All-optical inhibitory dynamics in photonic neuron based on polarization mode competition in a VCSEL with an embedded saturable absorber

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.

All-optical neuromorphic XOR and XNOR operation utilizing a photonic spiking neuron based on a passive add-drop microring resonator

We propose a concise hardware architecture supporting efficient exclusive OR (XOR) and exclusive NOR (XNOR) operations, by employing a single photonic spiking neuron based on a passive add-drop microring resonator (ADMRR). The threshold mechanism and inhibitory dynamics of the ADMRR-based spiking neuron are numerically discussed on the basis of the coupled mode theory. It is shown that a precise XOR operation in the ADMRR-based spiking neuron can be implemented by adjusting temporal differences within the inhibitory window. Additionally, within the same framework, the XNOR function can also be carried out by accumulating the input power over time to trigger an excitatory behavior. This work presents a novel, to the best of our knowledge, and pragmatic technique for optical neuromorphic computing and information processing utilizing passive devices.

Phase stability diagram, self-similar structures, and multistability in a free-running VCSEL with a small misalignment between the phase and amplitude anisotropies

We report on the global dynamics of a free-running vertical-cavity surface-emitting laser (VCSEL) with misalignment between the linear phase and amplitude anisotropies due to the fact that this case might occur in practice caused unintentionally by minor manufacturing variations or design, in virtue of high-resolution phase stability diagrams, where two kinds of self-similar structures are revealed. Of interest is that the Arnold tongue cascades covered by multiple distinct periodicities are discovered for the first time in several scenarios specified in the free-running VCSEL, to the best of our knowledge. Additionally, we also uncover the existence of multistability through the basin of the attraction, as well as the eyes of anti-chaos and periodicity characterized by fractal. The findings may shed new light on interesting polarization dynamics of VCSELs, and also open the possibility to detect the above-mentioned structures experimentally and develop some potential applications.

All-optical synaptic neuron based on add-drop microring resonator with power-tunable auxiliary light

We propose and demonstrate an all-optical synaptic neuron based on an add-drop microring resonator (ADMRR) with power-tunable auxiliary light. Dual neural dynamics of passive ADMRRs, having spiking response and synaptic plasticity, are numerically investigated. It is demonstrated that, by injecting two beams of power-tunable and opposite-direction continuous light into an ADMRR and maintaining their sum power at a constant value, linear-tunable and single-wavelength neural spikes can be flexibly generated, in virtue of the nonlinear effects triggered by perturbation pulses. Based on this, a weighting operation system based on cascaded ADMRRs is designed; it enables implementation of real-time weighting operations at a number of wavelengths. This work provides a novel, to the best of our knowledge, approach for integrated photonic neuromorphic systems based entirely on optical passive devices.

Inhibitory and excitatory integration with a quantum dot laser neuron

Neuromorphic computing has garnered a lot of attention in recent years. Excitable photonic systems in particular demonstrate great potential for ultrafast, controllable spike processing. Optically injected quantum dot lasers display several distinct excitable regimes. We demonstrate here that optically injected dual-state quantum dot lasers can display the classic leaky integrate-and-fire mechanism where the integration of several sub-threshold perturbations can yield an effective supra-threshold perturbation. Intriguingly, a contrasting integrate-and-inhibit mechanism is demonstrated in this work where the integration of two supra-threshold perturbations yields an effective sub-threshold perturbation similar to the pre-pulse inhibition mechanism of biological neurons. This is the first such mechanism in neuromorphic photonics to the best of our knowledge.

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.

An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning

Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the current mature CMOS platforms. Here, we present an architecture of photonic SNN which consists of photonic neuron, photonic spike timing dependent plasticity (STDP) and weight configuration that are all based on silicon micro-ring resonators (MRRs), via taking advantage of the nonlinear effects in silicon. The photonic spiking neuron based on the add-drop MRR is proposed, and a system-level computational model of all-MRR-based photonic SNN is presented. The proposed architecture could exploit the properties of small area, high integration and flexible structure of MRR, but also faces challenges caused by the high sensitivity of MRR. The spike sequence learning problem is addressed based on the proposed all-MRR-based photonic SNN architecture via adopting supervised training algorithms. We show the importance of algorithms when hardware devices are limited.

Computing Primitive of Fully VCSEL-Based All-Optical Spiking Neural Network for Supervised Learning and Pattern Classification

We propose computing primitive for an all-optical spiking neural network (SNN) based on vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically plausible mechanisms. The spike-timing-dependent plasticity (STDP) model was established based on the dynamics of the vertical-cavity semiconductor optical amplifier (VCSOA) subject to dual-optical pulse injection. The neuron-synapse self-consistent unified model of the all-optical SNN was developed, which enables reproducing the essential neuron-like dynamics and STDP function. Optical character numbers are trained and tested by the proposed fully VCSEL-based all-optical SNN. Simulation results show that the proposed all-optical SNN is capable of recognizing ten numbers by a supervised learning algorithm, in which the input and output patterns as well as the teacher signals of the all-optical SNN are represented by spatiotemporal fashions. Moreover, the lateral inhibition is not required in our proposed architecture, which is friendly to the hardware implementation. The system-level unified model enables architecture-algorithm codesigns and optimization of all-optical SNN. To the best of our knowledge, the computing primitive of an all-optical SNN based on VCSELs for supervised learning has not yet been reported, which paves the way toward fully VCSEL-based large-scale photonic neuromorphic systems with low power consumption.

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.

Photonic spiking neural network based on excitable VCSELs-SA for sound azimuth detection

We propose a photonic spiking neural network (SNN) based on excitable vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA) for emulating the sound azimuth detection function of the brain for the first time. Here, the spike encoding and response properties based on the excitability of VCSELs-SA are employed, and the difference between spike timings of two postsynaptic neurons serves as an indication of sound azimuth. Furthermore, the weight matrix contributing to the successful sound azimuth detection is carefully identified, and the effect of the time interval between two presynaptic spikes is considered. It is found that the weight range that can achieve sound azimuth detection decreases gradually with the increase of the time interval between the sound arriving at the left and right ears. Besides, the effective detection range of the time interval between two presynaptic spikes is also identified, which is similar to that of the biological auditory system, but with a much higher resolution which is at the nanosecond time scale. We further discuss the effect of device variations on the photonic sound azimuth detection. Hence, this photonic SNN is biologically plausible, which has comparable low energy consumption and higher resolution compared with the biological system. This work is valuable for brain-inspired information processing and a promising foundation for more complex spiking information processing implemented by photonic neuromorphic computing systems.