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

Panajotov K., Tlidi M.

We introduce a spin-flip model for a broad-area vertical-cavity surface-emitting laser (VCSEL) with a saturable absorber. We demonstrate simultaneous existence of orthogonally linearly polarized and elliptically polarized cavity solitons. We show that polarization degree of freedom leads to a period-doubling route to spatially localized chaos of the elliptically polarized cavity solitons.

Deng T., Robertson J., Wu Z., Xia G., Lin X., Tang X., Wang Z., Hurtado A.

We investigate experimentally and theoretically the communication of inhibited spiking dynamics between two interlinked photonic neurons based upon the vertical-cavity surface-emitting lasers (VCSELs). We show that the sub-nanosecond speed spiking dynamics fired by a Transmitter-VCSEL (T-VCSEL) can be inhibited under the arrival of suitable external stimuli and that the inhibited spiking behaviors are propagated into another Receiver-VCSEL (R-VCSEL). Both VCSELs exhibit analogous inhibited spiking dynamics in response to stimuli with different temporal durations and strength. In addition, a very good agreement is found between theoretical simulations and experiments. These results offer greater prospects for future networks of VCSEL-based photonic neurons for neuromorphic computing platforms.

Zhang Y., Xiang S., Guo X., Wen A., Hao Y.

The spike encoding properties of two polarization-resolved modes in vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) are investigated numerically, based on the spin-flip model combined with the Yamada model. The results show that the external input optical pulse (EIOP) can be encoded into spikes in X-polarization (XP) mode, Y-polarization (YP) mode, or both XP and YP modes. Furthermore, the numerical bifurcation diagrams show that a lower (higher) strength of EIOP is beneficial for generating tonic (phasic) spikes; a small amplitude anisotropy contributes to wide (narrow) tonic spiking range in XP (YP) mode; a large current leads to low thresholds of EIOP strength for both XP and YP modes. However, the spike encoding properties are hardly affected by the phase anisotropy. The encoding rate is shown to be improved by increasing EIOP strength. Moreover, dual-channel polarization-multiplexed spike encoding can also be achieved in a single VCSEL-SA. To the best of our knowledge, such single channel polarization-resolved and dual-channel polarization-multiplexed spike encoding schemes have not yet been reported. Hence, this work is valuable for ultrafast photonic neuromorphic systems and brain-inspired information processing.

Xiang S., Zhang Y., Guo X., Wen A., Hao Y.

We propose to generate excitatory and inhibitory neuron-like dynamics in vertical-cavity surface-emitting lasers (VCSELs) by applying simultaneously the orthogonally-polarized CW optical injection (OPCWOI) and parallelly-polarized pulse optical injection stimulus. Based on the spin flip model, excitatory and inhibitory neuron-like dynamics accompanying with reverse polarization switching is numerically investigated. It is found that, due to the injection locking effect or beating effect between two injected fields, the excitatory phasic and tonic spiking dynamics can be obtained in the originally dominated polarization mode. Moreover, the unwanted relaxation oscillation followed by the excitatory spiking dynamics at the end of the stimulus pulse, which is present in previous reported photonic neuron based on the VCSELs subject to a single orthogonally-polarized optical pulse injection, can be completely suppressed. In addition, the inhibition of tonic spiking dynamics can also be achieved, and the transition from tonic spiking dynamics to phasic bursting dynamics can be obtained, when the two injected fields have the same frequency. These results are interesting and valuable for the ultrafast photonic neuromorphic systems and neuron-inspired photonic information processing.

Robertson J., Ackemann T., Lester L.F., Hurtado A.

Controlled generation and inhibition of externally-triggered picosecond optical pulsating regimes are demonstrated experimentally in a quantum dot mode locked laser (QDMLL) subject to external injection of an amplitude modulated optical signal. This approach also allows full control and repeatability of the time windows of generated picosecond optical pulses; hence permitting to define precisely their temporal duration (from <1 ns spans) and repetition frequency (from sub-Hz to at least hundreds of MHz). The use of a monolithic QDMLL, operating at 1300 nm, provides a system with a very small footprint that is fully compatible with optical telecommunication networks. This offers excellent prospects for use in applications requiring the delivery of ultrashort optical pulses at precise time instants and at tunable rates, such as optical imaging, time-of-flight diagnostics and optical communication systems.

Ma P.Y., Shastri B.J., Ferreira de Lima T., Huang C., Tait A.N., Nahmias M.A., Peng H., Prucnal P.R.

Neocortical systems encode information in electrochemical spike timings, not just mean firing rates. Learning and memory in networks of spiking neurons is achieved by the precise timing of action potentials that induces synaptic strengthening (with excitation) or weakening (with inhibition). Inhibition should be incorporated into brain-inspired spike processing in the optical domain to enhance its information-processing capability. We demonstrate the simultaneous excitatory and inhibitory dynamics in an excitable (i.e., a pulsed) laser neuron, both numerically and experimentally. We investigate the bias strength effect, inhibitory strength effect, and excitatory and inhibitory input timing effect, based on the simulation platform of an integrated graphene excitable laser. We further corroborate these analyses with proof-of-principle experiments utilizing a fiber-based graphene excitable laser, where we introduce inhibition by directly modulating the gain of the laser. This technology may potentially open novel spike-processing functionality for future neuromorphic photonic systems.

Schelte C., Panajotov K., Tlidi M., Gurevich S.V.

We consider a wide-aperture surface-emitting laser with a saturable absorber section subjected to time-delayed feedback. We adopt the mean-field approach assuming a single longitudinal mode operation of the solitary VCSEL. We investigate cavity soliton dynamics under the effect of time- delayed feedback in a self-imaging configuration where diffraction in the external cavity is negligible. Using bifurcation analysis, direct numerical simulations and numerical path continuation methods, we identify the possible bifurcations and map them in a plane of feedback parameters. We show that for both the homogeneous and localized stationary lasing solutions in one spatial dimension the time-delayed feedback induces complex spatiotemporal dynamics, in particular a period doubling route to chaos, quasiperiodic oscillations and multistability of the stationary solutions.

Li N., Susanto H., Cemlyn B.R., Henning I.D., Adams M.J.

A detailed stability and bifurcation analysis of spin-polarized vertical-cavity surface-emitting lasers (VCSELs) is presented. We consider both steady-state and dynamical regimes. In the case of steady-state operation, we carry out a small-signal (asymptotic) stability analysis of the steady-state solutions for a representative set of spin-VCSEL parameters. Compared with full numerical simulation, we show this produces surprisingly accurate results over the whole range of pump ellipticity, and spin-VCSEL bias up to 1.5 times the threshold. We then combine direct numerical integration of the extended spin-flip model and standard continuation technique to examine the underlying dynamics. We find that the spin VCSEL undergoes a period-doubling or quasiperiodic route to chaos as either the pump magnitude or polarization ellipticity is varied. Moreover, we find that different dynamical states can coexist in a finite interval of pump intensity, and observe a hysteresis loop whose width is tunable via the pump polarization. Finally we report a comparison of stability maps in the plane of the pump polarization against pump magnitude produced by categorizing the dynamic output of a spin VCSEL from time-domain simulations, against supercritical bifurcation curves obtained by the standard continuation package auto. This helps us better understand the underlying dynamics of the spin VCSELs.

Robertson J., Deng T., Javaloyes J., Hurtado A.

We report experimentally and theoretically on the controllable inhibition of spiking regimes in a 1300 nm wavelength vertical-cavity surface-emitting laser. Reproducible suppression of spiking dynamics is demonstrated at fast operation speeds (up to sub-ns rates) and with total control on the temporal duration of the spiking inhibition windows. This Letter opens new paths toward a photonic inhibitory neuronal model system for use in future neuromorphic photonic information processing modules and which are able to operate at speeds up to 8 orders of magnitude faster than biological neurons.

Xiang S., Wen A., Pan W.

The dynamical response properties of photonic neuron based on vertical-cavity surface emitting lasers (VCSELs) subject to orthogonal polarized optical pulse injection stimuli have been numerically investigated. Based on the well-known spin flip model, we first reproduce some experimental findings of neuron-like dynamics in VCSELs, such as phasic spiking with a single abrupt pulse, and tonic spiking with multiple periodic pulses. Besides, we further go beyond in three directions and obtain several novel results. The operating parameter ranges corresponding to different neuron-like dynamics are identified by extensive bifurcation analysis. In addition, the effect of the time-varying pump current on the neuron-like dynamics for VCSELs under given optical injecting pulse strength is also discussed. For a given pump current, the spiking frequency dependence on the stimuli strength is further revealed in VCSELs with time-varying optical pulse injection. Such controllable neuron-like response dynamics and spiking frequency dependence in VCSELs are interesting and valuable for ultrafast photonic neuromorphic systems and neuron-inspired photonic information processing.

Selmi F., Braive R., Beaudoin G., Sagnes I., Kuszelewicz R., Barbay S.

We report on experimental evidence of neuronlike excitable behavior in a micropillar laser with saturable absorber. We show that under a single pulsed perturbation the system exhibits subnanosecond response pulses and analyze the role of the laser bias pumping. Under a double pulsed excitation we study the absolute and relative refractory periods, similarly to what can be found in neural excitability, and interpret the results in terms of a dynamical inhibition mediated by the carrier dynamics. These measurements shed light on the analogy between optical and biological neurons and pave the way to fast spike-time coding based optical systems with a speed several orders of magnitude faster than their biological or electronic counterparts.

Ostojic S.

Here the author shows that an unstructured, sparsely connected network of model spiking neurons can display two different types of asynchronous activity: one in which an external input leads to a highly redundant response of different neurons that favors information transmission and another in which the firing rates of individual neurons fluctuate strongly in time and across neurons to provide a substrate for complex information processing. Asynchronous activity in balanced networks of excitatory and inhibitory neurons is believed to constitute the primary medium for the propagation and transformation of information in the neocortex. Here we show that an unstructured, sparsely connected network of model spiking neurons can display two fundamentally different types of asynchronous activity that imply vastly different computational properties. For weak synaptic couplings, the network at rest is in the well-studied asynchronous state, in which individual neurons fire irregularly at constant rates. In this state, an external input leads to a highly redundant response of different neurons that favors information transmission but hinders more complex computations. For strong couplings, we find that the network at rest displays rich internal dynamics, in which the firing rates of individual neurons fluctuate strongly in time and across neurons. In this regime, the internal dynamics interact with incoming stimuli to provide a substrate for complex information processing and learning.

Fernandes B.J., Cavalcanti G.D., Ren T.I.

The human visual system is one of the most fascinating and complex mechanisms of the central nervous system that enables our capacity to see. It is through the visual system that we are able to accomplish from the most simple task such as object recognition to the most complex visual interpretation, understanding and perception. Inspired by this sophisticated system, two models based on the properties of the human visual system are proposed. These models are designed based on the concepts of receptive and inhibitory fields. The first model is a pyramidal neural network with lateral inhibition, called lateral inhibition pyramidal neural network. The second proposed model is a supervised image segmentation system, called segmentation and classification based on receptive fields. This work shows that the combination of these two models is beneficial, and the results obtained are better than that of other state-of-the-art methods.

Nahmias M.A., Shastri B.J., Tait A.N., Prucnal P.R.

We propose an original design for a neuron-inspired photonic computational primitive for a large-scale, ultrafast cognitive computing platform. The laser exhibits excitability and behaves analogously to a leaky integrate-and-fire (LIF) neuron. This model is both fast and scalable, operating up to a billion times faster than a biological equivalent and is realizable in a compact, vertical-cavity surface-emitting laser (VCSEL). We show that-under a certain set of conditions-the rate equations governing a laser with an embedded saturable absorber reduces to the behavior of LIF neurons. We simulate the laser using realistic rate equations governing a VCSEL cavity, and show behavior representative of cortical spiking algorithms simulated in small circuits of excitable lasers. Pairing this technology with ultrafast, neural learning algorithms would open up a new domain of processing.

Hurtado A., Schires K., Henning I.D., Adams M.J.

We report an approach based upon vertical cavity surface emitting lasers (VCSELs) to reproduce optically different behaviors exhibited by biological neurons but on a much faster timescale. The technique proposed is based on the polarization switching and nonlinear dynamics induced in a single VCSEL under polarized optical injection. The particular attributes of VCSELs and the simple experimental configuration used in this work offer prospects of fast, reconfigurable processing elements with excellent fan-out and scaling potentials for use in future computational paradigms and artificial neural networks.

Ruschel S., Pammi A., BRAIVE R., Sagnes I., Beaudoin G., Broderick N., Krauskopf B., Barbay S.

We report on the polarization dynamics of regenerative light pulses in a micropillar laser with an integrated saturable absorber (SA) coupled to an external feedback mirror. The delayed self-coupled ML is operated in the excitable regime, where it regenerates incident pulses with a supra-threshold intensity—resulting in a pulse train with an inter-pulse period approximately given by the feedback delay time, in analogy with a self-coupled biological neuron. We report the experimental observation of regenerative vectorial breathers (RVBs) in a polarization angle, manifesting themselves as a modulation of the linear polarized intensity components without a significant modulation of the total intensity. A numerical analysis of a suitable model reveals that the observed polarization mode competition is a consequence of symmetry-breaking bifurcations induced by polarization anisotropy. Our model reproduces well the observed experimental results and predicts different regimes as a function of the polarization anisotropy parameters and the pump parameter. We believe that these findings are relevant for the fabrication of flexible sources of polarized pulses, as well as for neuroinspired on-chip computing applications.

Zhang Y., Huang Z., Xiang S., Guo X., Zhang W., Tan Q., Han G., Hao Y.

Li J., Peng L., Li S., Zhang L., Ding X., Jiang L., Zou X., Pan W., Yan L.

Xiang S., Han Y., Gao S., Song Z., Zhang Y., Zheng D., Yu C., Guo X., Zeng X., Huang Z., Hao Y.

Photonic neuromorphic computing has emerged as a promising avenue toward building a high-speed, low-latency, and energy-efficient non-von-Neumann computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. Linear weighting and nonlinear spiking activation are two fundamental functions of a SNN. However, the nonlinear computation of PSNN remains a significant challenge. Therefore, this perspective focuses on the nonlinear computation of photonic spiking neurons, including numerical simulation, device fabrication, and experimental demonstration. Different photonic spiking neurons are considered, such as vertical-cavity surface-emitting lasers, distributed feedback (DFB) lasers, Fabry–Pérot (FP) lasers, or semiconductor lasers embedded with saturable absorbers (SAs) (e.g., FP-SA and DFB-SA). PSNN architectures, including fully connected and convolutional structures, are developed, and supervised and unsupervised learning algorithms that take into account optical constraints are introduced to accomplish specific applications. This work covers devices, architectures, learning algorithms, and applications for photonic and optoelectronic neuromorphic computing and provides our perspective on the challenges and prospects of photonic neuromorphic computing based on semiconductor lasers.

Li N., feng Y., HUANG Y., Zhou P., Mu P., Xiang S.

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.

Zhang Q., Jiang N., Li A., Zhang Y., Hu G., Cao Y., Qiu K.

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.

Guo X., Su Y.

The computing research is undergoing a revolutionary transformation echoing the ever-growing demands of bandwidth, computing speed, and power consumption. Brain-inspired photonic neuromorphic networks combine the efficiency of neural networks based on a non-von Neumann architecture and the benefits of photonics to form a new computing paradigm that can perform as a promising approach to expand the domain of electronic artificial intelligence. They provide complementary advantages to their electronic counterparts in high-parallelism, subnanosecond latencies, orders-of-magnitude higher computing speed, and energy efficiency. Research in neuromorphic photonics in both integrated platforms and free-space optics has been booming in terms of applications like machine learning acceleration, nonlinear programming, intelligent signal processing, etc. In this chapter, we hierarchically summarize the recent advances in neuromorphic photonic networks, including the concept, principle, key photonic components, and the architectures that construct the neuromorphic systems. We also discuss the current and future challenges with an outlook hoping to provide a better understanding of this emerging field.

ZHANG Y., Xiang S., Guo X., Han Y., Shi Y., Chen X., Han G., Hao Y.

Dendrites, branches of neurons that transmit signals between synapses and soma, play a vital role in spiking information processing, such as nonlinear integration of excitatory and inhibitory stimuli. However, the investigation of nonlinear integration of dendrites in photonic neurons and the fabrication of photonic neurons including dendritic nonlinear integration in photonic spiking neural networks (SNNs) remain open problems. Here, we fabricate and integrate two dendrites and one soma in a single Fabry–Perot laser with an embedded saturable absorber (FP-SA) neuron to achieve nonlinear integration of excitatory and inhibitory stimuli. Note that the two intrinsic electrodes of the gain section and saturable absorber (SA) section in the FP-SA neuron are defined as two dendrites for two ports of stimuli reception, with one electronic dendrite receiving excitatory stimulus and the other receiving inhibitory stimulus. The stimuli received by two electronic dendrites are integrated nonlinearly in a single FP-SA neuron, which generates spikes for photonic SNNs. The properties of frequency encoding and spatiotemporal encoding are investigated experimentally in a single FP-SA neuron with two electronic dendrites. For SNNs equipped with FP-SA neurons, the range of weights between presynaptic neurons and postsynaptic neurons is varied from negative to positive values by biasing the gain and SA sections of FP-SA neurons. Compared with SNN with all-positive weights realized by only biasing the gain section of photonic neurons, the recognition accuracy of Iris flower data is improved numerically in SNN consisting of FP-SA neurons. The results show great potential for multi-functional integrated photonic SNN chips.

Gu S., Zhou P., Mu P., Guo G., LIU X., Li N.

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.

Zhang Q., Jiang N., Li A., Zhang Y., Hu G., Cao Y., Qiu K.

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.

Song Z., Xiang S., Zhao S., Zhang Y., Guo X., Tian Y., Shi Y., Hao Y.

We propose an inferencing framework of a hybrid-integrated photonic spiking neural network (PSNN) to perform pattern recognition tasks, where the linear computation is realized based on a 4 × 4 silicon photonic Mach-Zehnder interferometer (MZI) array, and the nonlinear computation is performed by an InP-based spiking neuron array based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA). With the modified Tempotron-like remote supervised method (ReSuMe) training algorithm, we realize two pattern recognition tasks, the recognition of numbers “0-3” and optical character recognition (OCR). The phase shifts in the MZI array are accurately configured to represent the weight matrix according to the decomposition procedure of a 4 × 4 triangular MZI mesh. Besides, the effects of the phase shift error and quantization precision of phase shifters (PSs) on the recognition performance are analyzed. For the OCR task, the 400 × 10 PSNN is realized by multiplexing the 4 × 4 MZI array based on the matrix blocking and the reconfigurability of the MZI array. This work provides a systematic computational model of the hybrid-integrated PSNN based on the silicon photonics and InP platforms, enabling the co-design and optimization of hardware architecture and algorithm, which contributes one step forward toward the construction of a hybrid-integrated PSNN hardware system.

Dillane M., Viktorov E., Kelleher B.

Zhao S., Xiang S., Song Z., Zhang Y., Cao X., Wen A., Hao Y.

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.

El Srouji L., Krishnan A., Ravichandran R., Lee Y., On M., Xiao X., Ben Yoo S.J.

Recent advances in neuromorphic computing have established a computational framework that removes the processor-memory bottleneck evident in traditional von Neumann computing. Moreover, contemporary photonic circuits have addressed the limitations of electrical computational platforms to offer energy-efficient and parallel interconnects independently of the distance. When employed as synaptic interconnects with reconfigurable photonic elements, they can offer an analog platform capable of arbitrary linear matrix operations, including multiply–accumulate operation and convolution at extremely high speed and energy efficiency. Both all-optical and optoelectronic nonlinear transfer functions have been investigated for realizing neurons with photonic signals. A number of research efforts have reported orders of magnitude improvements estimated for computational throughput and energy efficiency. Compared to biological neural systems, achieving high scalability and density is challenging for such photonic neuromorphic systems. Recently developed tensor-train-decomposition methods and three-dimensional photonic integration technologies can potentially address both algorithmic and architectural scalability. This tutorial covers architectures, technologies, learning algorithms, and benchmarking for photonic and optoelectronic neuromorphic computers.

Xiang J., Zhang Y., Zhao Y., Guo X., Su Y.

With the rapid development of artificial intelligence and machine learning, brain-inspired neuromorphic photonics has emerged as an extremely attractive computing paradigm, promising orders-of-magnitude higher computing speed and energy efficiency compared to its electronic counterparts. Tremendous efforts have been devoted to photonic hardware implementations of mimicking the nonlinear neuron-like spiking response and the linear synapse-like weighting functionality. Here, we systematically characterize the spiking dynamics of a passive silicon microring neuron. The research of self-pulsation and excitability reveals that the silicon microring can function as an all-optical class II resonate-and-fire neuron. The typical refractory period has been successfully suppressed by configuring the pump power above the perturbation power, hence allowing the microring neuron to operate with a speed up to roughly sub-gigahertz. Additionally, temporal integration and controllable inhibition regimes are experimentally demonstrated for the first time, to the best of our knowledge. Our experimental verification is obtained with a commercial CMOS platform, hence offering great potential for large-scale neuromorphic photonics integration.