Fiber-optic frequency comb generation using low-seed power FWM and Brillouin-assisted power equalization.

In this paper, we experimentally demonstrate an all-fiber broadband tunable optical frequency comb (OFC) operating in the C-band. The OFC is generated by broadening a power-equalized stimulated Brillouin scattering (SBS)-based seed comb (SBS-OFC) using four-wave mixing (FWM) in a highly nonlinear fiber (HNLF). The seed SBS-OFC is obtained from a pump and Stokes power recycling cavity, which yields ≈15 comb lines with 10.8 GHz line spacing having 16 dBm average power. The seed SBS-OFC is further power-equalized by a Brillouin-assisted power equalization (BAPE) technique to minimize the high pump contribution at the recycling cavity output. The power-equalized seed SBS-OFC, which has low-power of -4.5d B m at the BAPE cavity output, is propagated down a dual-pass 200 m dispersion flattened HNLF. At the HNLF output, we obtain ≈140 comb lines within a 12 nm bandwidth having 10.8 GHz line spacing. We demonstrate wavelength tunability over a span of 35 nm by using a tunable laser source as the Brillouin pump. We also observe and measure a secondary OFC generated during the power-equalization process by placing a 10% coupler inside the BAPE cavity. Our experimental results closely match the trends obtained in the simulation.

All-SBS fiber-based setup for optical frequency comb generation utilizing a pump recycling technique and comb line isolation by implementing Brillouin amplification.

We describe an all-stimulated Brillouin scattering (SBS) fiber-based setup for the generation, amplification, and isolation of frequency components from an optical frequency comb (OFC). The cascaded SBS-OFC is obtained by utilizing pump and Stokes power recycling techniques. A total of >15 comb lines within a 45 dB bandwidth, having an average power of 16 dBm, is observed in the 1550 nm wavelength region of operation. By implementing a polarizer-analyzer setup exploiting the weak birefringence in silica fibers, we amplified and isolated the first Stokes component of the generated comb. The isolated component at 1550.03 nm was amplified by ≈55dB. In order to verify the isolation of a single comb line, the SBS-OFC is intensity modulated using sinusoidal signals of different frequencies, and the modulation is detected after the comb line isolation. We also observe that with the increase in Brillouin pump power during comb line isolation, the spontaneous Brillouin noise acts as a limitation to the selective amplification process.

Reduced sampling rate Kalman filters for carrier phase and frequency offset tracking in 200 Gbps 16 QAM coherent communication system.

We propose 1 state and 2 state multi-step Kalman filters (MKFs) to estimate and compensate CFO, LPN and NLPN in long-haul coherent fiber-optic communication systems. The proposed filters generate state estimates once every m symbols and therefore operate at a reduced sampling rate compared to conventional KFs that perform symbol by symbol processing. No computations are performed to obtain phase estimates of the intermediate [Formula: see text] samples; instead, the present and previous estimates are averaged and used to derotate the intermediate [Formula: see text] samples which are then demodulated to recover the transmitted symbols. This reduces the computational load on the receiver DSP. Further, in order to improve estimation accuracy, we adaptively vary the process noise covariance Q. Simulation results of 200 Gbps PDM 16 QAM system over 12 spans shows that the proposed 1 state MKF can reduce the sampling rate requirement by a factor of [Formula: see text] with Q-factor degradation of 1.32 dB compared to single-step KF at linewidth of 100 kHz. The 2 state MKF tracks PN and CFO with a maximum step size of [Formula: see text] for a CFO of 100 MHz at linewidth of 100 kHz. We also study the dynamic performance of the proposed algorithms by applying step change to CFO. The 2 state MKF with adaptive Q is able to track a step change of 400 MHz of CFO with [Formula: see text] and 3 with high estimation accuracy but slower convergence time compared to the non-adaptive 2 state MKF. Finally, we study the computational requirements of the proposed MKFs and show that they offer significant reduction in computations compared to single-step KF thus making the proposed filters suitable for hardware implementation.

16 Gbps random bit generation using chaos in near-symmetric erbium-doped fiber ring laser.

We demonstrate a bias-free random bit generator at 16 Gbps using chaos in a near-symmetric erbium-doped fiber (EDF) ring laser. The laser consists of two EDFs, each pumped at 980 nm, two intracavity filters of central wavelength 1549.30 nm, and two 90:10 output couplers. The presence of chaos at the laser output is demonstrated by computing the largest Lyapunov exponent for different embedding dimensions. The laser outputs are photodetected and subtracted to generate an electrical difference signal, which is then sampled at 2 GSa/s and postprocessed to extract random bits at 16 Gbps. The random bits exhibit very low autocorrelation (∼10-4) and have successfully passed all National Institute of Standards and Technology statistical tests and Diehard battery of tests.

Experimental demonstration of 12.5 GHz wideband chaos in symmetric dual-port EDFRL.

We study the dynamics of chaos in a dual-port erbium-doped fiber ring laser (EDFRL). The laser consists of two erbium-doped fibers, intracavity filters at 1549.32 nm, isolators, and couplers. At both ports, the laser transitions into the chaotic regime for pump currents greater than 100 mA via the period doubling route. We calculate the largest Lyapunov exponent using Rosenstein's algorithm. We obtain positive values for the largest Lyapunov exponent (≈0.2) for embedding dimensions 5, 7, 9, and 11 indicating chaos. We compute the power spectral density of the photocurrents at the output ports of the laser. We observe a bandwidth of 12.5 GHz at both ports. This ultra-wideband nature of chaos obtained has potential applications in high-speed random number generation and communication.

Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model.

Although the importance of long-range connections for cortical information processing has been acknowledged for a long time, most studies focused on the long-range interactions between excitatory cortical neurons. Inhibitory interneurons play an important role in cortical computation and have thus far been studied mainly with respect to their local synaptic interactions within the cortical microcircuitry. A recent study showed that long-range excitatory connections onto Martinotti cells (MC) mediate surround suppression. Here we have extended our previously reported attractor network of pyramidal cells (PC) and MC by introducing long-range connections targeting MC. We have demonstrated how the network with Martinotti cell-mediated long-range inhibition gives rise to surround suppression and also promotes saliency of locations at which simple non-uniformities in the stimulus field are introduced. Furthermore, our analysis suggests that the presynaptic dynamics of MC is only ancillary to its orientation tuning property in enabling the network with saliency detection. Lastly, we have also implemented a disinhibitory pathway mediated by another interneuron type (VIP interneurons), which inhibits MC and abolishes surround suppression.

A cortical attractor network with Martinotti cells driven by facilitating synapses.

The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems.

In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.