Deep Photonic Reservoir Computer for Speech Recognition.

Speech recognition is a critical task in the field of artificial intelligence (AI) and has witnessed remarkable advancements thanks to large and complex neural networks, whose training process typically requires massive amounts of labeled data and computationally intensive operations. An alternative paradigm, reservoir computing (RC), is energy efficient and is well adapted to implementation in physical substrates, but exhibits limitations in performance when compared with more resource-intensive machine learning algorithms. In this work, we address this challenge by investigating different architectures of interconnected reservoirs, all falling under the umbrella of deep RC (DRC). We propose a photonic-based deep reservoir computer and evaluate its effectiveness on different speech recognition tasks. We show specific design choices that aim to simplify the practical implementation of a reservoir computer while simultaneously achieving high-speed processing of high-dimensional audio signals. Overall, with the present work, we hope to help the advancement of low-power and high-performance neuromorphic hardware.

Integrated programmable spectral filter for frequency-multiplexed neuromorphic computers.

Artificial neural networks (ANN) are a groundbreaking technology massively employed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementations are very interesting mainly because of low power consumption and high bandwidth. We recently demonstrated a photonic neuromorphic computing system based on frequency multiplexing that executes ANNs algorithms as reservoir computing and Extreme Learning Machines. Neuron signals are encoded in the amplitude of the lines of a frequency comb, and neuron interconnections are realized through frequency-domain interference. Here we present an integrated programmable spectral filter designed to manipulate the optical frequency comb in our frequency multiplexing neuromorphic computing platform. The programmable filter controls the attenuation of 16 independent wavelength channels with a 20 GHz spacing. We discuss the design and the results of the chip characterization, and we preliminary demonstrate, through a numerical simulation, that the produced chip is suitable for the envisioned neuromorphic computing application.

Photonic reservoir computer based on frequency multiplexing.

Reservoir computing is a brain-inspired approach for information processing, well suited to analog implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The system processes 25 comb lines simultaneously (i.e., 25 neurons), at a rate of 20 MHz. We illustrate performances on two standard benchmark tasks: channel equalization and time series forecasting. We also demonstrate that frequency multiplexing allows output weights to be implemented in the optical domain, through optical attenuation. We discuss the perspectives for high-speed, high-performance, low-footprint implementations.

Photonic extreme learning machine based on frequency multiplexing.

The optical domain is a promising field for the physical implementation of neural networks, due to the speed and parallelism of optics. Extreme learning machines (ELMs) are feed-forward neural networks in which only output weights are trained, while internal connections are randomly selected and left untrained. Here we report on a photonic ELM based on a frequency-multiplexed fiber setup. Multiplication by output weights can be performed either offline on a computer or optically by a programmable spectral filter. We present both numerical simulations and experimental results on classification tasks and a nonlinear channel equalization task.

Scanless optical feedback imaging principle by single-pixel compressed sensing.

Optical feedback in lasers is being used for unconventional imaging of fluid dynamics, pressure fields, material properties, and free-carrier distribution, especially in spectral regions where two-dimensional detectors are not yet available. As this technique requires scanning the laser spot across the target, the resulting image contrast is often hampered by the speckle effect. Compressed sensing is becoming a workhorse technique for signal analysis, allowing the reconstruction of complex images from a relatively small number of integrated (single-pixel) measurements, and is being efficiently adapted to a number of single-pixel detector cameras. We applied compressed sensing algorithms to the inherently single-pixel optical feedback in a semiconductor diode laser, demonstrating for the first time, to the best of our knowledge, scanless and detectorless speckle-free imaging of a simple binary object.

Correlation plenoptic imaging between arbitrary planes.

We propose a novel method to perform plenoptic imaging at the diffraction limit by measuring second-order correlations of light between two reference planes, arbitrarily chosen, within the tridimensional scene of interest. We show that for both chaotic light and entangled-photon illumination, the protocol enables to change the focused planes, in post-processing, and to achieve an unprecedented combination of image resolution and depth of field. In particular, the depth of field results larger by a factor 3 with respect to previous correlation plenoptic imaging protocols, and by an order of magnitude with respect to standard imaging, while the resolution is kept at the diffraction limit. The results lead the way towards the development of compact designs for correlation plenoptic imaging devices based on chaotic light, as well as high-SNR plenoptic imaging devices based on entangled photon illumination, thus contributing to make correlation plenoptic imaging effectively competitive with commercial plenoptic devices.

Upper limb motor improvement in chronic stroke after combining botulinum toxin A injection and multi-joints robot-assisted therapy: a case report.

Spasticity is one of the major complications after stroke. Botulinum toxin type A (BoNT-A) injection is commonly used to manage focal spasticity. However, it is uncertain whether BoNT-A can improve activities of daily living function of paretic arm. The recovery of functions of the affected arm is also the aim of robotic upper limb (UL) therapy. The motorized exoskeleton assists the patient in a large 3D work environment by promoting movement for the UL (shoulder, elbow, wrist, hand). The combination of the BoNT-A injection and the robotic therapy might enhance functional recovery after stroke. We reported the case of a chronic stroke patient in which the injection of BoNT-A was combined with multi-joint exoskeleton training. The patient showed improvement in the motor control of the UL, supporting the feasibility of this approach.