low power consumption and ultra-low delay, it is great significance to design and implement a hardware-friendly learning algorithm of photonic neural networks and realize hardware-algorithm collaborative computing.

Neuromorphic convolution with a spiking DFB-SA laser neuron based on rate coding.

We propose a neuromorphic convolution system using a photonic integrated distributed feedback laser with a saturable absorber (DFB-SA) as a photonic spiking neuron. The experiments reveal that the DFB-SA laser can encode different stimulus intensities at different frequencies, similar to biological neurons. Based on this property, optical inputs are encoded into rectangular pulses of varying intensities and injected into the DFB-SA laser, enabling the convolution results to be represented by the firing rate of the photonic spiking neuron. Both experimental and numerical results show that the binary convolution is successfully achieved based on the rate-encoding properties of a single DFB-SA laser neuron. Furthermore, we numerically predict 4-channel quadratic convolution and accomplish MNIST handwritten digit classification using a spiking DFB-SA laser neuron model with rate coding. This work provides a novel approach for convolution computation, indicating the potential of integrating DFB-SA laser into future photonics spiking neural networks.

Cigarette smoke extract-induced inflammatory response via inhibition of the TFEB-mediated autophagy in NR8383 cells.

Objective: Chronic pulmonary inflammation caused by long-term smoking is the core pathology of COPD. Alveolar macrophages (AMs) are involved in the pulmonary inflammation of COPD. The accumulation of damaged materials caused by impaired autophagy triggers inflammatory response in macrophages. As a key transcription regulator, transcription factor EB (TFEB) activates the transcription of target genes related autophagy and lysosome by binding to promoters, whereas it is unclarified for the relationship between inflammatory response induced by cigarette smoke extract (CSE) and TFEB-mediated autophagy. Thus, we investigated the role of TFEB-mediated autophagy in inflammatory response induced by CSE in NR8383 cells, and to explore its potential mechanism. Methods: Based on cell viability and autophagy, cells treated with 20% concentration of CSE for 24 h were selected for further studies. Cells were divided into control group, chloroquine (CQ, the autophagy inhibitor) group, CSE group, CSE + rapamycin (the autophagy inducer) group and CSE + fisetin (the TFEB inducer) group. The levels of tumor necrosis factor α (TNF-α), interleukin 1ß (IL-1ß), and IL-6 in supernatant were detected by ELISA kits. The protein expressions were tested by western blot. The intensity of fluorescence of Lysosome-associated membrane protein 1 (LAMP1) and TFEB was detected by immunofluorescence. Lyso-Tracker Red staining was applied to detect the lysosome environment. Results: CSE inhibited the cell viability, increased the contents of TNF-α, IL-1ß, IL-6, the ratio of LC3II/I, and the level of P62 protein. Besides, CSE decreased the fluorescence intensity of LAMP1 protein and Lyso-Tracker Red staining, as well as the ratio of nucleus/cytosol of TFEB protein. Activating autophagy with rapamycin alleviated CSE-induced inflammatory response. The activation of TFEB via fisetin alleviated CSE-induced autophagy impairment and lysosomal dysfunction, thus alleviated inflammatory response in NR8383 cells. Conclusion: CSE-induced inflammatory response in NR8383 cells, which may be related to the inhibition of TFEB-mediated autophagy.

Experimental implementation of spike-based neuromorphic XOR operation based on polarization-mode competition in a single VCSOA.

We experimentally and numerically propose an approach for implementing spike-based neuromorphic exclusive OR (XOR) operation using a single vertical-cavity semiconductor optical amplifier (VCSOA). XOR operation is realized based on the neuron-like inhibitory dynamics of the VCSOA subject to dual-polarized pulsed optical injections. The inhibitory dynamics based on the polarization-mode competition effect are analyzed, and the inhibitory response can be obtained in a suitable range of wavelength detuning. Here, all input and output bits are represented by spikes that are compatible with the photonic spiking neural network. The experimental and numerical results show that XOR operation can be realized in two polarization modes by adjusting the time offset in the inhibitory window and setting defined reference thresholds. In addition, the influences of delay time and input intensity ratio on XOR operation are studied experimentally. This scheme is energy efficient because VCSOA neuromorphic photonics computing and information processing.

Hydrogen regulates the M1/M2 polarization of alveolar macrophages in a rat model of chronic obstructive pulmonary disease.

OBJECTIVE: Chronic obstructive pulmonary disease (COPD) is a respiratory disease with high morbidity and mortality worldwide, so far there is no ideal treatment method. Previous studies have shown that hydrogen (H2) is involved in the treatment of COPD as an antioxidant. In this study, the effect of H2 on M1/M2 polarization of alveolar macrophages in COPD rats was observed, and its anti-inflammatory mechanism was further elucidated. Methods: Twenty-four Sprague-Dawley rats were randomly divided into three groups including the control, COPD and H2 group. A rat model of COPD was established by cigarette exposure combined with lipopolysaccharide (LPS) induction. H2 therapy was administered 2 hours per day for 14 days. Lung function and pathology were assessed. The levels of interleukin (IL)-6, tumor necrosis factor (TNF)-α, transforming growth factor (TGF)-ß1 and IL-10 in bronchoalveolar lavage fluid (BALF) and lung tissue were measured by enzyme-linked immunosorbent assay. The mRNA, protein expression and immunoreactivity of inducible nitric oxide synthase (iNOS) and arginase (Arg)-1 in lung were observed by quantitative real-time PCR, western blot and immunohistochemistry. Results: Compared with the control rats, there were a significant decline in lung function, a marked inflammatory infiltration and pulmonary parenchymal remodeling and the increases of IL-6, TNF-α and TGF-ß1 levels in BALF and lung tissue, but a lower expression of IL-10 in COPD rats. The iNOS mRNA and protein expression, as well as its optical density (OD), were increased significantly in lung tissue, while those of Arg-1 decreased significantly. H2 treatment improved the lung function and the parenchymal inflammation, reversed the increased levels of IL-6, TNF-α and TGF-ß1, and the lower IL-10. Meanwhile, H2 also down-regulated the expression of iNOS, but up-regulated expression of Arg-1 in lung tissue. Conclusion: H2 reduces inflammation in the lung of COPD, which may be related to its inhibition of M1 type polarization and activation of M2 type polarization of alveolar macrophage.

Image edge detection with a photonic spiking VCSEL-neuron.

We report both experimentally and in theory on the detection of edge features in digital images with an artificial optical spiking neuron based on a vertical-cavity surface-emitting laser (VCSEL). The latter delivers fast (< 100 ps) neuron-like optical spikes in response to optical inputs pre-processed using convolution techniques; hence representing image feature information with a spiking data output directly in the optical domain. The proposed technique is able to detect target edges of different directionalities in digital images by applying individual kernel operators and can achieve complete image edge detection using gradient magnitude. Importantly, the neuromorphic (brain-like) spiking edge detection of this work uses commercially sourced VCSELs exhibiting responses at sub-nanosecond rates (many orders of magnitude faster than biological neurons) and operating at the important telecom wavelength of 1300 nm; hence making our approach compatible with optical communication and data-centre technologies.

Time-delay signature concealment of chaos and ultrafast decision making in mutually coupled semiconductor lasers with a phase-modulated Sagnac loop.

We propose and experimentally demonstrate the generation of dual-channels chaos with time delay signature (TDS) concealment by introducing a phase-modulated Sagnac loop in mutually coupled semiconductor lasers (MCSL). Furthermore, we demonstrate the utilization of the dual-channels chaos to solve multi-armed bandit (MAB) problem in reinforcement learning. The experimental results agree well with the numerical simulations. For the purpose of comparison, the MCSL with a conventional Sagnac loop is also considered. It is found that the TDS of dual-channels chaotic signals can be better concealed in our proposed system. Besides, the proposed system allows for a better decision making performance in MAB problem. Moreover, compared with the one-channel chaotic system, the proposed dual-channels chaotic system achieves ultrafast decision making in parallel, and thus, is highly valuable for further improving the security of communication systems and the performance of photonic intelligence.

All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on a VCSEL-SA.

We propose a simple hardware architecture for solving exclusive OR (XOR) tasks in a single step by using a single photonic spiking neuron based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA) subject to dual-polarized pulsed optical injection. We model the inhibitory photonic spiking neuron by extending the Yamada model and spin-flip model to incorporate the two polarization-resolved modes and the saturable absorber. It is shown that, by carefully adjusting the temporal difference according to the inhibitory window, the XOR operation can be realized in a single photonic spiking neuron, which is interesting and valuable for the photonic neuromorphic computing and information processing.

Experimental investigation of the time-delay signature of chaotic output and dual-channel physical random bit generation in 1550 nm mutually coupled VCSELs with common FBG filtered feedback.

Here we propose and experimentally demonstrate mutually coupled 1550 nm vertical-cavity surface-emitting lasers (VCSELs), subject to common fiber Bragg grating (FBG) feedback (FMC-VCSELs), to conceal the time-delay signature (TDS) of chaotic outputs. The autocorrelation function and delayed mutual information are used to quantitatively identify the TDS of chaotic output. For comparison, the evolution of the TDS of chaotic output in mutually coupled VCSELs (MC-VCSELs) is also presented. The effects of injection power, frequency detuning between two VCSELs, and frequency detuning between FBG and VCSELs on the TDS concealment are experimentally measured. Experimental results show that FMC-VCSELs have a better TDS suppression performance than MC-VCSELs for varying injection power. In addition, for the FMC-VCSELs system, the TDS can be suppressed to below 0.1 when the VCSELs x-polarization component is located at the edge of the main FBG lobe. Furthermore, dual-channel physical random numbers with verified randomness at a rate of 800 Gbps (2×5LSBs×80GHz) are achieved by utilizing the chaotic outputs with a low TDS from two VCSELs in the FMC-VCSELs system.

Time-delay signature concealment and physical random bits generation in mutually coupled semiconductor lasers with FBG filtered injection.

We propose and demonstrate experimentally the generation of chaos with suppressed time-delay signature (TDS) and physical random bits in two mutually coupled semiconductor lasers (MCSL) by introducing an auxiliary fiber Bragg grating (FBG) filtered injection path. The measurements show that, even by simply adding a single FBG path, the TDS of chaotic signals generated by both lasers in our proposed scheme can be better concealed compared to the conventional MCSL system. Moreover, better TDS concealment can be achieved in the laser with smaller wavelength. Additionally, two random bit streams extracted from the two chaotic lasers in the proposed scheme are achieved with minimal post-processing. The total generation rate reaches 640-Gbps.

Common-injection-induced isolated desynchronization in delay-coupled VCSELs networks with variable-polarization optical feedback.

We have investigated the cluster isolated desynchronization, a symmetry-breaking state, in the delay-coupled vertical-cavity surface-emitting lasers (VCSELs) networks subject to variable-polarization optical feedback (VPOF). It is shown that, in the VPOF-VCSELs networks, the elusive isolated desynchronization phenomenon could emerge out of the cluster synchronization by the common-signal injection approach from an additional auxiliary VCSEL. The influences of parameters in VPOF-VCSELs networks on the existence and stability of isolated desynchronization are systematically investigated. Moreover, the generality of the proposed scheme is validated in the VPOF-VCSELs network with real-world network topology (Nepal power grid network). Our results offer a new insight to manage the synchronization patterns of a VCSELs network.

Cluster synchronization in symmetric VCSELs networks with variable-polarization optical feedback.

The cluster synchronization of mutually coupled vertical-cavity surface-emitting lasers (VCSELs) networks subject to variable-polarization optical feedback (VPOF) with symmetric structure is theoretically investigated. Zero-lag synchronization could be achieved between different VCSELs within same cluster in such networks, which is solely derived from the intrinsic symmetry of network topology. The influences of significant parameters of VCSELs networks on the stability of cluster synchronization are further discussed. Moreover, it is shown that the polarizer angle of optical feedback in VCSELs plays a particularly important role on the formation of cluster.

Spike encoding and storage properties in mutually coupled vertical-cavity surface-emitting lasers subject to optical pulse injection.

The generation and storage properties of different spike codes in vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA) are investigated numerically. The results show that different spike codes are generated by injecting an optical pulse into one single VCSEL-SA and can be stored in two mutually coupled VCSELs-SA. In particular, in the case of the generation of spike codes, the effects of the input strength and the temporal duration of the input optical pulse are studied; in the case of the storage of spike codes, the roles of the coupling weight and the coupling delay between the two mutually coupled VCSELs-SA are examined. Simulations reveal that spikes can be triggered if the input strength and the temporal duration exceed the threshold values, and higher values of the input strength and the temporal duration are beneficial for generating more spikes. Moreover, successful storage of a perfectly formed train of spike codes in two mutually coupled VCSELs-SA can also be realized provided that the coupling weight and the coupling delay are larger than the corresponding threshold values.

Complexity-enhanced polarization-resolved chaos in a ring network of mutually coupled vertical-cavity surface-emitting lasers with multiple delays.

The complexity properties of polarization-resolved chaotic signals generated in a ring network of vertical-cavity surface-emitting lasers (VCSELs) mutually coupled with multiple delays are investigated quantitatively by using the proposed mean permutation entropy (MPE). For direct comparison, the complexity of polarization-resolved chaos in a ring network of VCSELs coupled with single delay is also considered. The effects of injection current, coupling strength, and frequency detuning on the chaotic complexity for both a single-delay ring network (SDRN) and a multiple-delay ring network (MDRN) are evaluated quantitatively and compared by the MPE. The effects of internal parameters of VCSELs on the complexity are also discussed, and the correlation properties between different polarization-resolved modes are also analyzed. It is shown that the complexity of chaos in two polarization-resolved modes of VCSELs in MDRN is much higher than those in SDRN in a much wider parameter region. Besides, wider range of injection current, coupling strength, and frequency detuning can be tuned to achieve the enhancement of chaotic complexity in MDRN. These results provide an effective quantifier, the proposed MPE, to evaluate quantitatively the complexity of chaos generated in systems with multiple delays, and the multichannel complexity-enhanced polarization-resolved chaos generated in MDRN of mutually coupled VCSELs is extremely meaningful for the chaos-based random number generators.

Photonic Doppler frequency shift measurement based on a dual-polarization modulator.

In this paper, a novel photonic-assisted Doppler frequency shift (DFS) measurement scheme based on an integrated dual-polarization Mach-Zehnder modulator is presented. In the proposed scheme, the DFS to be identified is transformed into a low-frequency electrical signal through an optical frequency-conversion link. The value of the DFS can be acquired by analyzing the spectrum of the low-frequency electrical signal. Meanwhile, the orientation of the DFS can be easily determined utilizing a 90° hybrid coupler. If the receiver is moving toward the transmitter, only the positive port has an output signal, while only the negative port has an output signal if the receiver is moving away from the transmitter. The scheme can simultaneously obtain the value and the orientation of the DFS. In addition, to investigate the frequency tunability of the proposed scheme, the DFS, which varies from -100 to 100 KHz at a step of 10 KHz for different microwave signals at frequencies of 10, 15, and 18 GHz, is demonstrated experimentally, and the errors are within ±5×10-6 Hz.

Photonic microwave frequency measurement with a tunable range based on a dual-polarization modulator.

This paper presents a novel photonic-assisted microwave frequency measurement scheme based on an integrated dual-polarization Mach-Zehnder modulator (DPol-MZM). The DPol-MZM is used to obtain a polarization multiplexing signal modulated by the microwave signal with the frequency to be identified. The obtained signal is split into two channels after propagating along a single mode fiber. The two divided parts are used to establish an amplitude comparison function (ACF) which provides frequency-power mapping. The proposed scheme is experimentally verified. The frequency responses of the two branches are nearly complementary; thus, a relatively steep ACF is obtained. A frequency measurement range from 2 to 28 GHz with an error of ±0.2 GHz is achieved. Moreover, the measurement range can be tuned by simply adjusting the polarization state of one channel. The proposed system is simple, and the measurement range can be easily adjusted.