Experimental reservoir computing with diffractively coupled VCSELs.

We present experiments on reservoir computing (RC) using a network of vertical-cavity surface-emitting lasers (VCSELs) that we diffractively couple via an external cavity. Our optical reservoir computer consists of 24 physical VCSEL nodes. We evaluate the system's memory and solve the 2-bit XOR task and the 3-bit header recognition (HR) task with bit error ratios (BERs) below 1% and the 2-bit digital-to-analog conversion (DAC) task with a root mean square error (RMSE) of 0.

Linear optical random projections without holography.

We introduce what we believe to be a novel method to perform linear optical random projections without the need for holography. Our method consists of a computationally trivial combination of multiple intensity measurements to mitigate the information loss usually associated with the absolute-square non-linearity imposed by optical intensity measurements. Both experimental and numerical findings demonstrate that the resulting matrix consists of real-valued, independent, and identically distributed (i.

(3+1)D printed adiabatic 1-to-M broadband couplers and fractal splitter networks.

We experimentally demonstrate, based on a generic concept for creating 1-to-M couplers, single-mode 3D optical splitters leveraging adiabatic power transfer towards up to 4 output ports. We use the CMOS compatible additive (3+1)D flash-two-photon polymerization (TPP) printing for fast and scalable fabrication. Optical coupling losses of our splitters are reduced below our measurement sensitivity of 0.

Additive 3D photonic integration that is CMOS compatible.

Today, continued miniaturization in electronic integrated circuits (ICs) appears to have reached its fundamental limit at ∼2 nm feature-sizes, from originally ∼1 cm. At the same time, energy consumption due to communication becomes the dominant limitation in high performance electronic ICs for computing, and modern computing concepts such neural networks further amplify the challenge. Communication based on co-integrated photonic circuits is a promising strategy to address the second.

Roadmap on Deep Learning for Microscopy.

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities.

Injection locking and coupling the emitters of large VCSEL arrays via diffraction in an external cavity.

Networks of semiconductor lasers are the foundation of numerous applications and fundamental investigations in nonlinear dynamics, material processing, lighting, and information processing. However, making the usually narrowband semiconductor lasers within the network interact requires both high spectral homogeneity and a fitting coupling concept. Here, we report how we use diffractive optics in an external cavity to experimentally couple vertical-cavity surface-emitting lasers (VCSELs) in a 5×5 array.

Combining one and two photon polymerization for accelerated high performance (3 + 1)D photonic integration.

Dense and efficient circuits with component sizes approaching the physical limit is the hallmark of high performance integration. Ultimately, these features and their pursuit enabled the multi-decade lasting exponential increase of components on integrated electronic chips according to Moore's law, which culminated with the high performance electronics we know today. However, current fabrication technology is mostly constrained to 2D lithography, and thermal energy dissipation induced by switching electronic signal lines presents a fundamental challenge for truly 3D electronic integration.

Two-color optically addressed spatial light modulator as a generic spatiotemporal system.

Nonlinear spatiotemporal systems are the basis for countless physical phenomena in such diverse fields as ecology, optics, electronics, and neuroscience. The canonical approach to unify models originating from different fields is the normal form description, which determines the generic dynamical aspects and different bifurcation scenarios. Realizing different types of dynamical systems via one experimental platform that enables continuous transition between normal forms through tuning accessible system parameters is, therefore, highly relevant.

Understanding and mitigating noise in trained deep neural networks.

Deep neural networks unlocked a vast range of new applications by solving tasks of which many were previously deemed as reserved to higher human intelligence. One of the developments enabling this success was a boost in computing power provided by special purpose hardware, such as graphic or tensor processing units. However, these do not leverage fundamental features of neural networks like parallelism and analog state variables.

Analysis of a Tubular Torsionally Resonating Viscosity-Density Sensor.

This paper discusses a state-of-the-art inline tubular sensor that can measure the viscosity-density of a passing fluid. In this study, experiments and numerical modelling were performed to develop a deeper understanding of the tubular sensor. Experimental results were compared with an analytical model of the torsional resonator.

Advancing Fourier: space-time concepts in ultrafast optics, imaging, and photonic neural networks.

The concepts of Fourier optics were established in France in the 1940s by Pierre-Michel Duffieux, and laid the foundations of an extensive series of activities in the French research community that have touched on nearly every aspect of contemporary optics and photonics. In this paper, we review a selection of results where applications of the Fourier transform and transfer functions in optics have been applied to yield significant advances in unexpected areas of optics, including the spatial shaping of complex laser beams in amplitude and in phase, real-time ultrafast measurements, novel ghost imaging techniques, and the development of parallel processing methodologies for photonic artificial intelligence.

Coupled Nonlinear Delay Systems as Deep Convolutional Neural Networks.

Neural networks are transforming the field of computer algorithms, yet their emulation on current computing substrates is highly inefficient. Reservoir computing was successfully implemented on a large variety of substrates and gave new insight in overcoming this implementation bottleneck. Despite its success, the approach lags behind the state of the art in deep learning.

Conditions for reservoir computing performance using semiconductor lasers with delayed optical feedback.

Photonic implementations of reservoir computing (RC) have been receiving considerable attention due to their excellent performance, hardware, and energy efficiency as well as their speed. Here, we study a particularly attractive all-optical system using optical information injection into a semiconductor laser with delayed feedback. We connect its injection locking, consistency, and memory properties to the RC performance in a non-linear prediction task.

Dynamical complexity and computation in recurrent neural networks beyond their fixed point.

Spontaneous activity found in neural networks usually results in a reduction of computational performance. As a consequence, artificial neural networks are often operated at the edge of chaos, where the network is stable yet highly susceptible to input information. Surprisingly, regular spontaneous dynamics in Neural Networks beyond their resting state possess a high degree of spatio-temporal synchronization, a situation that can also be found in biological neural networks.

Interaction between Liénard and Ikeda dynamics in a nonlinear electro-optical oscillator with delayed bandpass feedback.

We report on experimental and theoretical analysis of the complex dynamics generated by a nonlinear time-delayed electro-optic bandpass oscillator. We investigate the interaction between the slow- and fast-scale dynamics of autonomous oscillations in the breather regime. We analyze in detail the coupling between the fast-scale behavior associated to a characteristic low-pass Ikeda behavior and the slow-scale dynamics associated to a Liénard limit-cycle.

Bidirectional private key exchange using delay-coupled semiconductor lasers.

We experimentally demonstrate a key exchange cryptosystem based on the phenomenon of identical chaos synchronization. In our protocol, the private key is symmetrically generated by the two communicating partners. It is built up from the synchronized bits occurring between two current-modulated bidirectionally coupled semiconductor lasers with additional self-feedback.

Detecting Disordered Breathing and Limb Movement Using In-Bed Force Sensors.

We present and evaluate measurement fusion and decision fusion for recognizing apnea and periodic limb movement in sleep episodes. We used an in-bed sensor system composed of an array of strain gauges to detect pressure changes corresponding to respiration and body movement. The sensor system was placed under the bed mattress during sleep and continuously recorded pressure changes.

Non-24-Hour Sleep-Wake Disorder Revisited - A Case Study.

The human sleep-wake cycle is governed by two major factors: a homeostatic hourglass process (process S), which rises linearly during the day, and a circadian process C, which determines the timing of sleep in a ~24-h rhythm in accordance to the external light-dark (LD) cycle. While both individual processes are fairly well characterized, the exact nature of their interaction remains unclear. The circadian rhythm is generated by the suprachiasmatic nucleus ("master clock") of the anterior hypothalamus, through cell-autonomous feedback loops of DNA transcription and translation.

Reconfigurable semiconductor laser networks based on diffractive coupling.

Networks of optical emitters are highly sought-after, both for fundamental investigations as well as for various technological applications. We introduce and implement a novel scheme, based on diffractive optical coupling, allowing for the coupling of large numbers of optical emitters with adjustable weights. We demonstrate its potential by coupling emitters of a 2D array of semiconductor lasers with significant efficiency.

Minimal approach to neuro-inspired information processing.

To learn and mimic how the brain processes information has been a major research challenge for decades. Despite the efforts, little is known on how we encode, maintain and retrieve information. One of the hypothesis assumes that transient states are generated in our intricate network of neurons when the brain is stimulated by a sensory input.

Autocorrelation properties of chaotic delay dynamical systems: A study on semiconductor lasers.

We present a detailed experimental characterization of the autocorrelation properties of a delayed feedback semiconductor laser for different dynamical regimes. We show that in many cases the autocorrelation function of laser intensity dynamics can be approximated by the analytically derived autocorrelation function obtained from a linear stochastic model with delay. We extract a set of dynamic parameters from the fit with the analytic solutions and discuss the limits of validity of our approximation.

High resolution coherent population trapping on a single hole spin in a semiconductor quantum dot.

We report high resolution coherent population trapping on a single hole spin in a semiconductor quantum dot. The absorption dip signifying the formation of a dark state exhibits an atomic physicslike dip width of just 10 MHz. We observe fluctuations in the absolute frequency of the absorption dip, evidence of very slow spin dephasing.

Self-reported sleep disturbances in renal transplant recipients.

Background: Poor sleep quality (SQ) and daytime sleepiness (DS) are common in renal transplant (RTx) recipients; however, related data are rare. This study describes the prevalence and frequency of self-reported sleep disturbances in RTx recipients.

Methods: This cross-sectional study included 249 RTx recipients transplanted at three Swiss transplant centers.